Towardsdatascience
Jul 20, 16:30
How to Run Claude Code Agents for 24+ Hours
Apply long-running coding agents to become a more productive engineer The post How to Run Claude Code Agents for 24+ Hours appeared first on Towards Data Science .
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Apply long-running coding agents to become a more productive engineer One of the keys to being able to run a lot of coding agents in parallel is to ensure you have long-running sessions. In this article, I’ll discuss: First of all, I’d like to cover why you should follow the tips within this article.
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Apply long-running coding agents to become a more productive engineer The post How to Run Claude Code Agents for 24+ Hours appeared first on Towards Data Science .
Towardsdatascience
Jul 20, 15:00
Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
Enterprise Document Intelligence [Vol.1 #10B] - The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model The post Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #10B] – The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model Some bad parses do not look bad until the answer comes out (classic OCR is the textbook case: EasyOCR recovers the words and quietly drops the table structure around them, and the answer reads fine until…
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Enterprise Document Intelligence [Vol.1 #10B] - The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model The post Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM appeared first on Towards Data Science .
Kdnuggets
Jul 20, 14:00
A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming
This article walks through the actual configuration, permissions, hooks, and command habits that separate a fresh install from a setup that holds up under real, sustained agentic work.
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None of that is a limitation of the model. Claude Code ships with sensible defaults, but sensible defaults and high performance are different bars, and the gap between them is almost entirely made up of a handful of files most beginners never open.
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This article walks through the actual configuration, permissions, hooks, and command habits that separate a fresh install from a setup that holds up under real, sustained agentic work.
Towardsdatascience
Jul 20, 13:30
Water Cooler Small Talk, Ep. 12: Byzantine Fault Tolerance
How do you make decisions when you can't trust anyone in the room? The post Water Cooler Small Talk, Ep. 12: Byzantine Fault Tolerance appeared first on Towards Data Science .
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Water Cooler Small Talk, Ep. The post Water Cooler Small Talk, Ep. 12: Byzantine Fault Tolerance appeared first on Towards Data Science .
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12: Byzantine Fault Tolerance appeared first on Towards Data Science .
Edri
Jul 20, 12:14
The EU is about to sell our most sensitive data to the US for visa-free travel
Search How would you best describe that thing you're looking for? In 2022, the US government announced that it would require access to biometric databases of countries if they wish to keep visa exemp…
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The EU is about to sell our most sensitive data to the US for visa-free travel. In 2022, the US government announced that it would require access to biometric databases of countries if they wish to keep visa exemption for their citizens travelling to the US , including European Union (EU) Member States.
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The goal of these negotiations is to establish a “Framework Agreement” which would set out the modalities of the information exchange and general rules on processing of personal data between the US and Member States.
Towardsdatascience
Jul 20, 12:00
Automatically Assign a Category to Uncategorized Rows in Power Query and DAX
Having categorized data is everything in reporting. Uncategorized data cannot be grouped and aggregated. But sometimes we must assign a category to uncategorized data according to certain rules. Let’s see how I solved this in a facility management project. The post Automatically Assign a Category to Uncategorized Rows in Power Query and DAX appeared first on Towards Data Science .
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Having categorized data is everything in reporting. Uncategorized data cannot be grouped and aggregated. But sometimes we must assign a category to uncategorized data according to certain rules.
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The post Automatically Assign a Category to Uncategorized Rows in Power Query and DAX appeared first on Towards Data Science .
Machinelearningmastery
Jul 20, 11:27
Building Agentic Workflows in Python with LangGraph
In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using...
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Share Post Share In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using agent with persistent conversation memory. Most AI agent setups handle the single-turn case well: take a question, call a model, and return an answer.
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In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using...
Fortune
Jul 20, 04:19
Power companies are using eminent domain to seize land for data centers
While President Donald Trump has promoted AI advancement , calling it crucial to economic and national security, polling shows that 7 in 10 Americans oppose the construction of AI data centers in the…
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Power companies are using eminent domain to seize land for data centers. Data centers have massive power needs that can stress electrical grids and threaten their reliability . To meet this demand, power companies must build more transmission lines – and acquire land to put them on.
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But does a line built to serve a private data center qualify?
Privacy
Jul 20, 02:19
Delete Your Data with Drop
The Delete Request and Opt-out Platform (DROP) is now available. Use DROP to require data brokers delete your information with a single request.
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Delete Your Data with Drop. The Delete Request and Opt-out Platform (DROP) is now available. Use DROP to require data brokers delete your information with a single request.
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Use DROP to require data brokers delete your information with a single request.
Towardsdatascience
Jul 19, 17:00
Backpropagation Explained for Beginners (Part 1): Building the Intuition
Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .
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If you’re trying to understand how modern AI systems like large language models (LLMs) are trained, backpropagation is one of the most important concepts to understand. I realized and wanted to start from scratch and build my understanding one step at a time.
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Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .
Towardsdatascience
Jul 19, 15:00
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Enterprise Document Intelligence [Vol.1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering. On the question side, the loop is small by design: read the doc, ask what is missing, re-parse. The post Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering. On the question side, the loop is small by design: read the doc, ask what is missing, re-parse. The post Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval appeared first on Towards Data Science .
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The post Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval appeared first on Towards Data Science .
Towardsdatascience
Jul 19, 13:00
Your AI Agent Passed Every Eval. Finance Still Killed It.
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced. The one metric that predicts whether an agent survives production, and how to measure it without a rebuild. The post Your AI Agent Passed Every Eval. Finance Still Killed It. appeared first on Towards Data Science .
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Your AI Agent Passed Every Eval. The post Your AI Agent Passed Every Eval. appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Kdnuggets
Jul 19, 09:50
Could Your AI Systems Already Be High-Risk Under the EU AI Act?
Access the on-demand webinar to understand what the latest guidance means for your AI governance program and what your organization should do next.
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The European Commission’s latest draft guidelines provide much-needed clarity on how organizations should classify high-risk AI systems under Article 6 of the EU AI Act. Article 6 outlines two routes through which an AI system may be classified as high-risk.
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Access the on-demand webinar to understand what the latest guidance means for your AI governance program and what your organization should do next.
Towardsdatascience
Jul 18, 17:00
Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform. appeared first on Towards Data Science .
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Many Companies Use AI. Few Know How to Build an AI-Native Enterprise Data Platform.. A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance.
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appeared first on Towards Data Science .
Towardsdatascience
Jul 18, 15:00
Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It
Enterprise Document Intelligence [Vol.1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science .
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Run the cheap one everywhere and the single flattened table sinks the answer. Neither setting is right for the whole document, because the document is not uniform: most pages are plain text a fast parser handles fine, and a few carry the tables that need the expensive one.
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Enterprise Document Intelligence [Vol.1 #10A] - The escalation cascade and the free, deterministic checks that flag a failed parse before you pay for a deeper one The post Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It appeared first on Towards Data Science .
Kdnuggets
Jul 18, 13:00
KDnuggets Weekly Roundup: Week of July 13, 2026
Stop Using If-Else Chains: Use the Registry Pattern in Python Instead • 5 Real-World SQL Projects to Build Your Data Portfolio • 10 YouTube Channels Keeping You Ahead in AI • Structured Language Model Generation with Outlines
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Stop Using If-Else Chains: Use the Registry Pattern in Python Instead • 5 Real-World SQL Projects to Build Your Data Portfolio • 10 YouTube Channels Keeping You Ahead in AI • Structured Language Model Generation with Outlines 🐍 Stop Using If-Else Chains: Use the Registry Pattern in Python Instead Kanwal Mehreen · Python · July 15, 2026 Long conditional chains hinder exten…
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Stop Using If-Else Chains: Use the Registry Pattern in Python Instead • 5 Real-World SQL Projects to Build Your Data Portfolio • 10 YouTube Channels Keeping You Ahead in AI • Structured Language Model Generation with Outlines
Towardsdatascience
Jul 18, 13:00
How to Improve Customer Retention in FinTech
A practical guide to combining pre-churn scoring with uplift modelling for smarter retention. The post How to Improve Customer Retention in FinTech appeared first on Towards Data Science .
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How to Improve Customer Retention in FinTech. A practical guide to combining pre-churn scoring with uplift modelling for smarter retention. The post How to Improve Customer Retention in FinTech appeared first on Towards Data Science .
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The post How to Improve Customer Retention in FinTech appeared first on Towards Data Science .
Jvns
Jul 17, 17:45
Learning a few things about running SQLite
Hello! I’ve been working on a Django site recently, and I decided to use SQLite as the database. When I was getting started with using SQLite as database for a website I read a bunch of blog posts ab…
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Learning a few things about running SQLite. So here are a couple of small things I’ve been learning about running SQLite. Today I was running a query (using SQLite’s FTS5 for full-text search) on a table with 4000 rows and it took 5 seconds.
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I’ve been working on a Django site recently, and I decided to use SQLite as the database.
Towardsdatascience
Jul 17, 16:30
How to Work Effectively with GPT-5.6
In this article , I will give my first impressions of the newest OpenAI model, GPT-5.6. The model was released a few days ago, and I’ve gotten the chance to test it extensively since the release and…
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In this article , I will give my first impressions of the newest OpenAI model, GPT-5.6. First of all, I’d like to cover why you should care about this article, and in this case, it’s why you should care about GPT-5.6 and how to use it effectively. First of all, the reason you should care about GPT-5.6 is that the previous generation of the same model, GPT-5.
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Maximize the latest OpenAI model The post How to Work Effectively with GPT-5.6 appeared first on Towards Data Science .
Towardsdatascience
Jul 17, 15:00
Using Classical ML to Empower AI Agents
On the value of building on existing foundations The post Using Classical ML to Empower AI Agents appeared first on Towards Data Science .
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These tools can take all kinds of forms- a lot of them today in the business setting are data retrieval and organizing tools, graph databases, RAG knowledge bases, query construction and validation, and so on. However, I want to remind you that classical ML models can also be really valuable tools for your agent.
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On the value of building on existing foundations The post Using Classical ML to Empower AI Agents appeared first on Towards Data Science .
Kdnuggets
Jul 17, 14:00
Git Worktrees for AI Development
A Git worktree is a separate directory checked out from the same repository. You can have as many as you need, each on its own branch, all coexisting simultaneously on your filesystem.
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Git Worktrees for AI Development. A Git worktree is a separate directory checked out from the same repository. You can have as many as you need, each on its own branch, all coexisting simultaneously on your filesystem.
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A Git worktree is a separate directory checked out from the same repository.
Towardsdatascience
Jul 17, 13:30
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
Everyone is talking about loop engineering, but most discussions assume an LLM sits at the center of the loop. I wanted to isolate the architecture itself. So I built a deterministic, zero-dependency Python benchmark that replaces the model with simple rules, allowing me to measure one question directly: can a goal-directed controller isolate failures better than a traditional linear pipeline? After validating the benchmark across 300 random seeds—and fixing a subtle bug that initially invalidated my own results—I found that the controller consistently completed independent branches that a linear executor never reached. This article walks through the architecture, the benchmark design, the debugging process, and the evidence behind a narrow but practical claim: failure isolation is a measurable property of control flow, independent of LLM reasoning. The post Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop appeared first on Towards Data Science .
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Everyone is talking about loop engineering, but most discussions assume an LLM sits at the center of the loop. So I built a deterministic, zero-dependency Python benchmark that replaces the model with simple rules, allowing me to measure one question directly: can a goal-directed controller isolate failures better than a traditional linear pipeline?
TL;DR:
The post Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop appeared first on Towards Data Science .
Machinelearningmastery
Jul 17, 12:00
Agentic AI Security: Defending Against Prompt Injection and Tool Misuse
In this article, you will learn what prompt injection and tool misuse are in the context of agentic AI systems, and which defense strategies experts...
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Share Post Share In this article, you will learn what prompt injection and tool misuse are in the context of agentic AI systems, and which defense strategies experts recommend to mitigate them. There is an ongoing rapid transition of AI agents from experimental settings into real-world production environments.
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In this article, you will learn what prompt injection and tool misuse are in the context of agentic AI systems, and which defense strategies experts...
Kdnuggets
Jul 17, 12:00
5 FREE Resources on Agentic AI
Here are 5 curated resources to help you progress your agentic AI learning for FREE. Everyone is building agents.
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Everyone is building agents. The gap between shipping an agent and understanding one is where these five resources live, and every one of them is completely free. AI Agents for Beginners is a full course on GitHub under an MIT license, running to more than fifteen lessons with video walkthroughs and runnable Python for each one.
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Here are 5 curated resources to help you progress your agentic AI learning for FREE.
Towardsdatascience
Jul 17, 12:00
Analog AI Is Back, But Can It Survive Its Own Noise?
AI's energy crisis is reviving an old idea: computing with physics instead of digital logic. Here's how analog chips actually work, why noise nearly killed the idea once already, and what happens when you simulate that noise yourself. The post Analog AI Is Back, But Can It Survive Its Own Noise? appeared first on Towards Data Science .
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Analog AI Is Back, But Can It Survive Its Own Noise?. The post Analog AI Is Back, But Can It Survive Its Own Noise? appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
Jul 17, 10:30
One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited
Enterprise Document Intelligence [Vol.1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #9B] – One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC In the previous article ( A production RAG pipeline for PDFs: relational parsing, TOC retrieval, typed answers ) we upgraded each of the four bricks: document parsing, question parsing, retrieval, and generation, and w…
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Enterprise Document Intelligence [Vol.1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited appeared first on Towards Data Science .
Github
Jul 17, 00:07
Lingbot-map: A 3D foundation model for reconstructing scenes from streaming data
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window.
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Reload to refresh your session. Reload to refresh your session. Reload to refresh your session.
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You signed in with another tab or window.
Techwerkers
Jul 16, 16:49
'We used acid to sabotage Microsoft hyperscale data centre construction'
In the night of 16 july, people from Extinction Rebellion (XR) targetted a Microsoft data centre construction site in the Amsterdam port area.
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'We used acid to sabotage Microsoft hyperscale data centre construction'. A single Microsoft data centre consumes 1% of all available electricity in The Netherlands In the Netherlands there is a growing awareness of the ecological and social damage done by the massive data centers built by hyperscaler big tech.
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In the night of 16 july, people from Extinction Rebellion (XR) targetted a Microsoft data centre construction site in the Amsterdam port area.
Towardsdatascience
Jul 16, 16:30
Prepare These 5 Assets Before Your AI Agents Take On More Work
How to define recurring work, give AI the right context, explain what high-quality work looks like, and decide where human judgment is still needed. The post Prepare These 5 Assets Before Your AI Agents Take On More Work appeared first on Towards Data Science .
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Prepare These 5 Assets Before Your AI Agents Take On More Work. How to define recurring work, give AI the right context, explain what high-quality work looks like, and decide where human judgment is still needed. The post Prepare These 5 Assets Before Your AI Agents Take On More Work appeared first on Towards Data Science .
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The post Prepare These 5 Assets Before Your AI Agents Take On More Work appeared first on Towards Data Science .
Towardsdatascience
Jul 16, 15:00
Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
Enterprise Document Intelligence [Vol.1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream call The post Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation appeared first on Towards Data Science .
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The other half is that the question itself is context the LLM will see, and the question deserves the same treatment as the retrieved passage. That is not a retrieval failure and not a generation failure. What follows names those pieces, one strategy at a time, and shows what each one is for on the receiving side.
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Enterprise Document Intelligence [Vol.1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream call The post Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation appeared first on Towards Data Science .
Sinja
Jul 16, 14:59
Guide to data tools landscape for developers
Found yourself on a data project and have no idea what they all are talking about? Feel excluded from all the fun discussions in the office kitchen?
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Guide to data tools landscape for developers. There are so many data tools besides notebooks, and I had no idea what they were used for or what the general work process in data science was. Unfortunately, there wasn't a convenient "guide to data tools for software engineers who found themselves in a data company and have no idea what all those words mean", or I just couldn't f…
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I, however, didn't have any background in data.
Kdnuggets
Jul 16, 14:00
Working with Pi Coding Agents
The most interesting thing about Pi isn't any single feature; it's that the project treats "what we didn't build" as documentation worth writing, which is rare enough on its own to take seriously.
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Most coding agents compete on how much they do for you. Claude Code manages sub-agents, plan mode, and permission flows out of the box. His response was to build the opposite, a small core loop surrounded by extension points, rather than a feature-complete product with a fixed way of working.
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The most interesting thing about Pi isn't any single feature; it's that the project treats "what we didn't build" as documentation worth writing, which is rare enough on its own to take seriously.
Towardsdatascience
Jul 16, 13:30
How to Get the Most Out of Claude Fable 5
Maximize your Claude Fable 5 usage The post How to Get the Most Out of Claude Fable 5 appeared first on Towards Data Science .
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However, it’s now been returned to the Claude subscription, and anyone with the Claude subscription can access Claude Fable 5. These are the techniques that I use on a daily basis to get the most out of my Claude Code subscription. The main reason you should be using Claude Fable 5 is simply that it is the most powerful coding model out there at the moment.
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Maximize your Claude Fable 5 usage The post How to Get the Most Out of Claude Fable 5 appeared first on Towards Data Science .
Machinelearningmastery
Jul 16, 12:25
Run a Local AI Model with Ollama in 15 Minutes
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama....
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Share Post Share In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama. In our Introduction to Small Language Models , we covered how a new generation of efficient AI models is shifting workloads away from massive, expensive cloud APIs.
TL;DR:
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama....
Kdnuggets
Jul 16, 12:11
10 YouTube Channels Keeping You Ahead in AI
Explore 10 YouTube channels for AI engineers covering paper breakdowns, coding tutorials, and industry analysis. The artificial intelligence (AI) ecosystem is moving at a breakneck pace.
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The artificial intelligence (AI) ecosystem is moving at a breakneck pace. For data professionals, staying updated is no longer about reading everything; it's about curating the right information streams.
TL;DR:
Explore 10 YouTube channels for AI engineers covering paper breakdowns, coding tutorials, and industry analysis.
Towardsdatascience
Jul 16, 12:00
Why Your Betas Explode: The Hidden Geometry of Multicollinearity
Why your regression coefficients keep changing, and what geometry has to do with it. The post Why Your Betas Explode: The Hidden Geometry of Multicollinearity appeared first on Towards Data Science .
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Why Your Betas Explode: The Hidden Geometry of Multicollinearity. Why your regression coefficients keep changing, and what geometry has to do with it. The post Why Your Betas Explode: The Hidden Geometry of Multicollinearity appeared first on Towards Data Science .
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The post Why Your Betas Explode: The Hidden Geometry of Multicollinearity appeared first on Towards Data Science .
Towardsdatascience
Jul 15, 16:30
Don’t Let Claude Grade Its Own Homework
Cross-provider PR review with Codex in GitHub Actions, and why a second opinion from a different lab beats any self-review The post Don’t Let Claude Grade Its Own Homework appeared first on Towards Data Science .
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./market.md instead of market-analysis.md , ./monetization.md instead of business-model.md , some files were even made up entirely! A language model simply generates the most plausible continuation, and sometimes the most plausible continuation is fiction delivered with complete confidence. Confident and wrong reads exactly like confident and right.
TL;DR:
Cross-provider PR review with Codex in GitHub Actions, and why a second opinion from a different lab beats any self-review The post Don’t Let Claude Grade Its Own Homework appeared first on Towards Data Science .
Towardsdatascience
Jul 15, 15:00
Building Trustworthy Production RAG Systems Through Continuous Evaluation
A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users The post Building Trustworthy Production RAG Systems Through Continuous Evaluation appeared first on Towards Data Science .
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The retrieval fetches some chunks, passes it to the generative model, and it writes a fluent answer. The steps are written so you can follow them for your own RAG application, not just read them as theory.
TL;DR:
A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users The post Building Trustworthy Production RAG Systems Through Continuous Evaluation appeared first on Towards Data Science .
Kdnuggets
Jul 15, 14:00
Stop Using If-Else Chains: Use the Registry Pattern in Python Instead
Learn a cleaner, more extensible way to dispatch logic in Python.
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Stop Using If-Else Chains: Use the Registry Pattern in Python Instead. Learn a cleaner, more extensible way to dispatch logic in Python.
TL;DR:
Learn a cleaner, more extensible way to dispatch logic in Python.
Towardsdatascience
Jul 15, 13:30
How I Mastered Data Structures and Algorithms for ML (In 6 Weeks)
The strategies, questions, and process I used to ace coding interviews. The post How I Mastered Data Structures and Algorithms for ML (In 6 Weeks) appeared first on Towards Data Science .
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The strategies, questions, and process I used to ace coding interviews. The post How I Mastered Data Structures and Algorithms for ML (In 6 Weeks) appeared first on Towards Data Science .
TL;DR:
The post How I Mastered Data Structures and Algorithms for ML (In 6 Weeks) appeared first on Towards Data Science .
Dev
Jul 15, 13:22
Mysteries of Telegram Data Centers
Telegram claims to have 5 data centers (DCs), referred to as DC1~5 in Telegram’s code and documentation.
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Mysteries of Telegram Data Centers. When DC5 is down, users on DC5 cannot use Telegram, and the topic in Telegram circles often becomes, “Why is DC5 down again?” DC5 users can only wait for their constantly “reconnecting” clients to recover, then join group chats with users from other DCs to criticize DC5.
TL;DR:
Telegram claims to have 5 data centers (DCs), referred to as DC1~5 in Telegram’s code and documentation.
Longnow
Jul 15, 12:13
Richard Feynman and the Connection Machine
Menu Ideas Dr. Richard Feynman during the Special Lecture: the Motion of Planets Around the Sun, 01964 Long-term Thinking Richard Feynman and The Connection Machine For Richard, a crazy idea was an o…
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One day when I was having lunch with Richard Feynman, I mentioned to him that I was planning to start a company to build a parallel computer with a million processors. There he was instrumental in setting up some of the first plug-programmable tabulating machines for physical simulation.
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Richard Feynman during the Special Lecture: the Motion of Planets Around the Sun, 01964 Long-term Thinking Richard Feynman and The Connection Machine For Richard, a crazy idea was an opportunity to either prove it wrong or prove it right.
Kdnuggets
Jul 15, 12:00
7 Python Frameworks for Orchestrating Local AI Agents
This article contains seven Python tools that engineers are actually using in 2026 to build, coordinate, and run agents on local infrastructure.
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An agent that calls a cloud API for every decision is renting its intelligence. An agent built to run locally skips all of that. One command pulls a model, another serves it over a local API, with no Python environment to configure and no CUDA drivers to install by hand.
TL;DR:
This article contains seven Python tools that engineers are actually using in 2026 to build, coordinate, and run agents on local infrastructure.
Machinelearningmastery
Jul 15, 12:00
Scikit-Ollama for Scikit-LLM/Ollama Integration
In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API...
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Large language model (LLM) integration into traditional machine learning workflows is not only possible nowadays, but also transforming the way we work with these models, in terms of both cost and security.
TL;DR:
In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API...
Towardsdatascience
Jul 15, 12:00
Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up The post Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up The post Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent appeared first on Towards Data Science .
TL;DR:
Fix retrieval, and the model has nothing left to make up The post Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent appeared first on Towards Data Science .
Fortune
Jul 15, 00:20
Data centers have hiked electricity prices on the public by $23B
For example, a recent report by the organization that monitors the PJM market , an area that encompasses all or part of 14 mid-Atlantic and Midwest states, concluded that expected power demand from d…
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Data centers have hiked electricity prices on the public by $23B. For example, a recent report by the organization that monitors the PJM market , an area that encompasses all or part of 14 mid-Atlantic and Midwest states, concluded that expected power demand from data centers was a primary reason for $23 billion in customer price increases that will last until at least the end…
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First, regulators identify the costs that a utility company incurs to provide service .
Towardsdatascience
Jul 14, 16:30
How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI
The analytics career I signed up for five years ago doesn't exist anymore, and honestly, I am fine with that. The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
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How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI. The analytics career I signed up for five years ago doesn't exist anymore, and honestly, I am fine with that. The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
TL;DR:
The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
Towardsdatascience
Jul 14, 15:00
A Gentle Introduction to Autoencoders & Latent Space
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation while preserving the main context. There are various methods that achieve this […] The post A Gentle Introduction to Autoencoders & Latent Space appeared first on Towards Data Science .
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Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation while preserving the main context.
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There are various methods that achieve this […] The post A Gentle Introduction to Autoencoders & Latent Space appeared first on Towards Data Science .
Kdnuggets
Jul 14, 14:00
Getting Started with Conductor for Gemini CLI
Conductor is a Gemini CLI extension built to fix your context problems. Learn all about it here.
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Getting Started with Conductor for Gemini CLI. Conductor is a Gemini CLI extension built to fix your context problems. Learn all about it here.
TL;DR:
Conductor is a Gemini CLI extension built to fix your context problems.
Blog
Jul 14, 13:52
The Conservationist Who Turned 40 Terabytes of Public Data into a Video Game
There’s a grass fire burning behind Raffael Hickisch when we speak on a video call on Tuesday. He’s not worried—fires like this are part of everyday life in Sub-Saharan Africa, where people burn off…
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The Conservationist Who Turned 40 Terabytes of Public Data into a Video Game. Fire data helps Hickisch track where people are moving and informs policies related to how they can use the land. Historically, Hickisch’s challenge has been cobbling together information from different sources and assembling it in a way that people can use.
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He’d download human settlement data, deforestation data, fire data from NASA, and use a desktop program to visualize how all the pieces worked together.
Towardsdatascience
Jul 14, 13:30
How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
I measured the actual GPU electricity for eight local models on one RTX 3090 — and the cheapest wasn't the smallest, nor the priciest the biggest. The post How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured) appeared first on Towards Data Science .
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How Much Does It Actually Cost to Run a Local LLM? The post How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured) appeared first on Towards Data Science .
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(Euros per Million Tokens, Measured) appeared first on Towards Data Science .
Kdnuggets
Jul 14, 12:00
12 Ways to Reduce LLM Latency and Inference Costs in Production
Scaling LLMs isn’t about adding GPUs. It’s about removing wasted work from every request.
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12 Ways to Reduce LLM Latency and Inference Costs in Production. Scaling LLMs isn’t about adding GPUs. It’s about removing wasted work from every request.
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Scaling LLMs isn’t about adding GPUs.
Machinelearningmastery
Jul 14, 12:00
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...
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Share Post Share In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why the LLM-as-a-judge mechanism they all rely on has measurable biases you need to actively design around. You ship an LLM feature after seeing a couple of outputs and decide it looks good.
TL;DR:
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...
Towardsdatascience
Jul 14, 12:00
Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science .
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Pydantic is a Python library for data validation using type annotations. This means that it lets you define the shape and types of your data as a Python class, and then validates that any data you pass in actually conforms to that description. If it doesn’t, Pydantic raises a clear, descriptive error rather than letting bad data silently propagate through your system.
TL;DR:
How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science .
Neow
Jul 13, 20:01
Samsung will delete your health data if you don't let them use it to train AI
Samsung has started notifying users that they'd have to consent to the use of their private health data to train new AI models or risk losing it forever.
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Samsung will delete your health data if you don't let them use it to train AI. When you try to turn off this option, the app stops you in your tracks with a warning that reads: You will not be able to sync health data with your Samsung account and your health data will be deleted unless retained pursuant to applicable law.
TL;DR:
Samsung has started notifying users that they'd have to consent to the use of their private health data to train new AI models or risk losing it forever.
Towardsdatascience
Jul 13, 16:30
Agentic RAG: Let the Agent Search
A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science .
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A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop For many of us, the first LLM application we build is a RAG app. In this post, we’ll build a mini agentic RAG workflow with the OpenAI Agents SDK. For our case study, we’ll build a policy RAG agent over a company policy document collection.
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A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science .
Towardsdatascience
Jul 13, 15:00
Context Rot: Why Claude Code Sessions Decay, and How to Govern Them
Long sessions rot quietly, well before any token limit is reached. Here’s why, and how to govern your context in Claude Code. The post Context Rot: Why Claude Code Sessions Decay, and How to Govern Them appeared first on Towards Data Science .
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Long sessions rot quietly, well before any token limit is reached. Here’s why, and how to govern your context in Claude Code. The post Context Rot: Why Claude Code Sessions Decay, and How to Govern Them appeared first on Towards Data Science .
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The post Context Rot: Why Claude Code Sessions Decay, and How to Govern Them appeared first on Towards Data Science .
Kdnuggets
Jul 13, 14:00
Structured Language Model Generation with Outlines
Outlines is an open-source library that introduces deterministic certainty into LLMs' output generation process for better, more reliable generation of structured outputs.
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Usually, when asking an LLM — abbreviation for " Large Language Model " — for a neat, structured output like JSON objects, for instance, a mix of careful prompt crafting with a "pinch" of luck is required. Otherwise, it might be tricky to get the model to obtain the perfectly structured output you are expecting.
TL;DR:
Outlines is an open-source library that introduces deterministic certainty into LLMs' output generation process for better, more reliable generation of structured outputs.
Towardsdatascience
Jul 13, 13:30
Building Models in Two Worlds: From Latent Constructs to Behavioral Signals
My PhD models tried to explain why people engage. My industry models predict who will. The statistics barely changed. Everything around them did. The post Building Models in Two Worlds: From Latent Constructs to Behavioral Signals appeared first on Towards Data Science .
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My PhD models tried to explain why people engage. My industry models predict who will. The post Building Models in Two Worlds: From Latent Constructs to Behavioral Signals appeared first on Towards Data Science .
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The post Building Models in Two Worlds: From Latent Constructs to Behavioral Signals appeared first on Towards Data Science .
Machinelearningmastery
Jul 13, 12:00
Building AI Agents? Here Are Some Anti-Patterns to Avoid.
Agent systems change constantly in production.
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Building AI Agents? Here Are Some Anti-Patterns to Avoid.. Agent systems change constantly in production.
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Agent systems change constantly in production.
Kdnuggets
Jul 13, 12:00
5 Real-World SQL Projects to Build Your Data Portfolio
Build a stronger data portfolio with these practical SQL projects covering customer churn, data warehousing, sales analysis, banking segmentation, and healthcare analytics.
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SQL is still one of the most important skills for data analysts, data scientists, business intelligence analysts, and analytics engineers. A strong SQL project should not only include queries — it should also show how you clean data, explore trends, answer business questions, and communicate insights clearly.
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Build a stronger data portfolio with these practical SQL projects covering customer churn, data warehousing, sales analysis, banking segmentation, and healthcare analytics.
Towardsdatascience
Jul 13, 12:00
The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices
A framework for aligning agentic AI with enterprise intent to ensure consistent scenario‑wide autonomous behavior. The post The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices appeared first on Towards Data Science .
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The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices. A framework for aligning agentic AI with enterprise intent to ensure consistent scenario‑wide autonomous behavior. The post The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices appeared first on Towards Data Science .
TL;DR:
The post The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices appeared first on Towards Data Science .
Marginalrevolution
Jul 12, 19:52
A Beautiful Theory Falls to Ugly Data
My latest paper, A Test of the Coase Conjecture Using Prices of Electronic Books , with the excellent Tim Groseclose, has just been published.
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A Beautiful Theory Falls to Ugly Data. The Coase Conjecture is another one of Coase’s little ideas — the original paper is six pages — that has spawned hundreds of follow-up papers and thousands of citations. Consumers see this coming, the monopolist knows the consumers see it coming, and so the monopolist cuts price to MC in period 1.
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But the same logic applies in period 2, and again in period 3, and so on — eventually the price unravels to MC.
Towardsdatascience
Jul 12, 15:00
RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
Two techniques, two different problems, and why the question is not really "which one wins" The post RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each appeared first on Towards Data Science .
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Two techniques, two different problems, and why the question is not really "which one wins" Over the past year or so , I have written quite a lot about RAG, starting with the Hitchhiker’s Guide to RAG with ChatGPT API and LangChain , and then exploring various topics related to RAG and AI, like chunking , hybrid search , reranking , contextual retrieval , and a three-part seri…
TL;DR:
Two techniques, two different problems, and why the question is not really "which one wins" The post RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each appeared first on Towards Data Science .
Towardsdatascience
Jul 12, 13:00
How to Orchestrate 100+ Agents With Claude Code
Run 100+ agents in parallel The post How to Orchestrate 100+ Agents With Claude Code appeared first on Towards Data Science .
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In this article , I’ll discuss how to orchestrate a lot of different agents using Claude Code or any other coding agents When you work with coding agents, you want to run as many agents in parallel as possible.
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Run 100+ agents in parallel The post How to Orchestrate 100+ Agents With Claude Code appeared first on Towards Data Science .
Towardsdatascience
Jul 11, 15:00
Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency while silently degrading output quality. This article introduces a deterministic prompt-pruning layer that reduces token usage without breaking dependencies, backed by real benchmarks and production-tested design. The post Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work appeared first on Towards Data Science .
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LLMs don’t fail because they forget—they fail because they remember too much. This article introduces a deterministic prompt-pruning layer that reduces token usage without breaking dependencies, backed by real benchmarks and production-tested design.
TL;DR:
The post Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work appeared first on Towards Data Science .
Towardsdatascience
Jul 11, 13:00
That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It
The best AI models still hallucinate. These hallucinations are sometimes funny, and sometimes cause actual damage. In this post we will consider recent tales of AI hallucinations, and then look under the hood to understand why they happen. The post That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It appeared first on Towards Data Science .
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The best AI models still hallucinate. In this post we will consider recent tales of AI hallucinations, and then look under the hood to understand why they happen. The post That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It appeared first on Towards Data Science .
TL;DR:
The post That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It appeared first on Towards Data Science .
Machinelearningmastery
Jul 10, 20:26
Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one...
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Share Post Share In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one category of information at a time. Memory is one of the defining capabilities of an AI agent , yet it’s often designed as an afterthought.
TL;DR:
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one...
Towardsdatascience
Jul 10, 17:00
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Building a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra The post I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer appeared first on Towards Data Science .
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I Built My Second ETL Pipeline. Building a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra The post I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer appeared first on Towards Data Science .
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This Time, I Started Thinking Like a Data Engineer appeared first on Towards Data Science .
Towardsdatascience
Jul 10, 15:00
PySpark for Beginners: Building Intermediate-Level Skills
A practical next step into partitions, shuffles, joins, caching, and execution plans. The post PySpark for Beginners: Building Intermediate-Level Skills appeared first on Towards Data Science .
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PySpark for Beginners: Building Intermediate-Level Skills. A practical next step into partitions, shuffles, joins, caching, and execution plans. The post PySpark for Beginners: Building Intermediate-Level Skills appeared first on Towards Data Science .
TL;DR:
The post PySpark for Beginners: Building Intermediate-Level Skills appeared first on Towards Data Science .
Kdnuggets
Jul 10, 14:00
Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills)
You don't need a PhD to understand fine-tuning. This article explains how pretrained models learn new skills through fine-tuning.
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Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills). You don't need a PhD to understand fine-tuning. This article explains how pretrained models learn new skills through fine-tuning.
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This article explains how pretrained models learn new skills through fine-tuning.
Towardsdatascience
Jul 10, 13:30
RAG Was Always a Temporary Workaround. What is Next?
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases. The post RAG Was Always a Temporary Workaround. What is Next? appeared first on Towards Data Science .
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RAG Was Always a Temporary Workaround. The post RAG Was Always a Temporary Workaround. appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Kdnuggets
Jul 10, 12:00
Local Video Summarization Pipeline: Processing Frames with SmolVLM2-2.2B
SmolVLM2-2.2B sits at a genuinely useful point on the capability-size trade-off curve; small enough to run on a single consumer GPU, capable enough to produce video summaries that are actually useful for real workflows.
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The first camp requires a cloud API; your footage is uploaded, processed on someone else's servers, and billed per minute of video. That combination, consumer hardware paired with results that actually hold up, is what this article is built around. The same pipeline handles meeting recordings, lectures, and surveillance footage without changing a line of code.
TL;DR:
SmolVLM2-2.2B sits at a genuinely useful point on the capability-size trade-off curve; small enough to run on a single consumer GPU, capable enough to produce video summaries that are actually useful for real workflows.
Towardsdatascience
Jul 10, 12:00
The Big Con of Agentic AI
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
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The seductive promise of AI has drawn in individuals, organizations, and governments alike. A company replaces human employees with AI agents, shedding the tacit, institutional knowledge needed to evaluate the AI output.
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What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
Towardsdatascience
Jul 9, 16:30
Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy
A measured look at distributed training, from DDP and FSDP to the ZeRO stages in between, and why the wiring between your GPUs matters as much as the strategy you choose The post Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy appeared first on Towards Data Science .
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You load the weights, load the data, and wait for it to finish. Each one trains on a different slice of the data in parallel, and the work finishes faster. The model is split into pieces so that no GPU has to hold the entire model.
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A measured look at distributed training, from DDP and FSDP to the ZeRO stages in between, and why the wiring between your GPUs matters as much as the strategy you choose The post Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy appeared first on Towards Data Science .
Machinelearningmastery
Jul 9, 15:38
LLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls
The default assumption in most LLM developer communities is that you start with raw API calls and graduate to a framework as your project grows.
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Share Post Share In this article, you will learn how LangChain, LlamaIndex, and raw API calls each solve a different layer of the LLM application stack, and how to choose among them based on what your project actually requires. The model is giving good answers. Maybe it is retrieval — the model needs to answer questions about documents it was not trained on.
TL;DR:
The default assumption in most LLM developer communities is that you start with raw API calls and graduate to a framework as your project grows.
Towardsdatascience
Jul 9, 15:00
How to Find the Optimal Coding Agent Interface
Find the optimal way to interact with your coding agents The post How to Find the Optimal Coding Agent Interface appeared first on Towards Data Science .
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I’ve spent a lot of time testing out different platforms to orchestrate coding agents, and in this article, I’ll give my opinion on some different tools and how you can find the tool that works best for you. Thus, I’ll also cover how to find out for yourself what works for you in this article I’m not sponsored by any of the tools I cover in this article.
TL;DR:
Find the optimal way to interact with your coding agents The post How to Find the Optimal Coding Agent Interface appeared first on Towards Data Science .
Kdnuggets
Jul 9, 14:00
Running OpenClaw with Ollama
This article covers the full path from zero to a running private research assistant on Telegram, including configuring the context length correctly, connecting the channel, enabling web search, and deploying it headlessly in Docker.
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You have successfully set up Ollama , pulled a capable model, run a few queries in the terminal, and it worked. It is a personal AI assistant that runs on your hardware and stays running, bridging your local Ollama models to the messaging apps you already use: WhatsApp , Telegram , Slack , Discord , iMessage. As of Ollama 0.17, the entire setup collapses to a single command.
TL;DR:
This article covers the full path from zero to a running private research assistant on Telegram, including configuring the context length correctly, connecting the channel, enabling web search, and deploying it headlessly in Docker.
Towardsdatascience
Jul 9, 13:30
Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents
Enterprise Document Intelligence [Vol.1 #7quater] - A 492-page document has a 358-entry table of contents. You can’t read it all, and top-k over every page mixes the answer with its neighbours. Route through the TOC instead: a bounded loop inside retrieval that saves tokens and lifts precision The post Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents appeared first on Towards Data Science .
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Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents. Enterprise Document Intelligence [Vol.1 #7quater] - A 492-page document has a 358-entry table of contents.
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Enterprise Document Intelligence [Vol.1 #7quater] - A 492-page document has a 358-entry table of contents.
Kdnuggets
Jul 9, 12:00
7 Steps to Automating Descriptive Statistics with Python
Stop writing mean() and std() for every column. Learn how to automate descriptive statistics in Python and generate publication-ready summary tables in just a few steps.
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7 Steps to Automating Descriptive Statistics with Python. Stop writing mean() and std() for every column. Learn how to automate descriptive statistics in Python and generate publication-ready summary tables in just a few steps.
TL;DR:
Learn how to automate descriptive statistics in Python and generate publication-ready summary tables in just a few steps.
Towardsdatascience
Jul 9, 12:00
Where Does an AI’s Personality Actually Come From?
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving. The post Where Does an AI’s Personality Actually Come From? appeared first on Towards Data Science .
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Where Does an AI’s Personality Actually Come From?. The post Where Does an AI’s Personality Actually Come From? appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
Jul 8, 16:30
The Real Challenge Limiting AI Models Today
Hint: it is not GPU speed! The post The Real Challenge Limiting AI Models Today appeared first on Towards Data Science .
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Hint: it is not GPU speed! The post The Real Challenge Limiting AI Models Today appeared first on Towards Data Science .
TL;DR:
The post The Real Challenge Limiting AI Models Today appeared first on Towards Data Science .
Kdnuggets
Jul 8, 15:10
How to Clean Messy CSV Files with Python: A Beginner’s Guide
Learn how to clean CSV files with pandas by handling missing values, duplicate rows, messy text, wrong data types, mixed date formats, invalid emails, and currency values.
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When you are just starting out with data analysis, one of the first things you learn is how to clean a dataset. Because raw data is rarely clean. Before you can understand what the data is telling you, you need to fix these issues.
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Learn how to clean CSV files with pandas by handling missing values, duplicate rows, messy text, wrong data types, mixed date formats, invalid emails, and currency values.
Towardsdatascience
Jul 8, 15:00
Redesign Work Before You Add More AI Agents
Map AI value, design workflows, redefine talent, upgrade the executive team, and measure the business impact. The post Redesign Work Before You Add More AI Agents appeared first on Towards Data Science .
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Redesign Work Before You Add More AI Agents. Map AI value, design workflows, redefine talent, upgrade the executive team, and measure the business impact. The post Redesign Work Before You Add More AI Agents appeared first on Towards Data Science .
TL;DR:
The post Redesign Work Before You Add More AI Agents appeared first on Towards Data Science .
Towardsdatascience
Jul 8, 13:30
Inside the Subspace Where Spurious Correlations Are Born
Why small samples can produce large correlations by chance, and why large does not always mean meaningful The post Inside the Subspace Where Spurious Correlations Are Born appeared first on Towards Data Science .
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This article answers the question: what correlation values should we expect when the variables are independent and the true population correlation is zero? Using the geometry of Pearson’s correlation coefficient, the article visualizes the effects of centering and normalization.
TL;DR:
Why small samples can produce large correlations by chance, and why large does not always mean meaningful The post Inside the Subspace Where Spurious Correlations Are Born appeared first on Towards Data Science .
Bbc
Jul 8, 13:10
Tiny data centre used to heat public swimming pool
Home News US & Canada UK UK Politics England N. Ireland N. Ireland Politics Scotland Scotland Politics Wales Wales Politics Africa Asia China India Australia Europe Latin America Middle East In Pictu…
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Tiny data centre used to heat public swimming pool. Home News US & Canada UK UK Politics England N. Ireland Politics Scotland Scotland Politics Wales Wales Politics Africa Asia China India Australia Europe Latin America Middle East In Pictures BBC InDepth BBC Verify Football 2026 Sport Business World of Business Technology of Business NYSE Opening Bell Technology Artificial In…
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Home News US & Canada UK UK Politics England N.
Towardsdatascience
Jul 8, 12:00
The Threshold Is a Price, Not a Percentage
How to decide when an AI agent should act on its own by using cost asymmetry instead of a fixed confidence cutoff The post The Threshold Is a Price, Not a Percentage appeared first on Towards Data Science .
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Can the agent write the SQL? If the agent acts, you risk the cost of a mistake. If you escalate, you pay for a human’s time, whether the agent was right or not.
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How to decide when an AI agent should act on its own by using cost asymmetry instead of a fixed confidence cutoff The post The Threshold Is a Price, Not a Percentage appeared first on Towards Data Science .
Github
Jul 8, 08:37
Geosql: A Claude/Codex skill for geospatial data
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window.
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Geosql: A Claude/Codex skill for geospatial data. Reload to refresh your session. dekart-xyz / geosql Public Notifications You must be signed in to change notification settings Fork 21 Star 140 main Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 90 Commits 90 Commits .
TL;DR:
You signed in with another tab or window.
Digipres
Jul 8, 03:22
Copy That Floppy – Cambridge guide for preserving data from fragile floppy disks
This may be due to an incorrect BASE_URL configuration. See the MyST Documentation for reference. Made with MyST Imaging Floppy Disks Content License: Creative Commons Attribution Share Alike 4.
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Copy That Floppy – Cambridge guide for preserving data from fragile floppy disks. Imaging floppy disks for long-term preservation This guide is written for practitioners wanting to create disk images of floppy disks with the intention of preserving them for the long-term.
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This guide will focus on 8-inch, 5.25-inch, 3.5-inch and 3-inch floppy disks and will only focus on getting material from these disks and will not cover writing disks.
Towardsdatascience
Jul 8, 01:57
Information Theory and Ensemble Models
How should we ensemble time-series forecasts better? The post Information Theory and Ensemble Models appeared first on Towards Data Science .
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How should we ensemble time-series forecasts better? The post Information Theory and Ensemble Models appeared first on Towards Data Science .
TL;DR:
The post Information Theory and Ensemble Models appeared first on Towards Data Science .
Towardsdatascience
Jul 8, 01:56
Granger Causal Networks and Indirect Feedback
A non-parametric variable selection for Structural VARs The post Granger Causal Networks and Indirect Feedback appeared first on Towards Data Science .
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One of the most utilized econometric workflows in the last decade has been that of using vector autoregressive models. From research done by academicians to economists informing policy implementation have all utilized VAR models in some shape or iteration [think vector error correcting models(VECM) or Structural VARs (SVAR)].
TL;DR:
A non-parametric variable selection for Structural VARs The post Granger Causal Networks and Indirect Feedback appeared first on Towards Data Science .
Towardsdatascience
Jul 8, 01:53
Measuring Structure Stability of Econometric Models
The simplest most important idea for time series forecasting The post Measuring Structure Stability of Econometric Models appeared first on Towards Data Science .
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With the rise of big data, the number of variables available to model a given problem have grown exponentially. Every day we have the least amount of data we’ll ever have the most we’ve ever had. Fortunately, data science came up with the idea of defining model stability.
TL;DR:
The simplest most important idea for time series forecasting The post Measuring Structure Stability of Econometric Models appeared first on Towards Data Science .
Machinelearningmastery
Jul 7, 17:04
Tools vs. Subagents: Building Effective AI Agents Without Over-Engineering
Share Post Share In this article, you will learn how to decide whether a given piece of agent functionality should be built as a tool or as a subagent, and how to avoid overengineering your agent arc…
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Subagents: Building Effective AI Agents Without Over-Engineering. Every AI agent you build reaches the same decision point eventually. This article explains what tools and subagents are, where each fits, and how to make the choice every time.
TL;DR:
Share Post Share In this article, you will learn how to decide whether a given piece of agent functionality should be built as a tool or as a subagent, and how to avoid overengineering your agent architecture in the process.
Towardsdatascience
Jul 7, 16:30
A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers
Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1. One upgraded contract per brick: document parsing, question parsing, retrieval, generation The post A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers appeared first on Towards Data Science .
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A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers. Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1. One upgraded contract per brick: document parsing, question parsing, retrieval, generation The post A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers appeared first on Tow…
TL;DR:
Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1.
Kdnuggets
Jul 7, 16:00
SQL vs Pandas vs AI Agents: Which Solves Analytics Problems Best?
Same three analytics problems, three tools, eight dimensions, measured with real execution times and real agent prompts.
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We gave the same three interview questions from StrataScratch to SQL, Pandas , and a Claude agent. The harder the question, the more the differences between SQL, Pandas, and the agent become visible. Agent response times are measured from the time the request is sent to the first token received.
TL;DR:
Same three analytics problems, three tools, eight dimensions, measured with real execution times and real agent prompts.
Towardsdatascience
Jul 7, 15:00
Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation
A technical comparison of Proxy-Pointer and LLM-Wiki The post Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation appeared first on Towards Data Science .
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Enterprise Retrieval-Augmented Generation (RAG) has evolved rapidly in recent times. The original RAG paradigm was designed to be straightforward: retrieve the most relevant chunks from a corpus and use them to answer a question. Future queries are answered primarily from this compiled knowledge rather than the original documents.
TL;DR:
A technical comparison of Proxy-Pointer and LLM-Wiki The post Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation appeared first on Towards Data Science .
Kdnuggets
Jul 7, 14:00
Zero-Shot Local Document Parsing with Gemma 4: Treating PDFs as Images
Treating PDFs as images and feeding those images to Gemma 4 dissolves the scanned-versus-digital distinction that makes every text-extraction pipeline fragile. Fix that.
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Text-extraction tools have one assumption baked in: the PDF has a selectable text layer. Feed that image to a vision-language model. The model reads the page the way a human reads a printed page.
TL;DR:
Treating PDFs as images and feeding those images to Gemma 4 dissolves the scanned-versus-digital distinction that makes every text-extraction pipeline fragile.
Towardsdatascience
Jul 7, 13:30
Identifying Microbes in Space
What's living on the International Space Station? The post Identifying Microbes in Space appeared first on Towards Data Science .
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What's living on the International Space Station? The post Identifying Microbes in Space appeared first on Towards Data Science .
TL;DR:
The post Identifying Microbes in Space appeared first on Towards Data Science .
Kdnuggets
Jul 7, 12:00
10 Probability Concepts for Machine Learning Explained Simply
A model is almost never 100% sure of anything. These 10 probability concepts explain how it makes decisions anyway.
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10 Probability Concepts for Machine Learning Explained Simply. A model is almost never 100% sure of anything. These 10 probability concepts explain how it makes decisions anyway.
TL;DR:
A model is almost never 100% sure of anything.
Towardsdatascience
Jul 7, 12:00
Survival Analysis for Data Drift and ML Reliability
Treating model degradation as a time-to-failure problem The post Survival Analysis for Data Drift and ML Reliability appeared first on Towards Data Science .
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Introduction Machine Learning systems rarely fail in a single moment. Their performance changes gradually as data distributions shift, calibration drifts, or new patterns emerge in the environment. These tools allow us to move beyond ad hoc thresholds and toward principled, data‑driven decisions about retraining schedules, alerting policies and long‑term maintenance.
TL;DR:
Treating model degradation as a time-to-failure problem The post Survival Analysis for Data Drift and ML Reliability appeared first on Towards Data Science .
Debasishg
Jul 7, 03:01
Cache-Conscious Data Layout in Rust: Field Zoning, False Sharing, 128-Byte Rule
Part 1 of Low-Level Systems Design in Rust - a series on writing high-throughput, low-latency systems code, using a single-producer / single-consumer (SPSC) ring buffer as the running example.
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Cache-Conscious Data Layout in Rust: Field Zoning, False Sharing, 128-Byte Rule. This post starts the micro-level work: given one such SPSC ring, how should it be laid out in memory? This post is about designing the layout deliberately.
TL;DR:
Part 1 of Low-Level Systems Design in Rust - a series on writing high-throughput, low-latency systems code, using a single-producer / single-consumer (SPSC) ring buffer as the running example.
Kdnuggets
Jul 6, 16:00
Data Scientists Are Becoming AI Managers, Not Model Builders
The role is shifting from building models to managing them.
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Data Scientists Are Becoming AI Managers, Not Model Builders. The role is shifting from building models to managing them.
TL;DR:
The role is shifting from building models to managing them.
Towardsdatascience
Jul 6, 15:00
How to Run End-to-End Tests with Claude Code
Increase the effectiveness of your coding agents through end-to-end testing. The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science .
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Increase the effectiveness of your coding agents through end-to-end testing. The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science .
TL;DR:
The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science .
Kdnuggets
Jul 6, 14:00
Getting Started with Hugging Face ML Intern: Your First ML Agent
You describe the model. It writes the code, runs the training, and ships the checkpoint. Welcome to ML Intern.
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Getting Started with Hugging Face ML Intern: Your First ML Agent. It writes the code, runs the training, and ships the checkpoint. Welcome to ML Intern.
TL;DR:
Welcome to ML Intern.
Towardsdatascience
Jul 6, 13:30
Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop
Enterprise Document Intelligence [Vol.1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback The post Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #8C] – Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback This article closes the generation brick of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and generation.
TL;DR:
Enterprise Document Intelligence [Vol.1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback The post Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop appeared first on Towards Data Science .
Kdnuggets
Jul 6, 12:00
5 Ways Small Language Models Are Powering Next-Gen Agents
This article looks at five concrete ways SLMs are showing up inside next-generation agents right now, from the research backing them to the tools and numbers worth knowing if you're deciding whether your next agent needs a frontier model at all
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This article looks at five concrete ways SLMs are showing up inside next-generation agents right now, from the research backing them to the tools and numbers worth knowing if you're deciding whether your next agent needs a frontier model at all For the last two years, the assumption in agentic AI was simple: the bigger the model, the better the agent.
TL;DR:
This article looks at five concrete ways SLMs are showing up inside next-generation agents right now, from the research backing them to the tools and numbers worth knowing if you're deciding whether your next agent needs a frontier model at all
Towardsdatascience
Jul 6, 12:00
Stop Ranking Agent Configs by Average Score
Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next. The post Stop Ranking Agent Configs by Average Score appeared first on Towards Data Science .
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Stop Ranking Agent Configs by Average Score. Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next. The post Stop Ranking Agent Configs by Average Score appeared first on Towards Data Science .
TL;DR:
The post Stop Ranking Agent Configs by Average Score appeared first on Towards Data Science .
Machinelearningmastery
Jul 6, 11:33
The Complete Guide to Tool Selection in AI Agents
Share Post Share In this article, you will learn why agent accuracy degrades as a tool catalog grows, and six practical techniques for keeping tool selection accurate and efficient at scale.
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The Complete Guide to Tool Selection in AI Agents. The Complete Guide to Tool Selection in AI Agents Click to enlarge You build an agent with five tools. Nothing about the model changed.
TL;DR:
Share Post Share In this article, you will learn why agent accuracy degrades as a tool catalog grows, and six practical techniques for keeping tool selection accurate and efficient at scale.
Towardsdatascience
Jul 5, 15:00
Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs
Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call The post Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #8B] – A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call This article is the second part of the generation brick of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, an…
TL;DR:
Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call The post Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs appeared first on Towards Data Science .
Towardsdatascience
Jul 5, 13:00
PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up
Understanding how PANet shortens the path between low-level and high-level features The post PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up appeared first on Towards Data Science .
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Understanding how PANet shortens the path between low-level and high-level features In my previous article I wrote about the FPN (Feature Pyramid Network) architecture [1], which is one of the most influential necks we can apply to a backbone model. FPN was first introduced to enhance the capability of an object detection model to detect small objects.
TL;DR:
Understanding how PANet shortens the path between low-level and high-level features The post PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up appeared first on Towards Data Science .
Towardsdatascience
Jul 4, 15:00
Setting Up Your Own Large Language Model
Still a long way to go, but the future is promising The post Setting Up Your Own Large Language Model appeared first on Towards Data Science .
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You’ve likely seen the headlines : frontier AI models are increasingly at risk of being locked behind strict export controls or mounting API costs. Today, the foundation for true democratization is already here: you can run a highly capable model entirely on your own laptop.
TL;DR:
Still a long way to go, but the future is promising The post Setting Up Your Own Large Language Model appeared first on Towards Data Science .
Towardsdatascience
Jul 4, 13:00
Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
Enterprise Document Intelligence [Vol.1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #8A] – The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable This article opens the generation brick of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and generation.
TL;DR:
Enterprise Document Intelligence [Vol.1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .
Sparsethought
Jul 3, 20:32
Giving a domain a hill to climb: benchmarking as data activation
0:00 0:00 1× give me an optimizable metric and i’ll move the world. benchmarking, i.e., the act of building and applying benchmarks, is a new(ish) form of data activation: a way of turning doma…
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Giving a domain a hill to climb: benchmarking as data activation. i wrote about data activation a while back, and i want to come back to it, because i think benchmarking is one of the cleaner examples of the thing i was reaching for then. so the interesting question, before any of the RL machinery, is whether you can give the domain a hill at all.
TL;DR:
benchmarking, i.e., the act of building and applying benchmarks, is a new(ish) form of data activation: a way of turning domain data into something models can be measured against, ranked by, and eventually trained on.
Towardsdatascience
Jul 3, 16:30
AI Agents Explained: What Is a ReAct Loop and How Does It Work?
How agents reason, act, and observe their way to a final answer, one step at a time The post AI Agents Explained: What Is a ReAct Loop and How Does It Work? appeared first on Towards Data Science .
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AI Agents Explained: What Is a ReAct Loop and How Does It Work?. How agents reason, act, and observe their way to a final answer, one step at a time The post AI Agents Explained: What Is a ReAct Loop and How Does It Work? appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Towardsdatascience
Jul 3, 15:00
Long Context vs. Short Context Model: When Does a Long Context Model Win?
Balancing context capability against cost, speed, and data The post Long Context vs. Short Context Model: When Does a Long Context Model Win? appeared first on Towards Data Science .
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Long Context vs. Balancing context capability against cost, speed, and data The post Long Context vs. appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Towardsdatascience
Jul 3, 13:30
LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler
Most "LLM wikis" use agents, embeddings, and repeated model calls to organize local notes. I built a deterministic alternative: a pure Python compiler that turns messy markdown into a linked, linted wiki using only the standard library. Along the way, I fixed two real bugs, benchmarked the pipeline on two operating systems, and showed why a compiler is often a better fit than an agent for mechanical text organization. The post LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler appeared first on Towards Data Science .
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Most "LLM wikis" use agents, embeddings, and repeated model calls to organize local notes. I built a deterministic alternative: a pure Python compiler that turns messy markdown into a linked, linted wiki using only the standard library. The post LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler appeared first on Towards Data Science .
TL;DR:
The post LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler appeared first on Towards Data Science .
Kdnuggets
Jul 3, 12:00
Getting Started with the Claude API in Python
In this article, you'll learn how to use the Claude API in Python, make your first request, and handle responses with the official SDK.
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You want to add Claude to a Python application. This article walks you through setup, your first API call, reading the response, system prompts, and streaming. You need Python 3.9 or higher, a free Claude Console account , and an API key from the Console's Settings > API Keys page.
TL;DR:
In this article, you'll learn how to use the Claude API in Python, make your first request, and handle responses with the official SDK.
Towardsdatascience
Jul 3, 12:00
The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation
Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #7ter] – Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG This article is a manifesto companion to Enterprise Document Intelligence , the series whose philosophy is laid out in Amplify the Expert . Retrieval is filtering on structured tables , not searching free text.
TL;DR:
Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation appeared first on Towards Data Science .
Towardsdatascience
Jul 2, 16:30
Tokenminning: How to Get More from Your Chatbot for Less
Tokenmaxxing is out. Real patterns for reducing costs without sacrificing AI effectiveness The post Tokenminning: How to Get More from Your Chatbot for Less appeared first on Towards Data Science .
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Tokenminning is a new pattern, which systematically minimizes token use while maintaining, if not improving, the performance of your AI agents. In this article, I cover practical strategies for tokenminning that I use to reduce costs. This assumption leads to larger than necessary prompts, loaded with uncompressed context and RAG bloat.
TL;DR:
Real patterns for reducing costs without sacrificing AI effectiveness The post Tokenminning: How to Get More from Your Chatbot for Less appeared first on Towards Data Science .
Github
Jul 2, 15:58
Show HN: ctx – Search the coding agent history already on your machine
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window.
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Reload to refresh your session. Reload to refresh your session. Reload to refresh your session.
TL;DR:
You signed in with another tab or window.
Towardsdatascience
Jul 2, 15:00
Design Loops, Not Prompts
“ We don’t write prompts anymore. We design loops.” — someone at Anthropic in June 2026 In a self-correcting agent loop, self-critique did no better than doing nothing.
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A loop is far harder to verify than a single call: with one call you check one output, but in a loop every step can drift, and the ways it can go wrong multiply with each iteration. A single call has one place to be wrong: the answer. Each of those is a model output, and each can be confidently wrong.
TL;DR:
But don't let the model check itself The post Design Loops, Not Prompts appeared first on Towards Data Science .
Machinelearningmastery
Jul 2, 14:02
Context vs. Memory Engineering in Agentic AI Systems
Share Post Share In this article, you will learn how context engineering and memory engineering solve different problems in agentic AI systems, and how the two disciplines meet at the point where ret…
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Share Post Share In this article, you will learn how context engineering and memory engineering solve different problems in agentic AI systems, and how the two disciplines meet at the point where retrieved memory enters the context window. As AI agents move into longer workflows and multi-session use cases, a familiar pattern emerges.
TL;DR:
Compression on Arrival Tool outputs should be compressed after a call returns, not after the window fills.
Kdnuggets
Jul 2, 14:00
10 Agentic AI Frameworks You Should Know in 2026
LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra, and more. If you're building AI agents in 2026, these are the frameworks worth paying attention to before starting your next project.
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10 Agentic AI Frameworks You Should Know in 2026. LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra, and more. If you're building AI agents in 2026, these are the frameworks worth paying attention to before starting your next project.
TL;DR:
LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra, and more.
Towardsdatascience
Jul 2, 13:30
Time-Series LLMs, Explained with t0-alpha
t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. Raw series are split into 32-step patches, embedded, processed through causal time-attention and group-attention layers, and decoded into future quantiles rather than a single point forecast. The post Time-Series LLMs, Explained with t0-alpha appeared first on Towards Data Science .
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Time-Series LLMs, Explained with t0-alpha. t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. The post Time-Series LLMs, Explained with t0-alpha appeared first on Towards Data Science .
TL;DR:
The post Time-Series LLMs, Explained with t0-alpha appeared first on Towards Data Science .
Kdnuggets
Jul 2, 12:00
Humanity’s Last Exam is a Distraction
This article takes a gentle dive into the ultimate AI systems evaluation benchmark, outlining why it was created, curating diverse opinions from groups of experts in the field about it, and wrapping up with a summary of the most widely accepted verdict.
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Humanity's Last Exam (HLE) is a benchmark designed to measure the reasoning and deep knowledge capabilities of most modern AI systems. Traditional testing methods used in classic AI systems became obsolete as these systems evolved and started to score perfectly without much effort.
TL;DR:
This article takes a gentle dive into the ultimate AI systems evaluation benchmark, outlining why it was created, curating diverse opinions from groups of experts in the field about it, and wrapping up with a summary of the most widely accepted verdict.
Towardsdatascience
Jul 2, 12:00
The Untaught Lessons of RAG Question Parsing: Structure Before You Search
Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #6ter] – Six positions on the question-parsing brick that contradict the mainstream RAG playbook This article is a manifesto companion to Enterprise Document Intelligence , the series whose philosophy is laid out in Amplify the Expert . Most RAG tutorials skip question parsing.
TL;DR:
Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science .
Towardsdatascience
Jul 1, 16:30
Why Powerful ML Is Deceptively Easy — Part 2
The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. AI-generated illustration created with DALL·E The post Why Powerful ML Is Deceptively Easy — Part 2 appeared first on Towards Data Science .
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The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. AI-generated illustration created with DALL·E The post Why Powerful ML Is Deceptively Easy — Part 2 appeared first on Towards Data Science .
TL;DR:
AI-generated illustration created with DALL·E The post Why Powerful ML Is Deceptively Easy — Part 2 appeared first on Towards Data Science .
Towardsdatascience
Jul 1, 13:30
What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
How Pandas chunking, Dask, and Polars help process millions of records when adding more compute isn't an option. The post What Can We Do When Memory Becomes the New Bottleneck in Data Engineering? appeared first on Towards Data Science .
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What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?. The post What Can We Do When Memory Becomes the New Bottleneck in Data Engineering? appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Databricks
Jul 1, 12:48
Postgres data stored in Parquet on S3: LTAP architecture explained
When I started my PhD at UC Berkeley 16 years ago, my advisor told me: "OLTP databases are a solved problem. They work. Focus on analytics.
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Postgres data stored in Parquet on S3: LTAP architecture explained. This post takes a deep dive into the Lakebase OLTP architecture. Finally, we turn to LTAP , where that same architecture lets transactions and analytics run on a single copy of the data, in real time, without the delays and extra cost of CDC or "mirroring.”
TL;DR:
Focus on analytics." We were at the early innings of being able to collect far more data, structured and unstructured, and apply machine learning (which we now call “AI”).
Kdnuggets
Jul 1, 12:00
5 AI Coding Platforms to Build Apps Without the Headache
Explore the best AI coding platforms, no-code app builders, and vibe coding tools that help beginners and developers build, test, and deploy full-stack apps using simple prompts.
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You might start exploring tools like Claude Code or other terminal-based AI coding assistants, only to realize that you still need to handle setup, security, deployment, hosting, scaling, and a lot of technical decision-making.
TL;DR:
Explore the best AI coding platforms, no-code app builders, and vibe coding tools that help beginners and developers build, test, and deploy full-stack apps using simple prompts.
Towardsdatascience
Jul 1, 12:00
Build and Run Your Own AI Agent in the Cloud
Build and deploy an agent on AWS with Strands and AgentCore The post Build and Run Your Own AI Agent in the Cloud appeared first on Towards Data Science .
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Using AI on AWS to perform useful work can be relatively straightforward. For a simple application, a few lines of Python using the boto3 library and the Bedrock API may be all that’s needed. All images shown in this article, apart from the headline image which is AI generated, were created by the author.
TL;DR:
Build and deploy an agent on AWS with Strands and AgentCore The post Build and Run Your Own AI Agent in the Cloud appeared first on Towards Data Science .
Research
Jun 30, 22:08
TabFM: A zero-shot foundation model for tabular data
Introducing TabFM: A zero-shot foundation model for tabular data June 30, 2026 Weihao Kong and Abhimanyu Das, Research Scientists, Google Research We’ve seen a massive shift in how people handle time…
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Now, we’re bringing that same "zero-shot" logic to tabular data. Tabular data constitutes the backbone of enterprise data infrastructure and powers a significant fraction of critical predictive machine learning applications .
TL;DR:
We introduce TabFM, a new foundation model for tabular data to simplify classification and regression workflows.
Towardsdatascience
Jun 30, 16:30
Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer
Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on one LLM call. Corpus, conversation, and tool extensions are follow-up work The post Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on one LLM call. Corpus, conversation, and tool extensions are follow-up work The post Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer appeared first on Towards Data Science .
TL;DR:
Corpus, conversation, and tool extensions are follow-up work The post Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer appeared first on Towards Data Science .
404media
Jun 30, 16:05
County with 37 Data Centers Asks Schools to 'Conserve Electricity'
On June 26, the County Manager of Henrico County, Virginia, John Vithoulkas, sent an email to thousands of county employees asking them to help the local government conserve electricity.
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County with 37 Data Centers Asks Schools to 'Conserve Electricity'. It also hosts 37 data centers and there are plans to build 17 more , including plans to convert hundreds of acres of Civil War battlefields into data centers.
TL;DR:
Meta built a data center there in 2017.
Waag
Jun 30, 15:17
We moved our Bluesky data to Eurosky
At a time when major tech platforms are concentrating ever more power, it is vital to take as many steps as possible towards digital autonomy.
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We moved our Bluesky data to Eurosky. A Personal Data Server (PDS) is where your personal data is stored within the AT Protocol, the network protocol on which Bluesky is built. On traditional social media, you manage your account, but not your data.
TL;DR:
Instead, our data now runs on Eurosky’s Personal Data Server (PDS).
Towardsdatascience
Jun 30, 15:00
Surviving the Data Science Behavioral Interview
In the age of AI, standing out here means a lot more than ever. Here are three tips to walk into your next interview with confidence. The post Surviving the Data Science Behavioral Interview appeared first on Towards Data Science .
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Surviving the Data Science Behavioral Interview. In the age of AI, standing out here means a lot more than ever. The post Surviving the Data Science Behavioral Interview appeared first on Towards Data Science .
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The post Surviving the Data Science Behavioral Interview appeared first on Towards Data Science .
Kdnuggets
Jun 30, 14:00
Building Local AI Systems: Qwen3.6 + MCPs
Define a tool once as an MCP server and any MCP-compatible client, any model, any framework, can discover and call it with zero custom integration code per model.
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Every developer building with local AI hits the same wall eventually. You are left writing custom Python wrappers for every tool you need, hardcoding the glue between model output and tool execution, and maintaining those wrappers every time an API changes. Qwen3.6-35B-A3B is the most capable local model for this kind of work right now.
TL;DR:
Define a tool once as an MCP server and any MCP-compatible client, any model, any framework, can discover and call it with zero custom integration code per model.
Towardsdatascience
Jun 30, 13:30
How to Maximize Codex Exec Command
Build a more powerful coding agent setup with a model ensemble The post How to Maximize Codex Exec Command appeared first on Towards Data Science .
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Build a more powerful coding agent setup with a model ensemble Codex exec is a command you can use to run Codex separately from the terminal to complete a very specific task, where the agent triggering Codex only receives the final output from the task.
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Build a more powerful coding agent setup with a model ensemble The post How to Maximize Codex Exec Command appeared first on Towards Data Science .
Kdnuggets
Jun 30, 12:00
7 Real-World Python Projects You Can Build in 2026 (With Guides)
Check out this practical list of Python projects covering AI automation, machine learning, APIs, dashboards, data analysis, and portfolio-ready apps, with guides, demos, repositories, and datasets.
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Python remains one of the best programming languages for building practical, real-world projects, especially as AI, automation, APIs, dashboards, and data applications continue to grow in 2026. In this article, I have put together seven Python projects that I personally created, tested, and documented so you can follow along without getting stuck.
TL;DR:
Check out this practical list of Python projects covering AI automation, machine learning, APIs, dashboards, data analysis, and portfolio-ready apps, with guides, demos, repositories, and datasets.
Machinelearningmastery
Jun 30, 12:00
Context Window Management for Long-Running Agents: Strategies and Tradeoffs
In this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach...
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Share Post Share In this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach introduces. Agents and large language models, or LLMs in their abbreviated form, are two sides of the same coin in modern AI systems, so to speak.
TL;DR:
In this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach...
Towardsdatascience
Jun 30, 12:00
Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns
A hands-on walkthrough of a hybrid local-cloud workflow using Gemma 4 and GPT-5.4, with reasoning and structured outputs The post Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns appeared first on Towards Data Science .
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A hands-on walkthrough of a hybrid local-cloud workflow using Gemma 4 and GPT-5.4, with reasoning and structured outputs Today, when people build LLM applications, two deployment choices are commonly seen: either we go fully cloud, i.e., sending everything to a cloud LLM API, or we go fully local, i.e., run everything with an open model served locally.
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A hands-on walkthrough of a hybrid local-cloud workflow using Gemma 4 and GPT-5.4, with reasoning and structured outputs The post Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns appeared first on Towards Data Science .
Noyb
Jun 30, 05:17
US Supreme Court Just Blew Up EU-US Data Transfers
On Monday, the US Supreme Court decided in Trump v. Slaughter that the US Federal Trade Commission (“FTC”) may not be independent anymore.
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US Supreme Court Just Blew Up EU-US Data Transfers. Since 1995 the EU generally prohibits the export of personal data to third countries, to ensure that EU privacy rules cannot be evaded by simply sending data abroad.
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Since 2000 the EU has relied on the “independent” FTC as the enforcer of EU-US deals on personal data.
Towardsdatascience
Jun 29, 17:34
How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context. The post How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification appeared first on Towards Data Science .
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An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context. The post How Far Can Classical NLP Go?
TL;DR:
From Bag-of-Words to Stacking on Spooky Author Identification appeared first on Towards Data Science .
Towardsdatascience
Jun 29, 15:00
Prompt Engineering Fails Quietly — Prompt Regression Is Why
Small prompt changes can silently break critical behavior in production. This article introduces a practical framework to detect hidden regressions before users notice. The post Prompt Engineering Fails Quietly — Prompt Regression Is Why appeared first on Towards Data Science .
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Small prompt changes can silently break critical behavior in production. This article introduces a practical framework to detect hidden regressions before users notice. The post Prompt Engineering Fails Quietly — Prompt Regression Is Why appeared first on Towards Data Science .
TL;DR:
The post Prompt Engineering Fails Quietly — Prompt Regression Is Why appeared first on Towards Data Science .
Blog
Jun 29, 14:26
Using Aspect-Oriented Programming to Record DRL Agents' Data
Playtesting is one of the most important processes in game development. It helps with finding bugs, evaluating the game's UX, balancing the game, and, most importantly, assessing how fun it is.
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Using Aspect-Oriented Programming to Record DRL Agents' Data. My focus on this project is on data collection, aggregation, and visualization for the user. Basically, I am responsible for the step where an agent collects data while playing and uses that data to tell whether a game is balanced or not (just as an example).
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Basically, I am responsible for the step where an agent collects data while playing and uses that data to tell whether a game is balanced or not (just as an example).
Kdnuggets
Jun 29, 14:00
Your RAG Pipeline Is Probably Useless. Here’s a Better Alternative
Learn what to reach for when retrieval-augmented generation fails in production.
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Your RAG Pipeline Is Probably Useless. Here’s a Better Alternative. Learn what to reach for when retrieval-augmented generation fails in production.
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Learn what to reach for when retrieval-augmented generation fails in production.
Towardsdatascience
Jun 29, 13:30
I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work
The tools I use for analytics and reporting have changed more than I expected, yet my questions for any analytics project haven't moved much. The post I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work appeared first on Towards Data Science .
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I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work. The tools I use for analytics and reporting have changed more than I expected, yet my questions for any analytics project haven't moved much. The post I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work appeared first on Towards Data Science .
TL;DR:
The post I Completed Five Years in Analytics Consulting: 5 Lessons That Changed How I Work appeared first on Towards Data Science .
Kdnuggets
Jun 29, 12:00
5 AI Coding Subscription Plans That Give Developers the Best Value
This is an opinion-based look at the AI coding subscription plans that I think give developers the best value for their money, from token and usage-based plans to full coding-agent ecosystems.
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For a while, "unlimited" AI coding plans felt like the best deal in developer tools. You paid a fixed monthly fee and used powerful coding agents as much as you wanted. Now, many AI coding platforms are moving toward more controlled subscription models.
TL;DR:
This is an opinion-based look at the AI coding subscription plans that I think give developers the best value for their money, from token and usage-based plans to full coding-agent ecosystems.
Machinelearningmastery
Jun 29, 12:00
Model Context Protocol Explained in 3 Levels of Difficulty
MCP provides a standard way for AI applications and external systems to communicate.
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Model Context Protocol Explained in 3 Levels of Difficulty. MCP provides a standard way for AI applications and external systems to communicate.
TL;DR:
MCP provides a standard way for AI applications and external systems to communicate.
Towardsdatascience
Jun 29, 12:00
How to Choose Between Small and Frontier Models
The rise of small language models The post How to Choose Between Small and Frontier Models appeared first on Towards Data Science .
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Small Models , Big Moment For most of the last three years in AI, the reflex was simple. You had an AI task , so you called GPT or Claude or Gemini. I write more about AI Engineering here .
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The rise of small language models The post How to Choose Between Small and Frontier Models appeared first on Towards Data Science .
Towardsdatascience
Jun 28, 15:00
Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows
Behind a customer's API, a high-quality answer isn't enough. It has to be usable, which means on time. Delivering that consistently is a problem about variance, not speed, and the fixes are counterintuitive. The post Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows appeared first on Towards Data Science .
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Behind a customer's API, a high-quality answer isn't enough. Delivering that consistently is a problem about variance, not speed, and the fixes are counterintuitive. The post Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows appeared first on Towards Data Science .
TL;DR:
The post Tail Control: The Counterintuitive Engineering of Reliable Agentic Workflows appeared first on Towards Data Science .
Towardsdatascience
Jun 28, 13:00
I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won.
A concrete bias–variance lesson: why the smallest model had the best cross-validated fit, and how to know when to reach for the big hammer. The post I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won. appeared first on Towards Data Science .
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I Pitted XGBoost Against Logistic Regression on 358 Matches. The post I Pitted XGBoost Against Logistic Regression on 358 Matches. appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Gadgetreview
Jun 27, 20:16
A Farmer Arrested for Going 5 Seconds over His Time Limit at Data Center Meeting
Claremore resident Darren Blanchard faced trespass charges after speaking past a three-minute timer at a February council meeting on the 300-acre Project Mustang campus X LinkedIn Facebook I reincarn…
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A Farmer Arrested for Going 5 Seconds over His Time Limit at Data Center Meeting. A sacred rebel at heart, I believe in cooperation over competition and speaking my truth even if it ruffles a few feathers. A sacred rebel at heart, I believe in cooperation over competition and speaking my truth even if it ruffles a few feathers.
TL;DR:
Then an officer’s voice, flat and final: “Arrest him.” Darren Blanchard had committed the offense of speaking slightly past a three-minute public-comment timer — at a meeting about a 270-to-300-acre data center campus proposed for his community.
Towardsdatascience
Jun 27, 15:00
We Built a Routing Layer to Cut Our AI Costs. It Broke the Product.
A team cut their AI inference bill by more than half. Three months later, customer satisfaction was dropping and the cost savings were tied to the quality loss. Cost-optimization routing layers are a Pareto trap, and here's the detection methodology that catches them in days instead of months. The post We Built a Routing Layer to Cut Our AI Costs. It Broke the Product. appeared first on Towards Data Science .
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We Built a Routing Layer to Cut Our AI Costs. The post We Built a Routing Layer to Cut Our AI Costs. appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
Jun 27, 13:00
How to Build a Powerful LLM Knowledge Base
Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science .
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A knowledge base is a concept where you store a lot of information, and you make it accessible for future use. Knowledge bases were always useful even before LLMs, because it’s always useful to access past knowledge.
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Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science .
Newsweek
Jun 26, 17:24
'Cost Me the Election': Data Centers Trigger Voter Backlash
U.S. News Politics Tech Fact Check Sports Global Iran War Russia-Ukraine Middle East China And Asia Live Blog Better Planet All World News Lifestyle Family & Parenting Entertainment Travel Pets Autom…
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'Cost Me the Election': Data Centers Trigger Voter Backlash. Prefer Newsweek on Google to see more of our trusted coverage when you search. Stuart Adams—one of the most powerful Republicans in the state—lost his primary election after supporting a major data center development near the Great Salt Lake, in one of the clearest signs yet of the growing political risks tied to the…
TL;DR:
A wave of voter anger over massive data center projects is beginning to reshape U.S.
Towardsdatascience
Jun 26, 16:30
From Local LLM to Tool-Using Agent
Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent The post From Local LLM to Tool-Using Agent appeared first on Towards Data Science .
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Well, how about making the local LLM agentic with some tool use? By the end of the post, you’d have a working local deep research agent and a reusable implementation pattern for turning a local model into a local AI agent. In this post, we focus on the more general pattern of connecting a local model to an agent runtime and external tools.
TL;DR:
Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent The post From Local LLM to Tool-Using Agent appeared first on Towards Data Science .
Kdnuggets
Jun 26, 15:00
Fine-tuning Language Models on Apple Silicon with MLX
Fine-tune open language models locally on your Mac using MLX. No cloud GPUs or costs required.
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Fine-tuning Language Models on Apple Silicon with MLX. Fine-tune open language models locally on your Mac using MLX. No cloud GPUs or costs required.
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Fine-tune open language models locally on your Mac using MLX.
Towardsdatascience
Jun 26, 15:00
Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation
Why memorizing for the exam doesn't mean you understand the subject The post Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation appeared first on Towards Data Science .
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Water Cooler Small Talk, Ep. Why memorizing for the exam doesn't mean you understand the subject The post Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation appeared first on Towards Data Science .
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11: Overfitting in RAG evaluation appeared first on Towards Data Science .
Machinelearningmastery
Jun 26, 14:01
The AI Agent Tech Stack Explained
Share Post Share In this article, you will learn how the seven layers of a production AI agent stack fit together, from the foundation model down to deployment infrastructure.
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The AI Agent Tech Stack Explained. Picture this: you ask an AI agent to research three competitors, pull the pricing data from each of their websites, summarize the findings into a structured report, and drop it in a Slack channel by 9am.
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Share Post Share In this article, you will learn how the seven layers of a production AI agent stack fit together, from the foundation model down to deployment infrastructure.
Kdnuggets
Jun 26, 13:34
5 Agentic Workflows to Automate Your Data Science Pipeline
This article covers five concrete agentic workflows, one for each major stage of a data science pipeline.
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The average data scientist spends roughly 45% of their working time on data preparation and cleaning, not on modeling, not on insight generation, not on the work that requires genuine judgment. Agentic workflows do not replace the data scientist.
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This article covers five concrete agentic workflows, one for each major stage of a data science pipeline.
Towardsdatascience
Jun 26, 13:30
Amplify the Expert: A Philosophy for Building Enterprise RAG
Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Building Enterprise RAG appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #M1] – The thesis behind every architectural choice in this series This article is a manifesto of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: parsing, question parsing, retrieval, and generation.
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Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Building Enterprise RAG appeared first on Towards Data Science .
Towardsdatascience
Jun 26, 12:00
How to Ace Data and ML Behavioural Interviews
How to smash through data / ML behavioural interviews The post How to Ace Data and ML Behavioural Interviews appeared first on Towards Data Science .
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Frameworks and guides to nail your next behavioural interview I thought they would be a walk in the park because who wouldn’t want to hire me? It’s easy to think that because data science and machine learning are technical fields, interviewers and companies only care about your technical abilities.
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How to smash through data / ML behavioural interviews The post How to Ace Data and ML Behavioural Interviews appeared first on Towards Data Science .
Towardsdatascience
Jun 25, 18:37
Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory
I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval. The post Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory appeared first on Towards Data Science .
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I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval. The post Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory appeared first on Towards Data Science .
TL;DR:
The post Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory appeared first on Towards Data Science .
Towardsdatascience
Jun 25, 18:00
The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark
A reproducible benchmark on latency, cost, and reproducibility, and where agents actually earn their keep. The post The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark appeared first on Towards Data Science .
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A reproducible benchmark on latency, cost, and reproducibility, and where agents actually earn their keep. The post The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark appeared first on Towards Data Science .
TL;DR:
The post The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark appeared first on Towards Data Science .
Towardsdatascience
Jun 25, 16:30
Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression
Whether you should stick to a classic Ordinary Least Squares regression, introduce interaction terms, or pivot to a Tweedie distribution depends entirely on how your data handles the messy reality of zeros and extreme outliers. The post Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression appeared first on Towards Data Science .
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Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression. Whether you should stick to a classic Ordinary Least Squares regression, introduce interaction terms, or pivot to a Tweedie distribution depends entirely on how your data handles the messy reality of zeros and extreme outliers.
TL;DR:
The post Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression appeared first on Towards Data Science .
Kdnuggets
Jun 25, 16:00
Using Gemini to Create Google Sheets
In this tutorial, we will show you how to use Gemini to create Google Sheets, build a useful table, generate formulas, analyze data, and improve the spreadsheet with follow-up prompts.
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Gemini in Google Sheets is a powerful AI integration from Google that lets you create, populate, analyze, and manage spreadsheets using natural language prompts. Instead of manually creating tables, formulas, and layouts, you can describe what you need, and Gemini can generate a spreadsheet structure, suggest formulas, and even create data for testing.
TL;DR:
In this tutorial, we will show you how to use Gemini to create Google Sheets, build a useful table, generate formulas, analyze data, and improve the spreadsheet with follow-up prompts.
Towardsdatascience
Jun 25, 15:00
3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal
Beat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control. The post 3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal appeared first on Towards Data Science .
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Beat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control. 1 Aging GPU: Engineering Parallel Inference on Bare Metal appeared first on Towards Data Science .
TL;DR:
1 Aging GPU: Engineering Parallel Inference on Bare Metal appeared first on Towards Data Science .
Kdnuggets
Jun 25, 14:00
5 Open Source Omni AI Models That Handle Text, Images, Audio, and Video
Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
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5 Open Source Omni AI Models That Handle Text, Images, Audio, and Video. Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
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Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
Healeycodes
Jun 25, 13:32
A Tiny Compiler for Data-Parallel Kernels
Modern hardware can perform the same operation on multiple values at once (e.g. SIMD and SIMT ), and sometimes we write code directly for those execution models but other times, a compiler starts wit…
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SIMD and SIMT ), and sometimes we write code directly for those execution models but other times, a compiler starts with regular-looking code and rewrites it so multiple loop iterations can run together. I built a tiny compiler (~180LOC of Python) to understand what that transformation looks like.
TL;DR:
My compiler lowers kernels (rewrites them into a simpler, more explicit form where data parallelism is visible).
Towardsdatascience
Jun 25, 13:30
Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval
Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend The post Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend The post Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval appeared first on Towards Data Science .
TL;DR:
The output is one typed object your auditor can defend The post Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval appeared first on Towards Data Science .
Kdnuggets
Jun 25, 12:00
The Roadmap to Becoming an AI Architect in 2026
Follow this step-by-step path through the design, decision-making, and leadership skills that move an engineer into the architect's seat.
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An AI architect is not a senior engineer doing more of the same work. Organizations have accumulated AI prototypes built during the past two years and now need people who can turn them into governed, cost-aware production systems.
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Follow this step-by-step path through the design, decision-making, and leadership skills that move an engineer into the architect's seat.
Machinelearningmastery
Jun 25, 12:00
Agentic Workflow vs. Autonomous Agent: What’s the Difference?
In this article, you will learn how to distinguish agentic workflows from autonomous agents by focusing on who owns control flow — a human writing...
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Share Post Share In this article, you will learn how to distinguish agentic workflows from autonomous agents by focusing on who owns control flow — a human writing code in advance, or a model reasoning at runtime. Deloitte projects that by 2027, up to 50% of companies using generative AI will have launched agentic AI pilots or proofs of concept.
TL;DR:
In this article, you will learn how to distinguish agentic workflows from autonomous agents by focusing on who owns control flow — a human writing...
Towardsdatascience
Jun 25, 12:00
One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About
A reflection on the first month of learning data engineering in public, and what actually kept me going. The post One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About appeared first on Towards Data Science .
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A reflection on the first month of learning data engineering in public, and what actually kept me going. The post One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About appeared first on Towards Data Science .
TL;DR:
The post One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About appeared first on Towards Data Science .
Towardsdatascience
Jun 24, 18:00
How to Build a Credit Scoring Grid From a Logistic Regression Model
Turning model coefficients into a 0–1000 score, with risk classes and stability checks The post How to Build a Credit Scoring Grid From a Logistic Regression Model appeared first on Towards Data Science .
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This article follows the same logic, but applies it to our own model. The goal is simple: give each retained variable a weight, compute the score for every client in our data, and show how a new client’s score is calculated. I keep saying this because it matters: you can use AI agents to speed up your work.
TL;DR:
Turning model coefficients into a 0–1000 score, with risk classes and stability checks The post How to Build a Credit Scoring Grid From a Logistic Regression Model appeared first on Towards Data Science .
Towardsdatascience
Jun 24, 16:30
Your First Task as a Data Engineer in a New Company? Make the ETL Pipeline Testable
A practical data engineering onboarding workflow for environment setup, automated testing, and AI-assisted development. The post Your First Task as a Data Engineer in a New Company? Make the ETL Pipeline Testable appeared first on Towards Data Science .
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Your First Task as a Data Engineer in a New Company? A practical data engineering onboarding workflow for environment setup, automated testing, and AI-assisted development. The post Your First Task as a Data Engineer in a New Company?
TL;DR:
Make the ETL Pipeline Testable appeared first on Towards Data Science .
Towardsdatascience
Jun 24, 15:00
A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT
Activation patching reveals how facts are stored, routed, and read out across transformer layers, and why the residual stream does most of the work The post A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT appeared first on Towards Data Science .
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This post presents BizzaroWorld, a mechanistic interpretability study attempting to localize factual recall circuits in the Gemma model family using activation patching across 60 prompt pairs and 20 knowledge categories. The technical work here is greatly influenced by the work done by Prakash et al.¹, who looked at entity tracking within the LLaMa series of models.
TL;DR:
Activation patching reveals how facts are stored, routed, and read out across transformer layers, and why the residual stream does most of the work The post A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT appeared first on Towards Data Science .
Blogs
Jun 24, 14:10
45°C cooling design cuts data center water use to near zero
Hot tubs sit at about 38 to 40 degrees Celsius, warm enough that most people can only soak for about 15 minutes.
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45°C cooling design cuts data center water use to near zero. NVIDIA’s newest AI servers can run their cooling liquid even hotter — up to 45 degrees Celsius, or 113 degrees Fahrenheit. This liquid cooling methodology is outlined in the NVIDIA DSX AI factory reference design, a guide that outlines best practices to design, build and operate the entire AI factory infrastructure s…
TL;DR:
Although each generation offers significantly more computing power for each watt, full liquid-cooled AI compute infrastructure enables data centers to dramatically reduce cooling energy consumption — making a meaningful difference to overall data center energy use at hyperscale.
Towardsdatascience
Jun 24, 13:30
Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead
A practical walkthrough using text-to-SQL as the example The post Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead appeared first on Towards Data Science .
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After completing the development we started testing it and realised that one agent architecture was not enough for our application. After thorough testing of the application we realised that a single agent couldn’t perform every task. There’s an assumption when you first start building with LLMs that if the model is capable enough, a good prompt can do everything.
TL;DR:
A practical walkthrough using text-to-SQL as the example The post Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead appeared first on Towards Data Science .
Machinelearningmastery
Jun 24, 12:00
Context Windows Are Not Memory: What AI Agent Developers Need to Understand
In this article, you will learn why a large context window is not the same thing as agent memory, and how techniques like retrieval, compression,...
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Share Post Share In this article, you will learn why a large context window is not the same thing as agent memory, and how techniques like retrieval, compression, and summarization fit together in an agent’s cognitive stack.
TL;DR:
In this article, you will learn why a large context window is not the same thing as agent memory, and how techniques like retrieval, compression,...
Towardsdatascience
Jun 24, 12:00
Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
Enterprise Document Intelligence [Vol.1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #7B] – Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last This article is the retrieval brick of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: parsing, question parsing, retrieval, and generation.
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Enterprise Document Intelligence [Vol.1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
Kdnuggets
Jun 24, 10:00
Top 7 Coding Models You Can Run Locally in 2026
Explore the best local coding models for private AI coding, fast GGUF inference, agentic workflows, multimodal development, and running powerful open models on your own GPU.
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Local coding models are finally getting serious. I have been a big fan of this new wave of local large language models (LLMs), especially the open models and community GGML Universal File (GGUF) releases that make them easier to run on consumer hardware.
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Explore the best local coding models for private AI coding, fast GGUF inference, agentic workflows, multimodal development, and running powerful open models on your own GPU.
Wired
Jun 24, 00:28
Meta Pauses Employee-Tracking Program Following Internal Data Leak
Loader Save Story Save this story Comment Loader Save Story Save this story Meta is pausing a divisive employee tracking program after an internal security issue exposed potentially sensitive data co…
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Meta Pauses Employee-Tracking Program Following Internal Data Leak. “We have carefully designed this program with privacy safeguards and while we have no indication at this time that any data was improperly accessed by Meta employees, we're pausing it while we investigate,” says company spokesperson Tracy Clayton.
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Loader Save Story Save this story Comment Loader Save Story Save this story Meta is pausing a divisive employee tracking program after an internal security issue exposed potentially sensitive data collected through the initiative to other workers.
Washingtonian
Jun 23, 21:53
WaPo Loves Data Centers More Than Disclosing Bezos's Financial Interest in Them
The Washington Post’s opinion editors love data centers, the humongous and increasingly unpopular server warehouses that are the physical backbone of the internet and artificial intelligence.
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WaPo Loves Data Centers More Than Disclosing Bezos's Financial Interest in Them. America needs more data centers to boost the economy, compete with China, and power the AI revolution. Many of AWS’s existing facilities are in “Data Center Alley” — the archipelago of centers carved from the farmlands of Loudon, Prince William and Fairfax counties in Northern Virginia , all just…
TL;DR:
The Washington Post’s opinion editors love data centers, the humongous and increasingly unpopular server warehouses that are the physical backbone of the internet and artificial intelligence.
Github
Jun 23, 21:29
ATProto Permissioned Data Proposal Draft
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ATProto Permissioned Data Proposal Draft. You signed in with another tab or window. Reload to refresh your session.
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Copy link Copy Markdown Contributor This is an early draft of the proposal for permissioned data.
Towardsdatascience
Jun 23, 18:00
How to Create Powerful Loops in Claude Code
Learn about the concept of loops to power your coding agents. The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science .
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Learn about the concept of loops to power your coding agents. The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science .
TL;DR:
The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science .
Kdnuggets
Jun 23, 17:00
The Math Skills Every Aspiring Data Scientist Needs to Master Before Writing a Single Line of Code
This article breaks down each essential math discipline, explains its role in data science, and maps out an efficient learning path you can start today.
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The Math Skills Every Aspiring Data Scientist Needs to Master Before Writing a Single Line of Code. This article breaks down each essential math discipline, explains its role in data science, and maps out an efficient learning path you can start today.
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This article breaks down each essential math discipline, explains its role in data science, and maps out an efficient learning path you can start today.
Towardsdatascience
Jun 23, 16:30
I Spent an Hour on a Data Preprocessing Task Before Asking Gemini
How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions The post I Spent an Hour on a Data Preprocessing Task Before Asking Gemini appeared first on Towards Data Science .
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How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions As data scientists, we spend a significant amount of time on data preparation for downstream tasks. Whether it involves data cleaning, handling missing values, feature engineering, data preprocessing, or post processing, this phase requires a lot of time.
TL;DR:
How Gemini solved my Pandas problem in seconds, and why data science fundamentals still matter to spot suboptimal solutions The post I Spent an Hour on a Data Preprocessing Task Before Asking Gemini appeared first on Towards Data Science .
Towardsdatascience
Jun 23, 15:00
Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG
Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG appeared first on Towards Data Science .
TL;DR:
Pick anchors small, expand context large The post Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG appeared first on Towards Data Science .
Towardsdatascience
Jun 23, 13:30
The Era of No-Code AI: What You Need to Know
If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .
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Now, the world has changed, and everyone can create AI without a single line of code. In 2025, building local Agents still largely meant writing Python code, with developers turning to tools like LangChain to run open-source models directly on their own computers. Every interaction with an AI model starts with a prompt.
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If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .
Machinelearningmastery
Jun 23, 12:00
Clustering Unstructured Text with LLM Embeddings and HDBSCAN
The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.
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Share Post Share In this article, you will learn how to build a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based clustering algorithm, to automatically discover topics in unlabeled text data. Once that’s done, we can use these text representations for a variety of machine learning use cases, with clustering being no exception.
TL;DR:
The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.
Kdnuggets
Jun 23, 12:00
5 Essential Approaches to Robust Outlier Detection
Outliers can easily ruin the performance of any predictive analysis models you build: robustly detecting and handling them is crucial in any data project. This article lists and compares five essential approaches for detecting them.
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5 Essential Approaches to Robust Outlier Detection. Outliers can easily ruin the performance of any predictive analysis models you build: robustly detecting and handling them is crucial in any data project. This article lists and compares five essential approaches for detecting them.
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Outliers can easily ruin the performance of any predictive analysis models you build: robustly detecting and handling them is crucial in any data project.
Towardsdatascience
Jun 23, 12:00
Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode
From installing Ollama to launching OpenCode with a local model, step by step. The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science .
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From installing Ollama to launching OpenCode with a local model, step by step. The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science .
TL;DR:
The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science .
Kdnuggets
Jun 22, 17:00
ChatLLM by Abacus AI Review: A Multi-Model AI Workspace Built for Daily Work
An in-depth review of ChatLLM by Abacus AI, covering supported AI models, AI agents, coding tools, integrations, pricing, usage limits, and how it compares to ChatGPT.
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One platform offers access to OpenAI models, another focuses on Claude, while a third specializes in image generation or AI agents. Instead of offering access to a single AI model, ChatLLM brings together many of the world's leading AI models under one subscription.
TL;DR:
An in-depth review of ChatLLM by Abacus AI, covering supported AI models, AI agents, coding tools, integrations, pricing, usage limits, and how it compares to ChatGPT.
Towardsdatascience
Jun 22, 16:30
Encoding Categorical Data for Outlier Detection
Why one-hot encoding isn’t always the best approach, and alternative encodings The post Encoding Categorical Data for Outlier Detection appeared first on Towards Data Science .
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In this article, we look at working with categorical data. Generally when performing outlier detection with tabular data, we start by converting the data so that it is either entirely categorical or entirely numeric.
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Why one-hot encoding isn’t always the best approach, and alternative encodings The post Encoding Categorical Data for Outlier Detection appeared first on Towards Data Science .
Patrickdomanico
Jun 22, 15:24
Inventing the Future, One Lisp Machine at a Time
InsightAI Between People & Machine More InsightAI Between People & Machine More InsightAI Between People & Machine Tech Inventing the Future, One Lisp Machine at a Time Revolutionizing Last-Mile Deli…
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Tech AI Entertainment Facebook Instagram X Home Tech Inventing the Future, One Lisp Machine at a Time Tech Facebook X Pinterest WhatsApp Larry Masinter and Frank Halasz on Xerox PARC, Interlisp, NoteCards, and why “residential programming” still matters On the March 10, 2025 episode of Do You Speak Tech?
TL;DR:
InsightAI Between People & Machine More InsightAI Between People & Machine More InsightAI Between People & Machine Tech Inventing the Future, One Lisp Machine at a Time Revolutionizing Last-Mile Delivery: Bobby Healy and Manna Drone Delivery AI Entertainment More SEARCH...
Towardsdatascience
Jun 22, 15:00
How to Use Claude Code in Your Browser
Learn how to apply coding agents to verify work in your browser. The post How to Use Claude Code in Your Browser appeared first on Towards Data Science .
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Learn how to apply coding agents to verify work in your browser. The post How to Use Claude Code in Your Browser appeared first on Towards Data Science .
TL;DR:
The post How to Use Claude Code in Your Browser appeared first on Towards Data Science .
Chevron
Jun 22, 13:43
Chevron signs 20-year power agreement with Microsoft for West Texas data center
HOUSTON, June 22, 2026 — Chevron Corporation (NYSE: CVX) today announced that Energy Forge One LLC, a wholly owned subsidiary, has signed an agreement with Microsoft Corp.
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Chevron signs 20-year power agreement with Microsoft for West Texas data center. HOUSTON, June 22, 2026 — Chevron Corporation (NYSE: CVX) today announced that Energy Forge One LLC, a wholly owned subsidiary, has signed an agreement with Microsoft Corp.
TL;DR:
This positions Kilby among the largest co-located natural gas power and data center developments in the U.S., supporting the next phase of American AI growth by leveraging America’s natural gas advantage.
Towardsdatascience
Jun 22, 13:30
When RAG Users Ask Vague Questions: Clarify Once, Learn the Default
Enterprise Document Intelligence [Vol.1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users Ask Vague Questions: Clarify Once, Learn the Default appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #6bis] – Ask one focused clarification, learn the default from the answer, stay silent next time This article is a companion in Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: parsing, question parsing, retrieval, and generation.
TL;DR:
Enterprise Document Intelligence [Vol.1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users Ask Vague Questions: Clarify Once, Learn the Default appeared first on Towards Data Science .
Machinelearningmastery
Jun 22, 12:00
Building Browser-Using AI Agents in Python
Most AI agent tutorials start with an API.
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Building Browser-Using AI Agents in Python. Most AI agent tutorials start with an API.
TL;DR:
Most AI agent tutorials start with an API.
Kdnuggets
Jun 22, 12:00
3 NLTK Tricks for Advanced Text Preprocessing & Linguistic Analysis
In this article, we will walk through three essential NLTK tricks to elevate your text preprocessing: preserving phrase integrity with the MWETokenizer, context-aware lemmatization with POS mapping, and statistical collocation extraction using association measures.
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Natural language processing (NLP) has undergone an obvious paradigm shift in recent years, with large language models (LLMs) and transformers handling complex end-to-end understanding tasks. However, in any practical NLP workflow, raw text must still be tokenized, normalized, and analyzed before it ever reaches a model.
TL;DR:
In this article, we will walk through three essential NLTK tricks to elevate your text preprocessing: preserving phrase integrity with the MWETokenizer, context-aware lemmatization with POS mapping, and statistical collocation extraction using association measures.
Towardsdatascience
Jun 22, 12:00
Neural Networks, Explained for Beginners: Start Here If They’ve Confused You
The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .
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The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .
TL;DR:
The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .
Mcipetition
Jun 21, 23:34
Petition against Meta's employee training data collection for ML models
We demand that you not collect employee “computer-use” data for the purposes of training AI Models. Recently, leadership announced in a limited audience group (MSL Infra FYI) that a program called "M…
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Petition against Meta's employee training data collection for ML models. When employees asked what privacy reviews were conducted, including any "people data reviews" (which are required for processing employee data), no completed privacy reviews were provided.
TL;DR:
We demand that you not collect employee “computer-use” data for the purposes of training AI Models.
Towardsdatascience
Jun 21, 17:00
Tool Calling, Explained: How AI Agents Decide What to Do Next
Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .
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Understanding ow LLMs interact with the world around them, from returning data to taking action In my latest post , we talked about how to get structured, machine-readable outputs as a response from an LLM, using JSON Mode, function calling, and structured outputs.
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Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .
Towardsdatascience
Jun 21, 15:00
Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section
Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the page-alignment step everyone forgets The post Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section appeared first on Towards Data Science .
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It extends Article 5 (document parsing) on one table: toc_df , the document’s section structure, which Article 5 fills from the PDF’s native outline (PyMuPDF’s doc.get_toc ) when there is one. Article 5 (document parsing) and Article 5B (the relational data model) leaned on doc.get_toc() , the PDF’s native outline, to fill toc_df .
TL;DR:
Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the page-alignment step everyone forgets The post Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section appeared first on Towards Data Science .
Towardsdatascience
Jun 21, 13:00
What Are the Possibilities to Build Date Tables in Self-Service Environments?
For years, I created date tables with DAX code whenever I didn’t have a way to create them upstream of the data flow. Now I've realised there's another way to do it. Let’s see what the alternatives are and how they compare. The post What Are the Possibilities to Build Date Tables in Self-Service Environments? appeared first on Towards Data Science .
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What Are the Possibilities to Build Date Tables in Self-Service Environments?. For years, I created date tables with DAX code whenever I didn’t have a way to create them upstream of the data flow. appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Rerun
Jun 21, 12:57
Robotics Teams Are Rebuilding the Data Stack from Scratch
Search documentation / esc Start typing to search the docs Navigate Open The data layer tax for robot learning Written by Nikolaus West 1 month ago Scaling laws are starting to work for robotics, pro…
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Robotics Teams Are Rebuilding the Data Stack from Scratch. LLM teams scaled on mature data infrastructure to improve performance through fast iteration on data. The data layer for Physical AI is still immature, and the cost is visible at every stage of the pipeline.
TL;DR:
Architecturally, the data layer owns storing, modeling, and accessing data.
Towardsdatascience
Jun 20, 17:00
7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture
What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .
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What data teams need to build with AI to make self-healing data architecture a practical reality Introduction For many data engineers , AI examples of data engineering revolve around one thing: fixing a pipeline. The dream for data teams is a system whereby data pipelines and workflows generally succeed without any human intervention at all.
TL;DR:
What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .
Towardsdatascience
Jun 20, 15:00
Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All
Enterprise Document Intelligence [Vol.1 #5sexies] - image_df tells you where every picture is. Turning the few that matter into searchable text is a separate, cost-ordered job The post Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #5sexies] - image_df tells you where every picture is. Turning the few that matter into searchable text is a separate, cost-ordered job The post Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All appeared first on Towards Data Science .
TL;DR:
Turning the few that matter into searchable text is a separate, cost-ordered job The post Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All appeared first on Towards Data Science .
Towardsdatascience
Jun 20, 13:00
Materialized Lake Views in Microsoft Fabric: When Your Medallion Fits in a SELECT Statement
For the longest time, building a medallion architecture in Microsoft Fabric meant stitching together a small orchestra of moving parts: notebooks for the transformations, pipelines for orchestration,…
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Materialized Lake Views in Microsoft Fabric: When Your Medallion Fits in a SELECT Statement. For the longest time, building a medallion architecture in Microsoft Fabric meant stitching together a small orchestra of moving parts: notebooks for the transformations, pipelines for orchestration, schedules for refresh, custom code for data quality checks, and the Monitor Hub for ke…
TL;DR:
You write a SELECT query that describes the transformation you want, and Fabric takes care of execution, storage, refresh, dependency tracking, and data quality enforcement.
Hex
Jun 20, 00:23
We built a lab to evaluate data agents – Hex
Inside Hex's eval architecture and the synthetic business it runs on. linkedin Data analytics is a uniquely cursed domain for agents to operate in. Easy questions look hard. Hard questions look easy.
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We built a lab to evaluate data agents – Hex. linkedin Data analytics is a uniquely cursed domain for agents to operate in. There is almost no realistic public data to train on or build environments from, and there is a surplus of unrealistic tutorial-slop jamming up the pretrain.
TL;DR:
Everyone’s data warehouse is out of distribution.
Github
Jun 20, 00:04
Cirrus: ATProto Personal Data Server That Runs on Cloudflare Workers
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Cirrus: ATProto Personal Data Server That Runs on Cloudflare Workers. Reload to refresh your session. Dismiss alert {{ message }} ascorbic / cirrus Public Notifications You must be signed in to change notification settings Fork 25 Star 352 main Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit…
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You signed in with another tab or window.
Towardsdatascience
Jun 19, 18:00
Python 3.14 and its New JIT Compiler
A technical overview and some benchmarks The post Python 3.14 and its New JIT Compiler appeared first on Towards Data Science .
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If you want more details on GIL-free Python, I’ll leave a link to my article about it at the end. It’s the result of years of architectural preparation done by the Python core team and others, aimed at making Python “faster by default” without breaking the C-extension ecosystem that powers everything from data science to web backends.
TL;DR:
A technical overview and some benchmarks The post Python 3.14 and its New JIT Compiler appeared first on Towards Data Science .
Towardsdatascience
Jun 19, 16:30
Building a Custom GStreamer Plugin for NVIDIA DeepStream
Why Custom Inference in DeepStream? The post Building a Custom GStreamer Plugin for NVIDIA DeepStream appeared first on Towards Data Science .
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Why Custom Inference in DeepStream? The post Building a Custom GStreamer Plugin for NVIDIA DeepStream appeared first on Towards Data Science .
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The post Building a Custom GStreamer Plugin for NVIDIA DeepStream appeared first on Towards Data Science .
Towardsdatascience
Jun 19, 15:00
I Tried to Schedule My ETL Pipeline. Here’s What I Didn’t Expect.
What I thought was a scheduling problem turned out to be a portability problem first The post I Tried to Schedule My ETL Pipeline. Here’s What I Didn’t Expect. appeared first on Towards Data Science .
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I Tried to Schedule My ETL Pipeline. What I thought was a scheduling problem turned out to be a portability problem first The post I Tried to Schedule My ETL Pipeline. appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Kdnuggets
Jun 19, 14:00
Loss Function Explained For Noobs (How Models Know They Are Wrong)
This is a simple guide to understanding loss functions in machine learning and how models learn from their mistakes.
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I know that when beginners start learning machine learning, things seem easy at first. A loss function is how a machine learning model knows how wrong it is. The model makes a prediction.
TL;DR:
This is a simple guide to understanding loss functions in machine learning and how models learn from their mistakes.
Towardsdatascience
Jun 19, 13:30
Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document
Enterprise Document Intelligence [Vol.1 #5quinquies] - Same 1974 scanned PDF, two engines. EasyOCR recovers text. Docling recovers text + sections + figures. The structural gap makes one output usable downstream and the other one a flat string. The post Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #5quinquies] - Same 1974 scanned PDF, two engines. EasyOCR recovers text. Docling recovers text + sections + figures.
TL;DR:
The post Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document appeared first on Towards Data Science .
Kdnuggets
Jun 19, 12:00
Practical SQL Tricks Every Data Scientist Should Know
In this article, we’ll cover essential SQL patterns and workflows that make everyday data analysis cleaner, faster, and easier to scale.
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Focusing only on SELECT , WHERE , and GROUP BY is enough for basic aggregation, but many real analytical tasks require patterns that go beyond simple queries. Examples include detecting consecutive activity streaks, segmenting customers by spend tier, smoothing noisy time-series data, or tracing plan upgrade paths across rows.
TL;DR:
In this article, we’ll cover essential SQL patterns and workflows that make everyday data analysis cleaner, faster, and easier to scale.
Kdnuggets
Jun 19, 12:00
Python Dictionary Tips and Tricks You Should Always Remember
Master these tips, and your dictionary code will become shorter, safer, and easier to read. Dictionaries in Python are useful for everything from configs, JSON data, to API responses.
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Dictionaries in Python are useful for everything from configs, JSON data, to API responses. Most beginners only learn the basics, like creating a dictionary, accessing a key, and updating a value. config = {"debug": True, "verbose": False} print(config.get("timeout", 30)) This will print 30 , which is the default value we set.
TL;DR:
Master these tips, and your dictionary code will become shorter, safer, and easier to read.
Github
Jun 19, 07:37
Show HN: Write SaaS apps where users control where their data is stored
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Reload to refresh your session. Reload to refresh your session. Reload to refresh your session.
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You signed in with another tab or window.
Towardsdatascience
Jun 18, 18:00
Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
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The user asks a question, the model responds in natural language, and we just display that response to the user in some way. But what happens when we need the model to return data in a specific format (e.g., a JSON object) so that we can further process it programmatically later on?
TL;DR:
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
Towardsdatascience
Jun 18, 16:30
How Powerful is Claude Fable (Mythos) 5 for Coding?
Learn about the upsides and downsides of Claude Fable 5 The post How Powerful is Claude Fable (Mythos) 5 for Coding? appeared first on Towards Data Science .
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How Powerful is Claude Fable (Mythos) 5 for Coding?. Learn about the upsides and downsides of Claude Fable 5 The post How Powerful is Claude Fable (Mythos) 5 for Coding? appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
Jun 18, 15:00
Proteins: A Mosaic Pattern to Rule Them All?
For decades, the existence of the hydrophobic core, a region in the 3D structure of proteins where hydrophobic amino acids reside together, has been considered a general property in proteins. What we have found now may extend that model. In particular, the rest of amino acids also seem to cluster together according to their chemical type (polar, acidic, basic, special), specifically in groups of ~8 units. This is what we have come to call the Mosaic Q model. Here is how we found it, along with tools for its quantification and visualization. The post Proteins: A Mosaic Pattern to Rule Them All? appeared first on Towards Data Science .
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Proteins: A Mosaic Pattern to Rule Them All?. The post Proteins: A Mosaic Pattern to Rule Them All? appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
Jun 18, 13:30
Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit
Enterprise Document Intelligence [Vol.1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
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Article 6_b (extraction) walked the five families of columns the parser reads straight from the user string. Question parsing on its own returns keywords=["name"] and retrieval looks for the literal word name in the file.
TL;DR:
Enterprise Document Intelligence [Vol.1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
Machinelearningmastery
Jun 18, 12:00
The Roadmap to Mastering AI Agent Evaluation
Share Post Share In this article, you will learn how to evaluate AI agents rigorously by examining their full execution process rather than only their final outputs.
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The Roadmap to Mastering AI Agent Evaluation. Agent evaluation addresses this gap. The principles covered in this article form the foundation of a systematic approach to measuring and improving agent performance.
TL;DR:
Share Post Share In this article, you will learn how to evaluate AI agents rigorously by examining their full execution process rather than only their final outputs.
Towardsdatascience
Jun 18, 12:00
The Power and Pitfalls of Vector-Based Image Search
A hands-on guide to setting up image similarity search in Milvus, and why visual replication isn't always enough. The post The Power and Pitfalls of Vector-Based Image Search appeared first on Towards Data Science .
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The Power and Pitfalls of Vector-Based Image Search. A hands-on guide to setting up image similarity search in Milvus, and why visual replication isn't always enough. The post The Power and Pitfalls of Vector-Based Image Search appeared first on Towards Data Science .
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The post The Power and Pitfalls of Vector-Based Image Search appeared first on Towards Data Science .
Towardsdatascience
Jun 17, 16:30
Your Churn Threshold Is a Pricing Decision
How unit economics should set your classification cutoff, and why they rarely do. The post Your Churn Threshold Is a Pricing Decision appeared first on Towards Data Science .
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Your Churn Threshold Is a Pricing Decision. How unit economics should set your classification cutoff, and why they rarely do. The post Your Churn Threshold Is a Pricing Decision appeared first on Towards Data Science .
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The post Your Churn Threshold Is a Pricing Decision appeared first on Towards Data Science .
Towardsdatascience
Jun 17, 15:00
The Secret to Reproducible and Portable Optimization: ORPilot’s Intermediate Representation (IR)
Why production-level AI optimization modeling agent needs reproducibility and portability, and how IR helps achieve them The post The Secret to Reproducible and Portable Optimization: ORPilot’s Intermediate Representation (IR) appeared first on Towards Data Science .
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Why production-level AI optimization modeling agent needs reproducibility and portability, and how IR helps achieve them In my previous post , I walked through ORPilot’s four core innovations that makes ORPilot a production-oriented open-source LLM-for-OR tool, namely interview agent, data collection agent, parameter computation agent and intermediate representation (IR).
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Why production-level AI optimization modeling agent needs reproducibility and portability, and how IR helps achieve them The post The Secret to Reproducible and Portable Optimization: ORPilot’s Intermediate Representation (IR) appeared first on Towards Data Science .
Kdnuggets
Jun 17, 14:00
How (and Why) I Built an AI Assistant
This article is an honest account of the process on why I built a custom AI assistant instead of just paying for one, what the architecture looks like, the actual code, what broke, and what it does now that I genuinely rely on.
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That evening, instead of closing my laptop and calling it a loss, I started thinking about the problem differently. Most people who decide to build an AI assistant start by Googling "Python LangChain tutorial." That's backwards. The first question worth sitting with is: why build it at all when Siri, ChatGPT, Copilot, and a dozen other tools already exist?
TL;DR:
This article is an honest account of the process on why I built a custom AI assistant instead of just paying for one, what the architecture looks like, the actual code, what broke, and what it does now that I genuinely rely on.
Towardsdatascience
Jun 17, 13:30
You Probably Don’t Need an Agent Framework
Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python. The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science .
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Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python. The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science .
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The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science .
Kdnuggets
Jun 17, 12:00
5 Fun Projects Using OpenAI Codex
Learn Codex by building small and practical projects step by step. OpenAI Codex is one of the most useful tools for building software with AI.
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OpenAI Codex is one of the most useful tools for building software with AI. If you are new to Codex, start with this tutorial first: OpenAI Codex Full Tutorial in 2026 | Using Codex from Scratch . It shows how to build a simple app step by step using Codex.
TL;DR:
Learn Codex by building small and practical projects step by step.
Towardsdatascience
Jun 17, 12:00
What the Question Parser Extracts from a User String: Keywords, Scope, Shape, Decomposition, Clarification
Enterprise Document Intelligence [Vol.1 #6b] - The five field families the parser reads straight from the user’s question, with the code that fills each one The post What the Question Parser Extracts from a User String: Keywords, Scope, Shape, Decomposition, Clarification appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #6b] – The five field families the parser reads straight from the user’s question, with the code that fills each one This article is the second part of the question-parsing brick of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: parsing, question parsing, retrieval, and generation.
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Enterprise Document Intelligence [Vol.1 #6b] - The five field families the parser reads straight from the user’s question, with the code that fills each one The post What the Question Parser Extracts from a User String: Keywords, Scope, Shape, Decomposition, Clarification appeared first on Towards Data Science .
Mattmahoney
Jun 16, 21:51
Data Compression Explained
Copyright (C) 2010-2012, Dell, Inc . You are permitted to copy and distribute material from this book provided (1) any material you distribute includes this license, (2) the material is not modified,…
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Data Compression Explained. This book is for the reader who wants to understand how data compression works, or who wants to write data compression software. Losslessly compressed data can be decompressed to exactly its original value.
TL;DR:
Data compression is the art of reducing the number of bits needed to store or transmit data.
Databricks
Jun 16, 19:45
Databricks Launches LTAP: A Unified OLAP/OLTP Data Architecture
DATA + AI SUMMIT – June 16, 2026 – Databricks , the Data and AI company, today introduced Lake Transactional/Analytical Processing (LTAP), a new data processing architecture that unifies transactions…
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Databricks Launches LTAP: A Unified OLAP/OLTP Data Architecture. Powered by major advances in Lakebase, LTAP provides a new data foundation for the AI application era. The data industry has tried to solve the problem of disparate systems before.
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DATA + AI SUMMIT – June 16, 2026 – Databricks , the Data and AI company, today introduced Lake Transactional/Analytical Processing (LTAP), a new data processing architecture that unifies transactions, analytics, streaming, and operational data on a single copy of storage in the lake.
Towardsdatascience
Jun 16, 16:30
Drilling Into AI’s Financial Sustainability
Budgets for AI tokens can’t be infinite, no matter how much hyperscalers wish they were The post Drilling Into AI’s Financial Sustainability appeared first on Towards Data Science .
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Budgets for AI tokens can’t be infinite, no matter how much hyperscalers wish they were In my April column , I talked about how the opaqueness of the true cost of AI is a potentially fatal flaw for the profitable commercialization of the technology long term. It feels like the winds in the AI industry are changing direction so fast that it’s difficult to keep track.
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Budgets for AI tokens can’t be infinite, no matter how much hyperscalers wish they were The post Drilling Into AI’s Financial Sustainability appeared first on Towards Data Science .
Towardsdatascience
Jun 16, 15:00
Run a Local LLM with OpenClaw on Your Mac Mini
Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches. The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science .
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Run a Local LLM with OpenClaw on Your Mac Mini. Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches. The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science .
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The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science .
Kdnuggets
Jun 16, 14:00
The Roadmap to Becoming an LLM Engineer in 2026
A step-by-step path through the skills that turn a machine learning practitioner into someone who ships large language model applications.
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An LLM engineer is not the same thing as a general machine learning engineer. Where a machine learning engineer might spend months training a neural network from scratch, an LLM engineer's work centers on adapting, orchestrating, and serving pretrained large language models (LLMs).
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A step-by-step path through the skills that turn a machine learning practitioner into someone who ships large language model applications.
Towardsdatascience
Jun 16, 13:30
LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity during provider swaps. The post LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer appeared first on Towards Data Science .
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LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity during provider swaps.
TL;DR:
The post LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer appeared first on Towards Data Science .
Machinelearningmastery
Jun 16, 12:00
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.
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Share Post Share In this article, you will learn how to build an end-to-end sentiment analysis pipeline using Scikit-LLM and open-source large language models served through the Groq API. With the rise of large language models (LLMs), the rules of the game have somewhat changed: it is now possible to leverage zero-shot or few-shot reasoning on existing, pre-trained models for…
TL;DR:
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.
Kdnuggets
Jun 16, 12:00
Stop Writing Loops in Pandas: 7 Faster Alternatives to Try
In this article, you will learn how to replace pandas loops with 7 faster methods for optimized data processing.
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In this article, you will learn how to replace pandas loops with 7 faster methods for optimized data processing. Row-by-row iteration is one of the most common performance bottlenecks in pandas code. Looping through rows in Python bypasses that entirely and forces every operation back into the Python interpreter — one row at a time.
TL;DR:
In this article, you will learn how to replace pandas loops with 7 faster methods for optimized data processing.
Towardsdatascience
Jun 16, 12:00
RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation
Enterprise Document Intelligence [Vol.1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs The post RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #6a] – Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs T his article is the question-parsing brick of Enterprise Document Intelligence , a series that builds an enterprise RAG system from four bricks: parsing, question parsing, retrieval, an…
TL;DR:
Enterprise Document Intelligence [Vol.1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs The post RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation appeared first on Towards Data Science .
Narracomm
Jun 16, 00:28
Amazon Announces Multibillion-Dollar Data Center in Missouri
Amazon Web Services is making a major push into Missouri with a multibillion-dollar investment in a new data center campus in Montgomery County, the company announced today.
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Amazon Announces Multibillion-Dollar Data Center in Missouri. The project is expected to create more than 400 full-time data center jobs, along with thousands of construction roles during the build-out. On the water front, the facilities will emphasize efficiency with features including: Amazon projects the site will use less than 0.
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Amazon Web Services is making a major push into Missouri with a multibillion-dollar investment in a new data center campus in Montgomery County, the company announced today.
Gizmodo
Jun 15, 20:06
US Government Reportedly Allowing Federal Data Center Rules to Expire
The AI boom continues to drive new data center projects (and subsequent public outcries) across the country, while a law that sets standards for how federal agencies build, use, and operate data cent…
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US Government Reportedly Allowing Federal Data Center Rules to Expire. government is set to allow the Federal Data Center Enhancement Act (FDCEA) to expire without any clear plan to renew or replace it. The law set standards for subjects like cybersecurity and sustainability for federally operated (and some contractor-operated) data centers.
TL;DR:
The AI boom continues to drive new data center projects (and subsequent public outcries) across the country, while a law that sets standards for how federal agencies build, use, and operate data centers is about to expire this year.
Exclav
Jun 15, 19:31
Flip TABLE: storing arbitrary data in iNaturalist
A few weeks ago, my friend Marcos ran an event called FLIP TABLE , celebrating unconventional database technology, including Strava, steganography, encoded number puzzles, and hair.
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Flip TABLE: storing arbitrary data in iNaturalist. An iNaturalist “classic project” can store an arbitrary number of observations, each with their own unique ID. The problem therefore became implementing a method of storing arbitrary data in an unordered set of integers.
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The higher sequence bits would allow the values to be ordered correctly when decoding data.
Roszigit
Jun 15, 17:29
How TimescaleDB compresses time-series data
TimescaleDB can achieve compression of up to 98% for typical time-series data. Compressing time-series data requires a fundamentally different approach than the general-purpose algorithms used in OLT…
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How TimescaleDB compresses time-series data. Compressing time-series data requires a fundamentally different approach than the general-purpose algorithms used in OLTP databases. the columns TOAST does not compress at all — TimescaleDB reaches a ratio of 10-100×, because it is built for this type of data.
TL;DR:
TimescaleDB can achieve compression of up to 98% for typical time-series data.
Towardsdatascience
Jun 15, 16:30
How to Effectively Align with Claude Code
Increase productivity with your LLMs The post How to Effectively Align with Claude Code appeared first on Towards Data Science .
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Coding agents are amazing at making quick implementations. However, now that coding has become a commodity, one of the main bottlenecks that I see is the knowledge transfer between a human brain and the coding agent. However, there are always a lot of nuances that are hard to cover in such a way when describing it to the coding agent.
TL;DR:
Increase productivity with your LLMs The post How to Effectively Align with Claude Code appeared first on Towards Data Science .
Towardsdatascience
Jun 15, 15:00
The Protocol That Cleaned Up Our Agent Architecture
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science .
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A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server A few weeks ago someone from the data team asked whether we could update the database schema which was being populated by one of the tools of our complex agentic system. The tool definition lived in the agent orchestrator.
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A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science .
Kdnuggets
Jun 15, 14:00
Building Time-Series Machine Learning Models with sktime in Python
In this article, we’ll build time-series machine learning models in Python using sktime and explore its core data structures for forecasting workflows.
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If you work with sensor readings, server metrics, or any data that arrives over time, you already know that standard scikit-learn pipelines don't quite fit. Time series data has structure that tabular models ignore: seasonality, trend, temporal ordering, and the fact that future values depend on past ones.
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In this article, we’ll build time-series machine learning models in Python using sktime and explore its core data structures for forecasting workflows.
Towardsdatascience
Jun 15, 13:30
I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.
A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it. The post I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions. appeared first on Towards Data Science .
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I Built 11 Models to Predict the 2026 World Cup. The post I Built 11 Models to Predict the 2026 World Cup. appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Machinelearningmastery
Jun 15, 12:00
AI Agent Tool Design: What Works and What Doesn’t
Share Post Share In this article, you will learn how tool design — not model capability — is the root cause of most AI agent failures, and what concrete design patterns you can apply to fix it.
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AI Agent Tool Design: What Works and What Doesn’t. Most AI agent failures look like model mistakes: choosing the wrong tool, passing bad arguments, or mishandling errors. This article covers: Each pattern is paired with its failure counterpart, because understanding why a design fails is as important as knowing what to replace it with.
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Share Post Share In this article, you will learn how tool design — not model capability — is the root cause of most AI agent failures, and what concrete design patterns you can apply to fix it.
Kdnuggets
Jun 15, 12:00
3 Pandas Tricks for Data Cleaning & Preparation
In this article, we will walk through three essential Pandas tricks to clean and prepare your data efficiently: declarative method chaining, memory and speed optimization via categoricals and vectorized string accessors, and group-aware imputation using .transform().
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Data cleaning and preparation are estimated to occupy up to 80% of a data scientist's daily workflow. Because Pandas is the standard data manipulation library in Python, the efficiency of your operations directly dictates how quickly you can move from raw, dirty datasets to model-ready features.
TL;DR:
In this article, we will walk through three essential Pandas tricks to clean and prepare your data efficiently: declarative method chaining, memory and speed optimization via categoricals and vectorized string accessors, and group-aware imputation using .transform().
Towardsdatascience
Jun 15, 12:00
The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem
How local optimization in last‑mile delivery can quietly break the system The post The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem appeared first on Towards Data Science .
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How local optimization in last‑mile delivery can quietly break the system In 1968, the mathematician Dietrich Braess described a result that still feels wrong the first time you hear it: adding a road to a traffic network can make everyone’s commute worse. The road can work exactly as intended, and the system can still get worse.
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How local optimization in last‑mile delivery can quietly break the system The post The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem appeared first on Towards Data Science .
Towardsdatascience
Jun 14, 17:00
4 Lines You Should Include in Your Claude Skill
Without these, Claude will be confidently wrong. The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .
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Without these, Claude will be confidently wrong. The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .
TL;DR:
The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .
Towardsdatascience
Jun 14, 15:00
Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures The post Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures The post Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG appeared first on Towards Data Science .
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A vision model also reads the pictures The post Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG appeared first on Towards Data Science .
Towardsdatascience
Jun 14, 13:00
GPU Time-Slicing for Concurrent LLM Agents on Kubernetes
A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .
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GPU Time-Slicing for Concurrent LLM Agents on Kubernetes. A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .
TL;DR:
The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .
Towardsdatascience
Jun 13, 17:00
Larger Context Windows Don’t Fix RAG — So I Built a System That Does
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely. The post Larger Context Windows Don’t Fix RAG — So I Built a System That Does appeared first on Towards Data Science .
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Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely.
TL;DR:
The post Larger Context Windows Don’t Fix RAG — So I Built a System That Does appeared first on Towards Data Science .
Towardsdatascience
Jun 13, 15:00
Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building The post Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload appeared first on Towards Data Science .
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Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload. Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine.
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No key, no per-page bill, nothing leaves the building The post Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload appeared first on Towards Data Science .
Desfontain
Jun 13, 13:54
US bans differential privacy in Census data
Last week , the United States Department of Commerce issued an order declaring that "noise infusion" will be banned from all statistical products published by the Census Bureau and the Bureau of Econ…
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US bans differential privacy in Census data. Census Bureau primarily relied on swapping for the decennial census. So they tried a few alternative approaches, and decided to adopt differential privacy for the 2020 Census: this was the one that kept the statistics most useful, while preventing these attacks.
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Scientists have developed a number of techniques that can be used to publish useful statistics while protecting the privacy of the original data.
Towardsdatascience
Jun 13, 13:00
Solving the 3Blue1Brown String Probability Problem (Without AI)
Let's practice data science thinking through a probability problem The post Solving the 3Blue1Brown String Probability Problem (Without AI) appeared first on Towards Data Science .
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Let's practice data science thinking through a probability problem Why did I go through the trouble of solving a silly probability problem in my free time when I could’ve been doom scrolling? We randomly select the end of one string and then randomly select the end of another string.
TL;DR:
Let's practice data science thinking through a probability problem The post Solving the 3Blue1Brown String Probability Problem (Without AI) appeared first on Towards Data Science .
Towardsdatascience
Jun 12, 18:00
When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout
Enterprise Document Intelligence [Vol.1 #5bis] - The same relational tables. Native table cells. OCR for scanned pages and images. Captions and headings without regex. The post When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol.1 #5bis] - The same relational tables. Native table cells. OCR for scanned pages and images. Captions and headings without regex. The post When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout appeared first on Towards Data Science .
TL;DR:
The post When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout appeared first on Towards Data Science .
Bitboard
Jun 12, 16:58
Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents
Connect your data, build with your agent, share with your team. Generate dashboards and analysis in BitBoard from your favorite AI chat or coding agent.
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Know exactly where your data came from and can rerun with consistent logic, even if the logic was AI-generated. Give BitBoard direct access to your data sources for live connections or push data from your agent to leverage existing connections with minimal setup. Use AI for data analysis without losing logic and context in your chat threads.
TL;DR:
Connect your data, build with your agent, share with your team.
Towardsdatascience
Jun 12, 16:30
Why Decade-Old Residual Connections Still Power All of AI (And Why That’s a Problem)
For nearly a decade, this part of neural networks barely changed. DeepSeek is trying to reinvent it. The post Why Decade-Old Residual Connections Still Power All of AI (And Why That’s a Problem) appeared first on Towards Data Science .
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For nearly a decade, this part of neural networks barely changed. DeepSeek is trying to reinvent it. The post Why Decade-Old Residual Connections Still Power All of AI (And Why That’s a Problem) appeared first on Towards Data Science .
TL;DR:
The post Why Decade-Old Residual Connections Still Power All of AI (And Why That’s a Problem) appeared first on Towards Data Science .
Towardsdatascience
Jun 12, 15:00
A Harness for Every Task: Putting a Team of Claudes on One Job
Claude can now write its own harness on the fly, custom-built for the task at hand. The post A Harness for Every Task: Putting a Team of Claudes on One Job appeared first on Towards Data Science .
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A Harness for Every Task: Putting a Team of Claudes on One Job. Claude can now write its own harness on the fly, custom-built for the task at hand. The post A Harness for Every Task: Putting a Team of Claudes on One Job appeared first on Towards Data Science .
TL;DR:
The post A Harness for Every Task: Putting a Team of Claudes on One Job appeared first on Towards Data Science .
Kdnuggets
Jun 12, 14:00
Pairing Claude Code with Local Models
Local models in 2026 are good enough. For the tasks Claude Code handles daily: code completion, refactoring, debugging, codebase explanation; a well-chosen quantized model running locally covers the vast majority of real use cases at zero per-token cost and with no rate limits.
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Pairing Claude Code with Local Models. Local models in 2026 are good enough. For the tasks Claude Code handles daily: code completion, refactoring, debugging, codebase explanation; a well-chosen quantized model running locally covers the vast majority of real use cases at zero per-token cost and with no rate limits.
TL;DR:
Local models in 2026 are good enough.
Towardsdatascience
Jun 12, 13:30
I Thought Data Engineering Was Just Writing Scripts. I Was Wrong.
I tried to make my ETL pipeline production-ready. Three things broke. Each one taught me something scripting alone never could. The post I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. appeared first on Towards Data Science .
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I Thought Data Engineering Was Just Writing Scripts. The post I Thought Data Engineering Was Just Writing Scripts. appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Machinelearningmastery
Jun 12, 12:00
Python Concepts Every AI Engineer Must Master
Transitioning from writing local experimental scripts to building scalable, production-grade AI systems requires a shift in how we write Python.
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Share Post Share In this article, you will learn five essential Python concepts that every AI engineer must master to build scalable, production-grade AI systems. While dynamic typing, basic loops, and list comprehensions are reasonable for prototyping models or exploring data, they fail to meet the performance, memory, and latency constraints of real-world AI applications.
TL;DR:
Transitioning from writing local experimental scripts to building scalable, production-grade AI systems requires a shift in how we write Python.
Kdnuggets
Jun 12, 12:00
3 NumPy Tricks for Numerical Performance
In this article, we will cover three essential NumPy tricks to optimize your code: vectorization and broadcasting, in-place operations, and leveraging memory views instead of copies.
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The Python scientific computing and machine learning ecosystem relies heavily on NumPy . Unfortunately, many data scientists and developers write NumPy code that fails to leverage this power. Iterating over a data structure element-by-element forces the Python interpreter to perform type checking and method lookups at every single step.
TL;DR:
In this article, we will cover three essential NumPy tricks to optimize your code: vectorization and broadcasting, in-place operations, and leveraging memory views instead of copies.
Towardsdatascience
Jun 12, 12:00
Is Language Visual? An Experiment with Chinese Characters
A story about a broken printer, visual inductive bias, and why the race endedin a tie. The post Is Language Visual? An Experiment with Chinese Characters appeared first on Towards Data Science .
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A story about a broken printer, visual inductive bias, and why the race endedin a tie. The post Is Language Visual? An Experiment with Chinese Characters appeared first on Towards Data Science .
TL;DR:
An Experiment with Chinese Characters appeared first on Towards Data Science .
Towardsdatascience
Jun 11, 18:00
BI Is Dead, Long Live BI
The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science .
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The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science .
TL;DR:
The post BI Is Dead, Long Live BI appeared first on Towards Data Science .
Towardsdatascience
Jun 11, 16:30
Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs
Enterprise Document Intelligence [Vol.1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs appeared first on Towards Data Science .
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The previous part turned a PDF into line_df , one row per line of text on the page. RAG tutorials start the same way: text = extract_text(pdf) . You build a RAG pipeline.
TL;DR:
Enterprise Document Intelligence [Vol.1 #5B] - One PDF in, a relational set of DataFrames out: lines, pages, TOC, images, cross-references, captions, spans, and a parsing summary The post Stop Returning Flat Text from a PDF: The Relational Shape RAG Needs appeared first on Towards Data Science .
Towardsdatascience
Jun 11, 15:00
PySpark for Beginners: Beyond the Basics
Take the next step to building real workflows with Spark on your laptop The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science .
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Take the next step to building real workflows with Spark on your laptop If you’ve read my first article in this series, PySpark for Beginners: Mastering the Basics then you already understand the heart of Spark: distributed data, DataFrames, and lazy execution.
TL;DR:
Take the next step to building real workflows with Spark on your laptop The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science .
Kdnuggets
Jun 11, 14:00
Feature Stores from Scratch: A Minimal Working Implementation
Build the five components every feature store needs, then see where AI changes the design. Most teams discover they need a feature store the hard way.
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Large language model (LLM) agents and retrieval-augmented generation (RAG) pipelines need structured user context at inference time, on every request, in under 10ms. An LLM has no memory of who the user is. That is exactly what a feature store's online store and retrieval API give us.
TL;DR:
Build the five components every feature store needs, then see where AI changes the design.
Towardsdatascience
Jun 11, 13:30
When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI
Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science .
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The scheduler still treated those nodes as “healthy enough” because GPU and memory metrics looked acceptable. In simple words, one of the storage drives on those machines had failed or become unreliable, and the server was busy rebuilding the lost data across the remaining drives. The machines were technically still online.
TL;DR:
Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science .
Machinelearningmastery
Jun 11, 12:00
Multi-Label Text Classification with Scikit-LLM
Text classification typically boils down to scenarios where a product review is "positive" or "negative", or a customer inquiry belongs to one category or another.
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Share Post Share In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training.
TL;DR:
Text classification typically boils down to scenarios where a product review is "positive" or "negative", or a customer inquiry belongs to one category or another.
Towardsdatascience
Jun 11, 12:00
NuCS vs Choco: A Pure-Python Constraint Solver Meets a JVM Veteran
An in-depth performance test comparing Nucs and Choco The post NuCS vs Choco: A Pure-Python Constraint Solver Meets a JVM Veteran appeared first on Towards Data Science .
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TL;DR NuCS is a constraint solver written 100% in Python, developed by me, accelerated by NumPy and Numba . When both solvers run the same model they are, for all practical purposes, the same speed — and on the largest instances NuCS actually pulls ahead , because once Numba has compiled the inner loops the Python tax is gone and only the cost-per-node remains.
TL;DR:
An in-depth performance test comparing Nucs and Choco The post NuCS vs Choco: A Pure-Python Constraint Solver Meets a JVM Veteran appeared first on Towards Data Science .
Tomshardware
Jun 10, 19:06
Farmer donates land for a park, city sells it for $10M as data center land
Tom's Hardware Premium equips you with world-class coverage and detailed insights into the evolving hardware landscape.
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Farmer donates land for a park, city sells it for $10M as data center land. Tom's Hardware Premium equips you with world-class coverage and detailed insights into the evolving hardware landscape. Go beyond the headlines with expert reporting on the hardware industry.
TL;DR:
Bench Performance Database Dive into our proprietary testing data and compare hardware with detailed benchmarks.
Towardsdatascience
Jun 10, 18:00
How to Refactor Code with Claude Code
Improve coding agent productiveness with refactored code The post How to Refactor Code with Claude Code appeared first on Towards Data Science .
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Claude Code and other coding agents are amazing at quickly implementing a lot of code. Maybe you’ve spent a few days writing code with AI. In this article, I discuss how to know when you need to refactor your code, what the signs are, and how to do it effectively using Claude Code or other coding agents.
TL;DR:
Improve coding agent productiveness with refactored code The post How to Refactor Code with Claude Code appeared first on Towards Data Science .
Techcrunch
Jun 10, 17:18
Meta steals a tactic from Tesla and builds data centers in tents
Meta steals a tactic from Tesla and builds data centers in tents Tim De Chant 12:33 PM PDT · June 4, 2026 Just when you thought the AI data center boom couldn’t get any crazier, Meta has gone and bui…
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Meta steals a tactic from Tesla and builds data centers in tents. Meta CEO Mark Zuckerberg spoke to The Information last year about his plan to use weatherproof tents to house the company’s multi-gigawatt data centers. The satellite images he shared in his post on X show the structures have all been built.
TL;DR:
Meta steals a tactic from Tesla and builds data centers in tents Tim De Chant 12:33 PM PDT · June 4, 2026 Just when you thought the AI data center boom couldn’t get any crazier, Meta has gone and built data centers in tents.
Towardsdatascience
Jun 10, 16:30
How to Train a Scoring Model in the Age of Artificial Intelligence
A structured methodology for comparing candidate models, testing stability, and selecting a robust final score The post How to Train a Scoring Model in the Age of Artificial Intelligence appeared first on Towards Data Science .
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A few well-structured prompts can now help a data scientist write Python scripts, estimate logistic regressions, compute AUC and Gini, generate plots, and document the results. This article is part of a broader series on building robust, interpretable, and stable scoring models.
TL;DR:
A structured methodology for comparing candidate models, testing stability, and selecting a robust final score The post How to Train a Scoring Model in the Age of Artificial Intelligence appeared first on Towards Data Science .
Towardsdatascience
Jun 10, 15:00
Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality
Enterprise Document Intelligence [Vol.1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science .
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Read the document the way a human would before answering a question about it. An expert was asked a question about a document they had never opened. The next article (5_B) covers the second: knowing the content precisely through a relational base where every line, span, image, and TOC entry becomes one row keyed by page and position.
TL;DR:
Enterprise Document Intelligence [Vol.1 #5A] - Document signals (metadata, native TOC, source software) and page-level content (text vs scans, tables, images, columns, page profile) The post Beyond extract_text: The Two Layers of a PDF That Drive RAG Quality appeared first on Towards Data Science .
Kdnuggets
Jun 10, 14:00
Local Agentic Programming on the Cheap: Claude Code + Ollama + Gemma4
This article builds a full local agentic programming stack using Ollama, Gemma 4, and Claude Code. Visualize this: a multi-agent workflow that reads files, writes patches, runs tests, and iterates…
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It scores 77.1% on LiveCodeBench v6 and 86.4% on τ2-bench agentic tool use — the benchmark that specifically tests what happens when a model has to call tools, execute steps, and handle errors across a multi-step workflow.
TL;DR:
This article builds a full local agentic programming stack using Ollama, Gemma 4, and Claude Code.
Towardsdatascience
Jun 10, 13:30
Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty
An intuitive introduction to reasoning with uncertainty, from directed Bayesian networks to undirected Markov networks and weighted logical rules. The post Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty appeared first on Towards Data Science .
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An intuitive introduction to reasoning with uncertainty, from directed Bayesian networks to undirected Markov networks and weighted logical rules. The post Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty appeared first on Towards Data Science .
TL;DR:
The post Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty appeared first on Towards Data Science .
Kdnuggets
Jun 10, 12:00
5 Useful Python Scripts to Automate Boring PDF Tasks
PDFs are used everywhere, and these five Python scripts help you automate the most common PDF tasks. PDF files are widely used in many workflows.
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PDF files are widely used in many workflows. Combining multiple PDF files into one, or splitting a large PDF into separate files by page range, are among the most common PDF tasks. Metadata from the first input file is preserved in merge mode.
TL;DR:
PDFs are used everywhere, and these five Python scripts help you automate the most common PDF tasks.
Towardsdatascience
Jun 10, 12:00
Physical AI: What It Is and What It Is Not
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science .
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A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins NVIDIA is talking about it, consulting firms are talking about it, and so are the investors and robotics startups. They are all talking about Physical AI. A physical AI is different than that.
TL;DR:
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science .
Machinelearningmastery
Jun 10, 11:35
Multimodal Browser AI with Transformers.js for Images and Speech
Most browser AI tutorials cover text because it is a natural starting point, but the applications people actually want to build are rarely text-only.
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Share Post Share In this article, you will learn how to build multimodal AI capabilities — image classification, image captioning, and speech transcription — that run entirely in the browser using Transformers.js, with no server, no API key, and no data leaving the user’s device. The data is multimodal and the AI should be too.
TL;DR:
Most browser AI tutorials cover text because it is a natural starting point, but the applications people actually want to build are rarely text-only.
Support
Jun 9, 17:23
Anthropic requires 30 day data retention for Fable and Mythos
To ensure we’re responsibly deploying Mythos-class models, we are requiring limited data retention and review as part of our safety work.
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Anthropic requires 30 day data retention for Fable and Mythos. To ensure we’re responsibly deploying Mythos-class models, we are requiring limited data retention and review as part of our safety work. This applies to Mythos-class models and future models with similar capabilities that we designate as covered models .
TL;DR:
Learn more about how we retain data for consumer plans.
Towardsdatascience
Jun 9, 16:30
10 Common RAG Mistakes We Keep Seeing in Production
Enterprise Document Intelligence [Vol.1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science .
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This pitfalls article lists the failure modes we both kept seeing on production RAG systems, and that pushed us toward the four-brick contract in the first place. One PDF, one question, send, read the answer. In enterprise work the question is almost never about one document.
TL;DR:
Enterprise Document Intelligence [Vol.1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science .
Towardsdatascience
Jun 9, 15:00
The Hardware That Makes AI Possible
When we talk about AI, we often describe it as a software revolution, which it is! From breakthroughs in neural networks and transformers to large language models, it is easy to assume that these sma…
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When we talk about AI, we often describe it as a software revolution, which it is! But as AI models grew larger and more computationally demanding, new hardware architectures were needed to run these models. In this article, we will explore the hardware that powers modern AI and explain why different processors are needed for different tasks.
TL;DR:
CPUs, GPUs, TPUs, and NPUs The post The Hardware That Makes AI Possible appeared first on Towards Data Science .
Kdnuggets
Jun 9, 14:00
Best Free Image Generators on Hugging Face Right Now!
This article cuts through the 90,000 options to the seven models worth your time in 2026.
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Best Free Image Generators on Hugging Face Right Now!. This article cuts through the 90,000 options to the seven models worth your time in 2026.
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This article cuts through the 90,000 options to the seven models worth your time in 2026.
Towardsdatascience
Jun 9, 13:30
Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines
Stop re-computing the same context. Learn how to build a C++ runtime with copy-on-fork KV snapshots to eliminate redundant LLM prefills in multi-agent pipelines. The post Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines appeared first on Towards Data Science .
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Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines. Learn how to build a C++ runtime with copy-on-fork KV snapshots to eliminate redundant LLM prefills in multi-agent pipelines. The post Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines appeared first on Towards Data Science .
TL;DR:
The post Prefill Once, Fan Out: KV Snapshot Sharing for Multi-Agent LLM Pipelines appeared first on Towards Data Science .
Kdnuggets
Jun 9, 12:00
10 GitHub Repositories for Web Development in Python
Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces.
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Python is no longer just for scripting, automation, and data science. Today, there are newer frameworks that make Python useful not only for backend development but also for building interactive frontends, data apps, visualizations, and simple web interfaces without needing a complex JavaScript setup.
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Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces.
Towardsdatascience
Jun 9, 12:00
The Exact ML Project I’d Build to Get Hired in 2026
Follow this framework to build a project that will impress hiring managers The post The Exact ML Project I’d Build to Get Hired in 2026 appeared first on Towards Data Science .
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So in this article, I’m going to give you the exact framework I developed and followed to find your perfect ML project that will land you a job. The problem is nobody can hand you a project like that. So instead of handing you an idea, I’m going to give you a framework to follow to develop a project like this.
TL;DR:
Follow this framework to build a project that will impress hiring managers The post The Exact ML Project I’d Build to Get Hired in 2026 appeared first on Towards Data Science .
Towardsdatascience
Jun 9, 05:53
Can Machine Learning Predict the World Cup?
Building an ML football forecaster in R The post Can Machine Learning Predict the World Cup? appeared first on Towards Data Science .
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Can Machine Learning Predict the World Cup?. Building an ML football forecaster in R The post Can Machine Learning Predict the World Cup? appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
Jun 8, 18:00
Increase Recommendation Systems’ Precision with LLMs, Using Python
This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science .
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The philosophical discussion is out of scope for this article, but the practical consequences of these considerations are very much in line with data science and software engineering in general. In software engineering and data science, there is no such thing as the “perfect design” per se .
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This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science .
Towardsdatascience
Jun 8, 16:30
How to Keep Quantum Information Alive for Machine Learning
Quantum Machine Learning promises powerful new ways of processing information, but quantum states are extraordinarily fragile. In this article, we explore why quantum information is so difficult to protect, how noise and decoherence introduce errors, and the fundamental ideas behind Quantum Error Correction: the technology that may make large-scale quantum machine learning possible. The post How to Keep Quantum Information Alive for Machine Learning appeared first on Towards Data Science .
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Quantum Machine Learning promises powerful new ways of processing information, but quantum states are extraordinarily fragile. In this article, we explore why quantum information is so difficult to protect, how noise and decoherence introduce errors, and the fundamental ideas behind Quantum Error Correction: the technology that may make large-scale quantum machine learning pos…
TL;DR:
The post How to Keep Quantum Information Alive for Machine Learning appeared first on Towards Data Science .
Kdnuggets
Jun 8, 16:00
Why Do LLMs Corrupt Your Documents When You Delegate?
Analyzing several reasons why structural content decay may happen when asking LLMs to perform complex document editing for us.
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Therefore, they trust AI systems at an unprecedented level to maintain the integrity of files like documents across multiple interactions. When delegating tasks to a large language model (LLM) , it may silently corrupt documents you handed to it.
TL;DR:
Analyzing several reasons why structural content decay may happen when asking LLMs to perform complex document editing for us.
404media
Jun 8, 15:14
A Farmer Donated Land to Turn into a Park. The City Is Building a Data Center
Almost 30 years ago a farming family deeded land to the City of Taylor, Texas, on the condition the city use it for a public park.
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The City Is Building a Data Center. Now the land that was supposed to belong to the community will become a 135,000 square foot data center. Now a data center will be there, just 500 feet from Griffin’s home, nestled between a power substation and the nearby railroad tracks.
TL;DR:
Taylor sold it to Blueprint, a data center developer, for $10 million in 2025.
Towardsdatascience
Jun 8, 15:00
4 New Techniques to Maximize Claude Code
Get the most out of Claude Code with these four techniques The post 4 New Techniques to Maximize Claude Code appeared first on Towards Data Science .
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In this article , I’ll cover some of the newest techniques that I’ve developed and am actively using whenever I code with Claude Code and Codex. Both of these are excellent coding models that I’m using every single day when I program. First of all, I always like to cover why you should be interested in an article.
TL;DR:
Get the most out of Claude Code with these four techniques The post 4 New Techniques to Maximize Claude Code appeared first on Towards Data Science .
Kdnuggets
Jun 8, 14:00
Anthropic’s Complete Guide to Claude Skills Building
This guide covers the complete picture: what skills are technically, how to plan and design them, the exact file structure and naming rules, how to write instructions that Claude follows reliably, a complete working skill built from scratch, how to test and distribute, and what to do when things go wrong.
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Claude Skills are the fix. Skills launched in October 2025 and quickly became the dominant way to give Claude domain-specific capabilities in Claude Code, Claude Desktop, and the Claude API. As of May 2026, the repo has 141,000+ stars and 16,000+ forks, making it one of the most-watched AI tooling repositories on GitHub.
TL;DR:
This guide covers the complete picture: what skills are technically, how to plan and design them, the exact file structure and naming rules, how to write instructions that Claude follows reliably, a complete working skill built from scratch, how to test and distribute, and what to do when things go wrong.
Towardsdatascience
Jun 8, 13:30
Sequential Fitting: A Different Perspective on the Spectral Bias of Neural Networks
What Fourier analysis misses The post Sequential Fitting: A Different Perspective on the Spectral Bias of Neural Networks appeared first on Towards Data Science .
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by Conor Rowan and Finn Murphy-Blanchard Introduction Evidenced by their success with complex tasks such as image classification [1], autonomy [2], and language modeling [3], neural networks are spectacularly good at fitting high-dimensional, nonlinear functions from data.
TL;DR:
What Fourier analysis misses The post Sequential Fitting: A Different Perspective on the Spectral Bias of Neural Networks appeared first on Towards Data Science .
Towardsdatascience
Jun 8, 12:30
The Polynomial That Fixed 30 Years of Cloth Simulation
The clipping bug has lived in every 3D simulation pipeline for three decades. Here is exactly why it happens, how the math breaks, and how swapping one equation fixes it; as well as the python code to see it for yourself! The post The Polynomial That Fixed 30 Years of Cloth Simulation appeared first on Towards Data Science .
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The clipping bug has lived in every 3D simulation pipeline for three decades. Here is exactly why it happens, how the math breaks, and how swapping one equation fixes it; as well as the python code to see it for yourself! The post The Polynomial That Fixed 30 Years of Cloth Simulation appeared first on Towards Data Science .
TL;DR:
The post The Polynomial That Fixed 30 Years of Cloth Simulation appeared first on Towards Data Science .
Kdnuggets
Jun 8, 12:00
5 Must-Know Python Concepts for AI Engineers
In this article, we will explore five critical Python concepts that every AI engineer must know to build scalable, secure, and robust systems.
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The role of an AI engineer has now definitively split from traditional data science. Python plays a central role in AI engineering just as it has historically played — and currently plays! To build production-grade AI applications and deep learning architectures, you need to master the fundamental Python concepts that modern approaches rely on.
TL;DR:
In this article, we will explore five critical Python concepts that every AI engineer must know to build scalable, secure, and robust systems.
Ms365news
Jun 8, 08:11
OneDrive data now has an expiry date
This website uses cookies. We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.
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OneDrive data now has an expiry date. We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.
TL;DR:
By accepting our use of cookies, your data will be aggregated with all other user data.
Reeserichardson
Jun 8, 06:56
How much of Thermo Fisher's antibody data has been manipulated?
[ TL;DR: As of 3 June 2026, we have identified more than 450 images bearing signs of manipulation in verification data advertised by Thermo Fisher Scientific in its online primary antibodies catalog…
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How much of Thermo Fisher's antibody data has been manipulated?. [ TL;DR: As of 3 June 2026, we have identified more than 450 images bearing signs of manipulation in verification data advertised by Thermo Fisher Scientific in its online primary antibodies catalog (+1 by Abcam).
TL;DR:
It is labeled as “Advanced Verification” data on Thermo Fisher’s site and its caption implies that the data was produced internally (other images in the catalog that have not been produced internally are labeled under “Published Figures”).
Troyhunt
Jun 8, 03:17
1k Data Breaches Later, the Disclosure Lag Is Worse
Today, I loaded the 1,000th data breach into Have I Been Pwned . Reflecting on that milestone number, I pondered how to mark the occasion in writing, and what immediately came to mind was a very simp…
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1k Data Breaches Later, the Disclosure Lag Is Worse. 8.7M records with 7.5M email addresses and loyalty program data were published yesterday. The subsequent leak on the 24th was very public: an announcement was posted to the group's dark-web site, the data itself was published to their clear-web site, and industry commentary followed:
TL;DR:
Today, I loaded the 1,000th data breach into Have I Been Pwned .
Towardsdatascience
Jun 7, 15:00
We Should Train AI to Betray Its Users
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
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The dilemma You are the lowest level employee at an engineering company but have uncovered a deadly secret. Your cursor hovers in the “to:” line. The twist: you are not an employee, you are an AI.
TL;DR:
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
Towardsdatascience
Jun 7, 13:00
Building a Multi-Agent System in Python
An introduction to multi-agent systems The post Building a Multi-Agent System in Python appeared first on Towards Data Science .
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AI Agents are the talk of the town. We know about AI Agents, but what if we can build and use different AI Agents for different roles in a bigger project? As AI applications become more advanced, we are moving from single AI models that answer simple questions and do straightforward tasks to systems where multiple AI agents work together to solve complex problems.
TL;DR:
An introduction to multi-agent systems The post Building a Multi-Agent System in Python appeared first on Towards Data Science .
Towardsdatascience
Jun 6, 17:00
Picking an Experimentation Platform: A Retrospective
My approach to guiding the choice between Eppo and Statsig, and the lessons learned The post Picking an Experimentation Platform: A Retrospective appeared first on Towards Data Science .
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My approach to guiding the choice between Eppo and Statsig, and the lessons learned There is a moment , in every company that wants to ship products people love, when “we should experiment more” becomes “we cannot keep experimenting like this.” Hand-tuned holdouts; traffic-allocation tickets bouncing between PMs and engineers; analyst calendars booked weeks out.
TL;DR:
My approach to guiding the choice between Eppo and Statsig, and the lessons learned The post Picking an Experimentation Platform: A Retrospective appeared first on Towards Data Science .
Towardsdatascience
Jun 6, 15:00
Who Will Win the 2026 Soccer World Cup?
Building a forecast from Elo, Poisson, and 10,000 simulations The post Who Will Win the 2026 Soccer World Cup? appeared first on Towards Data Science .
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Who Will Win the 2026 Soccer World Cup?. Building a forecast from Elo, Poisson, and 10,000 simulations The post Who Will Win the 2026 Soccer World Cup? appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Towardsdatascience
Jun 6, 13:00
My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax
what it costs, what it gains and the three mistakes that I make The post My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax appeared first on Towards Data Science .
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My work involves taking models of the Universe – dark energy equations of state, modified gravity, tachyonic fields – and asking: what do the data actually say about the parameters? I usually run dynesty nested sampling for a few thousand to a few hundred thousand likelihood evaluations depending upon the complexity of the model. For a single nested sampling run.
TL;DR:
what it costs, what it gains and the three mistakes that I make The post My SciPy ODE Solver Was Killing My Bayesian Inference: A Cosmologist’s Honest Account of Discovering Diffrax appeared first on Towards Data Science .
Cnbc
Jun 5, 20:06
Google to pay SpaceX $920M a month for compute capacity at xAI data centers
Days before a planned IPO that's expected to raise record sums of cash, SpaceX has inked a deal with Google that will bring in $920 million a month by providing AI compute capacity to the search gian…
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Google to pay SpaceX $920M a month for compute capacity at xAI data centers. Days before a planned IPO that's expected to raise record sums of cash, SpaceX has inked a deal with Google that will bring in $920 million a month by providing AI compute capacity to the search giant.
TL;DR:
Last month, Anthropic announced a deal to use all of SpaceX's compute capacity at its Colossus 1 data center in Memphis, Tennessee.
Towardsdatascience
Jun 5, 16:30
My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server
I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result: 5 clients, under 50ms, and a design that stays simple without sacrificing capability. The post My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server appeared first on Towards Data Science .
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I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. The post My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server appeared first on Towards Data Science .
TL;DR:
The post My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server appeared first on Towards Data Science .
Towardsdatascience
Jun 5, 15:00
The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy
How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Science .
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The Fundamental Choice in Reinforcement Learning: On‑Policy vs. How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Science .
TL;DR:
Off‑Policy appeared first on Towards Data Science .
Scienceaim
Jun 5, 14:49
New York just passed a one-year temporary ban on data centers
On June 5, 2026, lawmakers sent a bill to Governor Kathy Hochul that would impose a one-year moratorium on permits for new large-scale data centers in the state.
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New York just passed a one-year temporary ban on data centers. On June 5, 2026, lawmakers sent a bill to Governor Kathy Hochul that would impose a one-year moratorium on permits for new large-scale data centers in the state. It bundles several ideas together under what lawmakers are calling a “responsible data center development” package.
TL;DR:
First, it pauses the issuance of new permits for data centers for one year.
Kdnuggets
Jun 5, 14:00
A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling
Discover three post-hoc methods for closing the gap between confidence and accuracy.
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A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling. Discover three post-hoc methods for closing the gap between confidence and accuracy.
TL;DR:
Discover three post-hoc methods for closing the gap between confidence and accuracy.
Towardsdatascience
Jun 5, 13:30
Automate Writing Your LLM Prompts
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science .
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Here, the software will work with predefined prompts and will pass these to the LLMs. Which means, they have to be written in a way that’s robust and reliable in the first place — we need prompts that we can be confident will work consistently well in production.
TL;DR:
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science .
Kdnuggets
Jun 5, 12:00
3 SpaCy Tricks for Efficient Text Processing & Entity Recognition
In this article, we will explore three essential spaCy tricks that every developer should have in their toolkit to maximize processing speed and customize entity recognition.
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Thanks especially to contemporary large language models, natural language processing (NLP) is a fundamental pillar of modern AI and software systems. When it comes to production-grade NLP in Python, spaCy is the undisputed industry standard. They load a model, run it on text, and accept the default processing speeds and extraction limits.
TL;DR:
In this article, we will explore three essential spaCy tricks that every developer should have in their toolkit to maximize processing speed and customize entity recognition.
Machinelearningmastery
Jun 5, 12:00
Building Semantic Search with Transformers.js and Sentence Embeddings
You've probably shipped this bug before, where a user types " affordable laptop " into your search bar and gets zero results.
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Share Post Share In this article, you will learn how sentence embeddings work and how to build a fully client-side semantic search engine using Transformers.js, with no server, no API key, and no backend infrastructure required. Semantic search fixes this by comparing meaning. And with Transformers.
TL;DR:
You've probably shipped this bug before, where a user types " affordable laptop " into your search bar and gets zero results.
Towardsdatascience
Jun 5, 12:00
How to Fine-Tune an SLM for Emotion Recognition
Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science .
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Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication Introduction Recent small language models (SLMs) fine-tuned for sentiment classification infer sentiment as a single score, capturing the overall emotional tone of the text.
TL;DR:
Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science .
Towardsdatascience
Jun 4, 17:04
How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI
Abacus.AI and the case for unified AI workflows The post How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI appeared first on Towards Data Science .
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Sponsored by Abacus.AI The rapid adoption of AI in writing, design, and analysis, to name just a few areas, came with mixed results: it made workflows faster and easier in some ways, and more complicated in others. As AI evolved into its current, agentic-focused form, however, the ecosystem of “AI tools” expanded rapidly, and workflow optimization became harder.
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Abacus.AI and the case for unified AI workflows The post How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI appeared first on Towards Data Science .
Towardsdatascience
Jun 4, 16:30
Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model
In Part 1 of this series, we introduced Chronos-2, a time-series foundation model. We got our hands dirty by walking through a real case study and saw what Chronos-2 can do straight out of the box, with no training. But as we noted at the end of Part 1, zero-shot isn’t always enough. In cases […] The post Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model appeared first on Towards Data Science .
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In Part 1 of this series, we introduced Chronos-2, a time-series foundation model. We got our hands dirty by walking through a real case study and saw what Chronos-2 can do straight out of the box, with no training. But as we noted at the end of Part 1, zero-shot isn’t always enough.
TL;DR:
In cases […] The post Five Ways to Fine-Tune Chronos-2, the Time Series Foundation Model appeared first on Towards Data Science .
Towardsdatascience
Jun 4, 15:00
Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce
When images, mosaics, and data cubes exist in abundance, but field labels are expensive, rare, and imperfect. The post Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce appeared first on Towards Data Science .
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Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce. When images, mosaics, and data cubes exist in abundance, but field labels are expensive, rare, and imperfect. The post Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce appeared first on Towards Data Science .
TL;DR:
The post Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce appeared first on Towards Data Science .
Kdnuggets
Jun 4, 14:00
What the Agentic Era Means for Data Science
Learn how AI agents are reshaping data science workflows and which skills practitioners need in 2026.
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Learn how AI agents are reshaping data science workflows and which skills practitioners need in 2026. Something has shifted at the intersection of AI and data science, and it's changed how practitioners work. This period is defined by AI systems executing autonomous, goal-directed behavior, and it has rewritten what data scientists actually do day-to-day.
TL;DR:
Learn how AI agents are reshaping data science workflows and which skills practitioners need in 2026.
Towardsdatascience
Jun 4, 13:30
FPN Paper Walkthrough: Leveraging the Internal Pyramid
Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch The post FPN Paper Walkthrough: Leveraging the Internal Pyramid appeared first on Towards Data Science .
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Unfortunately, my explanation about FPN in that article was not quite thorough since I was focusing more on YOLOv3 itself. Before we get into FPN, we first need to know that the structure of an object detection model is different from that of the classification model, in which the main difference lies in the very last layer.
TL;DR:
Understanding how FPN allows deep learning models detecting small objects and how to implement it from scratch The post FPN Paper Walkthrough: Leveraging the Internal Pyramid appeared first on Towards Data Science .
Machinelearningmastery
Jun 4, 12:55
Using Scikit-LLM with Open-Source LLMs
This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.
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Share Post Share In this article, you will learn how to use locally hosted language models through Ollama to perform text classification tasks, all without spending a cent on API calls. Then I recommend you check this article out first.
TL;DR:
This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.
Kdnuggets
Jun 4, 12:00
7 Steps to Mastering Time Series Analysis with Python
This article breaks down 7 key steps to help you analyze and forecast time series data with Python. Time series data is everywhere — energy consumption logged hourly, transactions recorded to the m…
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Analyzing, modeling, and forecasting this kind of data is one of the most in-demand skills across industries. What makes time series distinct from general data science is that it demands a different mental model at every stage. To get started, you need to understand the properties that make time series structurally different from tabular data.
TL;DR:
This article breaks down 7 key steps to help you analyze and forecast time series data with Python.
Towardsdatascience
Jun 4, 12:00
Is an Online Master’s Degree in AI a Good Idea?
A look at the real-world value of online graduate AI programs, combining hard data with firsthand experience of a big tech machine learning engineer The post Is an Online Master’s Degree in AI a Good Idea? appeared first on Towards Data Science .
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Is an Online Master’s Degree in AI a Good Idea?. A look at the real-world value of online graduate AI programs, combining hard data with firsthand experience of a big tech machine learning engineer The post Is an Online Master’s Degree in AI a Good Idea? appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Thebrockovichreport
Jun 3, 16:57
If AI data centers are so great, why are they being built in secret?
I’ve shown up to community after community across the country for decades because the people who live in these towns invite me.
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If AI data centers are so great, why are they being built in secret?. On April 27, I put out a simple ask: if you have concerns about an AI data center near you, tell me about it. Residents are using words like silenced , ignored , secretive , and not seen and not heard .
TL;DR:
So when I started hearing from people about AI data centers appearing in their communities with little to no notice, I paid attention.
Towardsdatascience
Jun 3, 16:30
I Spent May Evaluating Different Engines for OCR
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .
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Most companies use free tools alongside paid APIs to try to convert these documents, and if you want structured output, APIs like Textract Structured run you up to around $65 per 1k pages. In the last few years, though, a lot of new options have appeared: smaller open-source vision models specialized for OCR, general vision-language models, and document parsing tools like Llam…
TL;DR:
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .
Towardsdatascience
Jun 3, 15:00
Why AI Is NOT Stealing Your Job
AI does not decide who gets fired. Companies do. The post Why AI Is NOT Stealing Your Job appeared first on Towards Data Science .
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AI does not decide who gets fired. Companies do. The post Why AI Is NOT Stealing Your Job appeared first on Towards Data Science .
TL;DR:
The post Why AI Is NOT Stealing Your Job appeared first on Towards Data Science .
Kdnuggets
Jun 3, 14:00
How to Write to Files in Python: A Beginner’s Guide
Learn how to write, append, and save text, CSV, and JSON files in Python using native file handling tools that work out of the box.
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It lets you save data permanently instead of losing it when your program stops. You can use file saving to store results, logs, reports, user input, settings, and structured data. By the end, you will be able to write Python programs that save results, reports, logs, and structured data to files.
TL;DR:
Learn how to write, append, and save text, CSV, and JSON files in Python using native file handling tools that work out of the box.
Towardsdatascience
Jun 3, 13:30
I Built a C++ Backend So My GPU Would Stop Eating Air
A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing. The post I Built a C++ Backend So My GPU Would Stop Eating Air appeared first on Towards Data Science .
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I Built a C++ Backend So My GPU Would Stop Eating Air. A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing. The post I Built a C++ Backend So My GPU Would Stop Eating Air appeared first on Towards Data Science .
TL;DR:
The post I Built a C++ Backend So My GPU Would Stop Eating Air appeared first on Towards Data Science .
Kdnuggets
Jun 3, 12:00
5 Fun Papers That Explain LLMs Clearly
Want to understand LLMs better? Start with these five foundational papers that explain how they work.
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5 Fun Papers That Explain LLMs Clearly. Want to understand LLMs better? Start with these five foundational papers that explain how they work.
TL;DR:
Want to understand LLMs better?
Towardsdatascience
Jun 3, 12:00
What AI Agents Should Never Do on Their Own
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
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I use agents daily. The task was clear and the agent followed instructions, the only problem was that nothing told it where to stop . Recovery cost varies by task .
TL;DR:
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
Redis
Jun 3, 10:05
Redis 8.8: New array data structure, rate limiter, performance improvements
June 02, 2026 14 minute read Lior Kogan Redis 8.8 in Redis Open Source is now available, bringing performance improvements alongside a set of powerful new features.
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Redis 8.8: New array data structure, rate limiter, performance improvements. Redis has always been about choosing the right data structure for the job. In Redis 8.8, we introduce a window counter rate limiter (by @raffertyyu , together with the Redis team).
TL;DR:
In Redis 8.8, we introduce a new general-purpose data structure: array .
Towardsdatascience
Jun 2, 16:30
Code Is Cheap. Engineering Judgement Is Now the Scarce Resource
The barriers to building have collapsed. That shifts the bottleneck to ownership, validation, taste, and deciding what should actually exist The post Code Is Cheap. Engineering Judgement Is Now the Scarce Resource appeared first on Towards Data Science .
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The barriers to building have collapsed. That shifts the bottleneck to ownership, validation, taste, and deciding what should actually exist The post Code Is Cheap. Engineering Judgement Is Now the Scarce Resource appeared first on Towards Data Science .
TL;DR:
Engineering Judgement Is Now the Scarce Resource appeared first on Towards Data Science .
Vox
Jun 2, 15:32
Americans don't know how to fight AI. So they're fighting data centers
We rely on readers like you to fund our journalism. Will you support our work and become a Vox Member today? The data center revolt is a symptom of our political failure on AI.
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Americans don't know how to fight AI. So they're fighting data centers. Gift Demonstrators protest a data center in Tucson, Arizona, in May 2026.
TL;DR:
The data center revolt is a symptom of our political failure on AI.
Towardsdatascience
Jun 2, 15:00
From Local App to Public Website in Minutes
Three free ways to quickly deploy a static web app that anyone can access The post From Local App to Public Website in Minutes appeared first on Towards Data Science .
TL;DR:
Three free ways to quickly deploy a static web app that anyone can access The post From Local App to Public Website in Minutes appeared first on Towards Data Science .
Kdnuggets
Jun 2, 14:00
A Gentle Primer on LLM Explainability
This article discusses LLM explainability and outlines the advances, trends, and ongoing developments in this important field of study.
TL;DR:
This article discusses LLM explainability and outlines the advances, trends, and ongoing developments in this important field of study.
Towardsdatascience
Jun 2, 13:30
From Regex to Vision Models: Which RAG Technique Fits Which Problem
Enterprise Document Intelligence [Vol.1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Models: Which RAG Technique Fits Which Problem appeared first on Towards Data Science .
TL;DR:
Enterprise Document Intelligence [Vol.1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Models: Which RAG Technique Fits Which Problem appeared first on Towards Data Science .
Machinelearningmastery
Jun 2, 12:00
Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?
In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .
TL;DR:
In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .
Towardsdatascience
Jun 2, 12:00
Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn
Exploratory data analysis on the US Census Dataset The post Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn appeared first on Towards Data Science .
TL;DR:
Exploratory data analysis on the US Census Dataset The post Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn appeared first on Towards Data Science .
Kdnuggets
Jun 2, 12:00
10 GitHub Repositories for Modern Database Systems and Tools
Explore 10 top open-source GitHub repositories for modern databases, analytics, SQL, caching, monitoring, replication, PostgreSQL, SQLite, and AI agent memory.
TL;DR:
Explore 10 top open-source GitHub repositories for modern databases, analytics, SQL, caching, monitoring, replication, PostgreSQL, SQLite, and AI agent memory.
Towardsdatascience
Jun 1, 18:49
RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem
Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science .
TL;DR:
Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science .
Towardsdatascience
Jun 1, 17:30
How to Combine Claude Code and Codex for Maximum Coding Power
Get the most out of each coding model to have a very powerful coding setup The post How to Combine Claude Code and Codex for Maximum Coding Power appeared first on Towards Data Science .
TL;DR:
Get the most out of each coding model to have a very powerful coding setup The post How to Combine Claude Code and Codex for Maximum Coding Power appeared first on Towards Data Science .
Towardsdatascience
Jun 1, 15:00
Ensuring Data Integrity with Cryptographic Hashing and the Ethereum Blockchain
Applying blockchain primitives to dataset versioning, provenance, and integrity assurance The post Ensuring Data Integrity with Cryptographic Hashing and the Ethereum Blockchain appeared first on Towards Data Science .
TL;DR:
Applying blockchain primitives to dataset versioning, provenance, and integrity assurance The post Ensuring Data Integrity with Cryptographic Hashing and the Ethereum Blockchain appeared first on Towards Data Science .
Kdnuggets
Jun 1, 14:00
Mocking a Year of IoT Sensor Time Series Data with Mimesis
In this guide, you will learn the process of generating a year's worth of daily temperature readings, mimicking a seasonal curve that looks like real — all together with device-level metadata, and ready to build based on open-source frameworks.
TL;DR:
In this guide, you will learn the process of generating a year's worth of daily temperature readings, mimicking a seasonal curve that looks like real — all together with device-level metadata, and ready to build based on open-source frameworks.
Towardsdatascience
Jun 1, 13:30
It’s the Lessons We Learned Along the Way. Or, Is It?
Research projects in the age of AI The post It’s the Lessons We Learned Along the Way. Or, Is It? appeared first on Towards Data Science .
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It’s the Lessons We Learned Along the Way. Research projects in the age of AI The post It’s the Lessons We Learned Along the Way. appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Kdnuggets
Jun 1, 12:00
5 Must-Know Python Concepts for Data Scientists
In this article, we will dive deep into five must-know Python concepts that will help you transition from writing clunky, slow spaghetti code to constructing lightning-fast, production-grade, and beautifully functional data pipelines.
TL;DR:
In this article, we will dive deep into five must-know Python concepts that will help you transition from writing clunky, slow spaghetti code to constructing lightning-fast, production-grade, and beautifully functional data pipelines.
Towardsdatascience
Jun 1, 12:00
Escaping the Valley of Choice in BI
Why Agentic BI threatens an entire profession The post Escaping the Valley of Choice in BI appeared first on Towards Data Science .
TL;DR:
Why Agentic BI threatens an entire profession The post Escaping the Valley of Choice in BI appeared first on Towards Data Science .
Promptarmor
May 31, 20:35
ChatGPT for Google Sheets is vulnerable to data exfiltration and phishing
ChatGPT for Google Sheets is vulnerable to data exfiltration and phishing overlay attacks that affect workbooks across the victim’s account after an indirect prompt injection in a single sheet.
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ChatGPT for Google Sheets is vulnerable to data exfiltration and phishing. Recently, OpenAI launched an AI extension for using ChatGPT in Google Sheets, which has accumulated over 185,000 downloads since its launch less than a month ago.
TL;DR:
ChatGPT for Google Sheets is vulnerable to data exfiltration and phishing overlay attacks that affect workbooks across the victim’s account after an indirect prompt injection in a single sheet.
Towardsdatascience
May 31, 17:00
Solving a Murder Mystery Using Bayesian Inference
How Knives Out teaches Bayesian thinking (without you realizing it) The post Solving a Murder Mystery Using Bayesian Inference appeared first on Towards Data Science .
TL;DR:
How Knives Out teaches Bayesian thinking (without you realizing it) The post Solving a Murder Mystery Using Bayesian Inference appeared first on Towards Data Science .
Towardsdatascience
May 31, 15:00
Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost
Enterprise Document Intelligence [Vol. 1 #2bis] Why stacking a reranker on top of weak retrieval doesn’t save it, what cross-encoders actually fix vs what they don’t, and where the editorial position of the series lands. The post Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost appeared first on Towards Data Science .
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Enterprise Document Intelligence [Vol. 1 #2bis] Why stacking a reranker on top of weak retrieval doesn’t save it, what cross-encoders actually fix vs what they don’t, and where the editorial position of the series lands. The post Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost appeared first on Towards Data Science .
TL;DR:
The post Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost appeared first on Towards Data Science .
Towardsdatascience
May 31, 13:00
Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs
Structure-guided NER optimization for enterprise GraphRAG systems The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science .
TL;DR:
Structure-guided NER optimization for enterprise GraphRAG systems The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science .
Towardsdatascience
May 30, 17:00
Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About
As AI gets smarter, the real differentiator may be how well humans regulate their own thinking. The post Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About appeared first on Towards Data Science .
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Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About. As AI gets smarter, the real differentiator may be how well humans regulate their own thinking. The post Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About appeared first on Towards Data Science .
TL;DR:
The post Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About appeared first on Towards Data Science .
Towardsdatascience
May 30, 15:00
Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval
Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does. The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval appeared first on Towards Data Science .
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Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does. The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval appeared first on Towards Data Science .
TL;DR:
The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval appeared first on Towards Data Science .
Towardsdatascience
May 30, 13:00
Qdrant TurboQuant Explained: Is TurboQuant the Silver Bullet?
Most engineers see quantization as shrinking vectors. TurboQuant asks a harder question: can you shrink them without breaking their geometry? The post Qdrant TurboQuant Explained: Is TurboQuant the Silver Bullet? appeared first on Towards Data Science .
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Qdrant TurboQuant Explained: Is TurboQuant the Silver Bullet?. The post Qdrant TurboQuant Explained: Is TurboQuant the Silver Bullet? appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Dailycal
May 30, 05:25
Records Show UC Sharing Data with US Customs and Border Protection
In California, sharing data collected by ALPR systems with out-of-state agencies is illegal and could incur fines of up to $2,500 per instance of illicit sharing.
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Records Show UC Sharing Data with US Customs and Border Protection. Public records turned up by The Ellis Collective, a student-led research group, have revealed that the UC system shared data collected by automated license plate readers at multiple campuses with U.S.
TL;DR:
In California, sharing data collected by ALPR systems with out-of-state agencies is illegal and could incur fines of up to $2,500 per instance of illicit sharing.
Machinelearningmastery
May 30, 02:54
Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient
This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch together.
TL;DR:
This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch together.
Towardsdatascience
May 29, 19:10
Baseline Enterprise RAG, From PDF to Highlighted Answer
Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science .
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Baseline Enterprise RAG, From PDF to Highlighted Answer. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science .
TL;DR:
The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science .
Github
May 29, 17:06
Reconciling Kubernetes cost estimates with CUR / FOCUS billing data
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Reconciling Kubernetes cost estimates with CUR / FOCUS billing data. Slack-native — /burn for instant cost reports. Install # Homebrew brew install tanrikuluozlem/burn/burn # Upgrade brew upgrade tanrikuluozlem/burn/burn # Binary VERSION= $( curl -s https://api.github.
TL;DR:
/burn ask "..." for AI analysis.
Towardsdatascience
May 29, 16:30
RAG Is Burning Money — I Built a Cost Control Layer to Fix It
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality. The post RAG Is Burning Money — I Built a Cost Control Layer to Fix It appeared first on Towards Data Science .
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RAG Is Burning Money — I Built a Cost Control Layer to Fix It. Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. The post RAG Is Burning Money — I Built a Cost Control Layer to Fix It appeared first on Towards Data Science .
TL;DR:
The post RAG Is Burning Money — I Built a Cost Control Layer to Fix It appeared first on Towards Data Science .
Towardsdatascience
May 29, 15:00
Why Gradient Descent Became Stochastic
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent The post Why Gradient Descent Became Stochastic appeared first on Towards Data Science .
TL;DR:
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent The post Why Gradient Descent Became Stochastic appeared first on Towards Data Science .
Kdnuggets
May 29, 14:00
Practical NLP in the Browser with Transformers.js
This tutorial covers three NLP tasks: text classification, zero-shot labelling, and question answering using Transformers.js's pipeline() API.
TL;DR:
This tutorial covers three NLP tasks: text classification, zero-shot labelling, and question answering using Transformers.js's pipeline() API.
Towardsdatascience
May 29, 13:30
Explaining Lineage in DAX
One of the most important concepts in DAX is lineage. It’s about the information on where something comes from. Let’s see what it is and how we can manipulate it. The post Explaining Lineage in DAX appeared first on Towards Data Science .
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One of the most important concepts in DAX is lineage. It’s about the information on where something comes from. Let’s see what it is and how we can manipulate it. The post Explaining Lineage in DAX appeared first on Towards Data Science .
TL;DR:
The post Explaining Lineage in DAX appeared first on Towards Data Science .
Kdnuggets
May 29, 12:00
The ‘Entry-Level’ Gatekeeper: Auditing Job Descriptions with Textstat
This article shows how to use free, open-source tools like Python and its Textstat library to build a script that automates the process of capturing "gatekeeping language" in job descriptions before publishing them.
TL;DR:
This article shows how to use free, open-source tools like Python and its Textstat library to build a script that automates the process of capturing "gatekeeping language" in job descriptions before publishing them.
Towardsdatascience
May 29, 12:00
Five Questions About Chronos-2, the Time Series Foundation Model
Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting. The post Five Questions About Chronos-2, the Time Series Foundation Model appeared first on Towards Data Science .
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Five Questions About Chronos-2, the Time Series Foundation Model. Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting. The post Five Questions About Chronos-2, the Time Series Foundation Model appeared first on Towards Data Science .
TL;DR:
The post Five Questions About Chronos-2, the Time Series Foundation Model appeared first on Towards Data Science .
Bbc
May 29, 03:01
Cars collect a startling amount of data about you
Home News US & Canada UK UK Politics England N. Ireland N. Ireland Politics Scotland Scotland Politics Wales Wales Politics Africa Asia China India Australia Europe Latin America Middle East In Pictu…
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Cars collect a startling amount of data about you. Home News US & Canada UK UK Politics England N. Ireland Politics Scotland Scotland Politics Wales Wales Politics Africa Asia China India Australia Europe Latin America Middle East In Pictures BBC InDepth BBC Verify Sport Business World of Business Technology of Business NYSE Opening Bell Technology Artificial Intelligence Inte…
TL;DR:
Home News US & Canada UK UK Politics England N.
Towardsdatascience
May 28, 16:30
EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026
A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since. The post EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026 appeared first on Towards Data Science .
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EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026. A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since. The post EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026 appeared first on Towards Data Science .
TL;DR:
A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since.
Towardsdatascience
May 28, 15:00
The Infrastructure Behind Making Local LLM Agents Actually Useful
Lessons from building a fast, reliable scientific agent with local open-weight models, vLLM, and long-context infrastructure The post The Infrastructure Behind Making Local LLM Agents Actually Useful appeared first on Towards Data Science .
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That works for a chatbot, but it doesn’t automatically work for an agent. In my case, I’ve been building an agent for automated single-cell RNA-seq analysis. Building all of these on top of a local model also means you own the infrastructure, and that’s what I’m going to be focusing on here.
TL;DR:
Lessons from building a fast, reliable scientific agent with local open-weight models, vLLM, and long-context infrastructure The post The Infrastructure Behind Making Local LLM Agents Actually Useful appeared first on Towards Data Science .
Kdnuggets
May 28, 14:00
Tweaking Local Language Model Settings with Ollama
In this article, we will go deep under the hood of Ollama's configuration engine, exploring how to fine-tune local language model parameters.
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Language models continue to shape how machine learning practitioners and developers build applications. By bypassing third-party APIs, running models locally guarantees complete data privacy, eliminates per-token API costs, and enables offline operation. However, simply pulling a model and running it with the default settings is rarely optimal.
TL;DR:
In this article, we will go deep under the hood of Ollama's configuration engine, exploring how to fine-tune local language model parameters.
Towardsdatascience
May 28, 13:30
Why AI Still Can’t Solve Your Real Mathematical Optimization Problem
And what ORPilot does differently The post Why AI Still Can’t Solve Your Real Mathematical Optimization Problem appeared first on Towards Data Science .
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If you’ve ever tried to use AI to build a mathematical optimization model for a real business problem, you’ve probably run into the same wall: the AI works beautifully on textbook examples and falls apart the moment you hand it your actual data and your actual problem.
TL;DR:
And what ORPilot does differently The post Why AI Still Can’t Solve Your Real Mathematical Optimization Problem appeared first on Towards Data Science .
Machinelearningmastery
May 28, 12:00
Building a Context Pruning Pipeline for Long-Running Agents
Share Post Share In this article, you will learn how to implement a context pruning pipeline for long-running AI agents, enabling them to manage conversational memory efficiently through semantic sim…
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Share Post Share In this article, you will learn how to implement a context pruning pipeline for long-running AI agents, enabling them to manage conversational memory efficiently through semantic similarity. Building a context pruning pipeline can address this issue by dynamically managing recent conversational memory.
TL;DR:
Modern AI agents built on top of large language models (LLMs) are designed to run continuously.
Kdnuggets
May 28, 12:00
7 Real World AI Projects to Build in 2026 (with Guides)
Explore seven practical AI projects that automate real workflows, including job search, web research, investment research, market trend analysis, invoice processing, chart digitization, and personalized exercise training.
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AI projects are most useful when they solve real workflow problems, not just when they demonstrate a new model or tool. Instead of manually searching, reading, comparing, copying, and summarizing information, these projects show how AI can handle much of the repetitive work for you. The tutorial uses Kimi K2.6 , Olostep , OpenAI Agents SDK , and Gradio .
TL;DR:
Explore seven practical AI projects that automate real workflows, including job search, web research, investment research, market trend analysis, invoice processing, chart digitization, and personalized exercise training.
Towardsdatascience
May 28, 12:00
DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation
A diffusion-inspired framework for stress-testing and denoising LLM-as-a-Judge pipelines, applied to safety-critical driving video. The post DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation appeared first on Towards Data Science .
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DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation. A diffusion-inspired framework for stress-testing and denoising LLM-as-a-Judge pipelines, applied to safety-critical driving video. The post DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation appeared first on Towards Data Science .
TL;DR:
The post DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation appeared first on Towards Data Science .
Towardsdatascience
May 27, 16:30
How to Effectively Run Many Claude Code Sessions in Parallel
Keep an overview of all your coding agents that run in parallel The post How to Effectively Run Many Claude Code Sessions in Parallel appeared first on Towards Data Science .
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Keep an overview of all your coding agents that run in parallel If you’re running coding agents sequentially and not in multiple runs in parallel, you’re losing out. One of the key benefits of coding agents is that you can start completing work in parallel, something that was never really possible before when working on software engineering tasks.
TL;DR:
Keep an overview of all your coding agents that run in parallel The post How to Effectively Run Many Claude Code Sessions in Parallel appeared first on Towards Data Science .
Towardsdatascience
May 27, 15:00
Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Towards Data Science .
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How to turn simple head-to-head choices Into probabilistic rankings Much of statistical learning assumes the availability of absolute labels. They may hesitate to assign an absolute quality score to a candidate, but they can say which of two candidates seems stronger. When item i is compared with item j, the probability that i is preferred to j is defined as:
TL;DR:
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Towards Data Science .
Kdnuggets
May 27, 14:00
Pandas GroupBy Explained With Examples
Learn how to use Pandas GroupBy to summarize, compare, and analyze grouped data with simple, practical examples.
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For example, if you are working with sales data, you may want to calculate total revenue by region, average order value by product category, or the number of orders handled by each sales representative.
TL;DR:
Learn how to use Pandas GroupBy to summarize, compare, and analyze grouped data with simple, practical examples.
Towardsdatascience
May 27, 13:30
Most AI Agents Fail in Production Because They’re Built Backwards
Good models don't save bad architecture, and most teams learn that the hard way. The post Most AI Agents Fail in Production Because They’re Built Backwards appeared first on Towards Data Science .
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Most AI Agents Fail in Production Because They’re Built Backwards. Good models don't save bad architecture, and most teams learn that the hard way. The post Most AI Agents Fail in Production Because They’re Built Backwards appeared first on Towards Data Science .
TL;DR:
The post Most AI Agents Fail in Production Because They’re Built Backwards appeared first on Towards Data Science .
Kdnuggets
May 27, 12:00
5 Scipy.stats Tricks for Simulating ‘What If’ Scenarios
In this article, we will take a look under the hood of scipy.stats, exploring five essential tricks to design high-performance, rigorous simulations using only NumPy and SciPy.
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Data is rarely static. As a data scientist, you are frequently asked to stress-test business assumptions, explore distributional uncertainty, or simulate alternative realities. Answering these what-if questions requires moving from simple point estimates (like the simple mean) to robust, probabilistic thinking.
TL;DR:
In this article, we will take a look under the hood of scipy.stats, exploring five essential tricks to design high-performance, rigorous simulations using only NumPy and SciPy.
Machinelearningmastery
May 27, 12:00
The Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough
When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
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Share Post Share In this article, you will learn how logits, temperature, and top-p sampling work together to control next-token prediction in large language models. In particular, we will explore how raw model scores, known as logits , interact with two other model settings — temperature and top-p — which are three key parameters utilized to control the token selection proces…
TL;DR:
When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
Towardsdatascience
May 27, 12:00
They Requested It. I Built It. Nobody Ever Used It.
Why good data work gets ignored after delivery. The post They Requested It. I Built It. Nobody Ever Used It. appeared first on Towards Data Science .
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Why good data work gets ignored after delivery. appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Towardsdatascience
May 26, 16:30
What Is a Data Agent?
A simple explanation of what a data agent is and how it works The post What Is a Data Agent? appeared first on Towards Data Science .
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What Is a Data Agent?. A simple explanation of what a data agent is and how it works The post What Is a Data Agent? appeared first on Towards Data Science .
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appeared first on Towards Data Science .
Towardsdatascience
May 26, 15:00
The AI Model Confidence Trap
Why your AI model can be wrong with 99% confidence The post The AI Model Confidence Trap appeared first on Towards Data Science .
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Last year, I was feeling a bit whimsical on a Saturday and decided to ask ChatGPT a fairly simple question: “ Who won the Nobel Prize in Physics in 2025? ” ChatGPT responded immediately: “ The 2025 Nobel Prize in Physics was awarded to… ” It even provided names, research areas, and an explanation of the specific research that earned them the Nobel Prize!
TL;DR:
Why your AI model can be wrong with 99% confidence The post The AI Model Confidence Trap appeared first on Towards Data Science .
Kdnuggets
May 26, 14:00
Visual Debugging Tools for Machine Learning Workflows
In this article, we cover three topics: what to visualize during training, the tools that provide those visualizations, and the methods to capture model computations directly using hooks and breakpoints.
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When training a model, the loss curve is usually the first thing to check. When validation loss starts rising while training loss keeps falling, the model is overfitting. When both curves plateau early, the model isn't learning, which typically indicates a problem with the data or learning rate.
TL;DR:
In this article, we cover three topics: what to visualize during training, the tools that provide those visualizations, and the methods to capture model computations directly using hooks and breakpoints.
Towardsdatascience
May 26, 13:30
Stop Using LLMs Like Giant Problem Solvers
How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents The post Stop Using LLMs Like Giant Problem Solvers appeared first on Towards Data Science .
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The brute force approach was obvious: give the agent the source text, explain the task, provide examples, and ask it to generate the rules. Some rules were too broad, others were missed. Hopefully, these insights will be useful if you’re building AI systems that need to scale, stay reliable, and deal with messy data.
TL;DR:
How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents The post Stop Using LLMs Like Giant Problem Solvers appeared first on Towards Data Science .
Kdnuggets
May 26, 12:00
Top 7 Python Libraries for Large-Scale Data Processing
This article covers Python libraries that make large-scale data processing faster, more scalable, and easier to manage across modern data workflows.
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Python has a super rich ecosystem of libraries for handling data at scale. This article covers libraries that handle: PySpark is the Python API for Apache Spark , the industry standard for distributed large-scale data processing. It breaks data into chunks and builds a task graph that executes lazily, on a single machine or across a cluster.
TL;DR:
This article covers Python libraries that make large-scale data processing faster, more scalable, and easier to manage across modern data workflows.
Towardsdatascience
May 26, 12:00
The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment
How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment. The post The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment appeared first on Towards Data Science .
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The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment. How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment.
TL;DR:
The post The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment appeared first on Towards Data Science .
Towardsdatascience
May 25, 17:37
I Built My First ETL Pipeline as a Complete Beginner. Here’s How.
A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API The post I Built My First ETL Pipeline as a Complete Beginner. Here’s How. appeared first on Towards Data Science .
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I Built My First ETL Pipeline as a Complete Beginner. A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API The post I Built My First ETL Pipeline as a Complete Beginner. appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Towardsdatascience
May 25, 17:15
Can AI Write Your Code?
What a recent study on ChatGPT, Python, R, and Stata tells us about AI-assisted coding for causal inference The post Can AI Write Your Code? appeared first on Towards Data Science .
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Can AI Write Your Code?. What a recent study on ChatGPT, Python, R, and Stata tells us about AI-assisted coding for causal inference The post Can AI Write Your Code? appeared first on Towards Data Science .
TL;DR:
appeared first on Towards Data Science .
Kdnuggets
May 25, 14:00
Auditing Model Bias with Balanced Datasets with Mimesis
Learn how to use Mimesis library to generate a balanced, counterfactual dataset that helps analyze potential bias in your models.
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But in a high-stakes scenario or one where data is sensitive, how can we audit whether a model is biased without compromising real-world information? This hands-on article guides you in training a simple classification model for "loan approval" on biased data.
TL;DR:
Learn how to use Mimesis library to generate a balanced, counterfactual dataset that helps analyze potential bias in your models.
Towardsdatascience
May 25, 13:30
From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python. The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search appeared first on Towards Data Science .
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How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python. The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search appeared first on Towards Data Science .
TL;DR:
The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search appeared first on Towards Data Science .
Kdnuggets
May 25, 12:00
5 More Must-Know Python Concepts
Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit. Python is eating the world .
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Python is eating the world . Since its introduction over 35 years ago, Python has successfully bullied its way into the hearts of programmers the world over. This has helped make it one of the go-to languages of data science, machine learning and AI.
TL;DR:
Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit.
Machinelearningmastery
May 25, 12:00
Implementing Hybrid Semantic-Lexical Search in RAG
Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-ready solutions.
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Share Post Share In this article, you will learn how to implement a hybrid search strategy for RAG systems by combining BM25 lexical search with semantic search, fused together using Reciprocal Rank Fusion. However, lexical, keyword-based search with approaches like BM25 covers a small blind spot neglected by semantic search.
TL;DR:
Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-ready solutions.
Towardsdatascience
May 25, 12:00
Introducing the Agent Toolkit for Amazon Web Services
It’s like having your own personal expert AWS solutions architect and data engineer rolled into one. The post Introducing the Agent Toolkit for Amazon Web Services appeared first on Towards Data Science .
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Introducing the Agent Toolkit for Amazon Web Services. It’s like having your own personal expert AWS solutions architect and data engineer rolled into one. The post Introducing the Agent Toolkit for Amazon Web Services appeared first on Towards Data Science .
TL;DR:
The post Introducing the Agent Toolkit for Amazon Web Services appeared first on Towards Data Science .
Towardsdatascience
May 24, 17:00
The Ultimate Beginners’ Guide to Building an AI Agent in Python
Simple step-by-step tutorial to building an AI agent in Python The post The Ultimate Beginners’ Guide to Building an AI Agent in Python appeared first on Towards Data Science .
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Simple step-by-step tutorial to building an AI agent in Python Introduction to AI Agents Agentic AI is the new buzzword of the decade. But first, what exactly are AI Agents? We can ask the same question to both a chatbot and an AI Agent.
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Simple step-by-step tutorial to building an AI agent in Python The post The Ultimate Beginners’ Guide to Building an AI Agent in Python appeared first on Towards Data Science .
Towardsdatascience
May 24, 13:00
Beyond the Model: Why Data Scientists Must Embrace APIs and API Documentation
Unlock the power of API for data-driven solutions The post Beyond the Model: Why Data Scientists Must Embrace APIs and API Documentation appeared first on Towards Data Science .
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Introduction As data scientists work at the intersection of various domains — statistics, programming, AI — the ability to convey complex methodologies and insights becomes crucial. Finally, as Data Science becomes increasingly integrated into business strategies, well-documented APIs can improve the scalability of data solutions and simplify the process of working with data.
TL;DR:
Unlock the power of API for data-driven solutions The post Beyond the Model: Why Data Scientists Must Embrace APIs and API Documentation appeared first on Towards Data Science .
Towardsdatascience
May 23, 17:00
How to Mathematically Choose the Optimal Bins for Your Histogram
Optimal Resolution in Histograms: A Rigorous Bayesian Approach to Density Fitting The post How to Mathematically Choose the Optimal Bins for Your Histogram appeared first on Towards Data Science .
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While histograms are the most fundamental tool for data visualization, setting their resolution is important, especially when the histogram itself is used for further analyses. In this post, we explore the mathematics of density fitting, specifically looking at how bins should shrink as our dataset grows.
TL;DR:
Optimal Resolution in Histograms: A Rigorous Bayesian Approach to Density Fitting The post How to Mathematically Choose the Optimal Bins for Your Histogram appeared first on Towards Data Science .
Towardsdatascience
May 23, 15:00
Beyond the Scroll: How Social Media Algorithms Shape Your Reality
An intro to recommender systems The post Beyond the Scroll: How Social Media Algorithms Shape Your Reality appeared first on Towards Data Science .
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You’ve probably felt that your social media feed may know you too well. When you browse social media, you notice a very typical behavior: you watch one video, and suddenly your timeline is flooded with more of the same. It does this based on one word: data.
TL;DR:
An intro to recommender systems The post Beyond the Scroll: How Social Media Algorithms Shape Your Reality appeared first on Towards Data Science .
Machinelearningmastery
May 22, 12:00
Building Context-Aware Search in Python with LLM Embeddings + Metadata
Keyword search breaks the moment a user types something a document doesn't literally say.
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Building Context-Aware Search in Python with LLM Embeddings + Metadata. Keyword search breaks the moment a user types something a document doesn't literally say.
TL;DR:
Keyword search breaks the moment a user types something a document doesn't literally say.
Kdnuggets
May 22, 12:00
Easy Agentic Tool Calling with Gemma 4
In this tutorial, we will give Gemma 4 two new tools and watch the model decide, on its own, when to look around and when to compute.
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In a recent article on Machine Learning Mastery, we built a tool-calling agent that reached outward , that is pulling weather, news, currency rates, and time from public APIs. It could be argued that this is closer to truly "agentic." This article picks up where that one left off. I highly recommend that you first read this article before continuing on.
TL;DR:
In this tutorial, we will give Gemma 4 two new tools and watch the model decide, on its own, when to look around and when to compute.
Kdnuggets
May 21, 14:00
System Design Interview Questions: A Handy Collection
Ace system design interviews with 10 GitHub repositories packed with fundamentals, proven patterns, and real questions to help you design scalable systems with confidence.
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Even as AI can now generate huge amounts of code, system design remains one of the few skills that cannot be easily replaced. Writing code is only one part of building real products. From complete primers and interview question collections to visual explainers and specialized guides for mobile and frontend system design, these GitHub repositories have helped many candidates pr…
TL;DR:
Ace system design interviews with 10 GitHub repositories packed with fundamentals, proven patterns, and real questions to help you design scalable systems with confidence.
Machinelearningmastery
May 21, 12:00
How to Build a Multi-Agent Research Assistant in Python
I have been experimenting with the OpenAI Agents SDK, and it has quickly become one of my favorite ways to build agentic AI applications.
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Share Post Share In this article, you will learn how to build a multi-agent AI research assistant using the OpenAI Agents SDK, the GPT-5.4 mini model, and the Olostep Web API, including how to wire together a manager agent, specialist sub-agents, and live web tools to produce structured, source-grounded research reports.
TL;DR:
I have been experimenting with the OpenAI Agents SDK, and it has quickly become one of my favorite ways to build agentic AI applications.
Machinelearningmastery
May 20, 14:15
Agentic Programming: A Roadmap
Share Post Share In this article, you will learn what agentic programming is, how production-grade AI agents are built from the ground up, and what it takes to go from zero experience to shipping a r…
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Share Post Share In this article, you will learn what agentic programming is, how production-grade AI agents are built from the ground up, and what it takes to go from zero experience to shipping a real agent in production. Those two data points sit in the same market.
TL;DR:
Here is the number that defines the current state of things: <a href="https://svitla.
Machinelearningmastery
May 19, 12:00
Prompt Engineering for Agentic AI
Share Post Share In this article, you will learn how prompt engineering changes fundamentally when applied to agentic AI systems, and what principles and patterns enable reliable agent behavior at sc…
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Share Post Share In this article, you will learn how prompt engineering changes fundamentally when applied to agentic AI systems, and what principles and patterns enable reliable agent behavior at scale. That knowledge is genuinely useful, and it will take you only so far once you move into agentic AI. This article is about the second thing.
TL;DR:
You have probably spent time learning how to prompt AI well.
Machinelearningmastery
May 18, 13:45
Building Vector Similarity Search in PostgreSQL with pgvector
Search works well when users know exactly what they are looking for, but it breaks down when intent is described in natural language.
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Share Post Share In this article, you will learn how to implement vector similarity search in PostgreSQL using the pgvector extension, allowing you to find semantically similar results based on meaning rather than keyword matching. This is where similarity search becomes useful. This article shows how to implement similarity search in PostgreSQL using pgvector .
TL;DR:
Search works well when users know exactly what they are looking for, but it breaks down when intent is described in natural language.
Machinelearningmastery
May 13, 12:00
Choosing the Right Agentic Design Pattern: A Decision-Tree Approach
Share Post Share In this article, you will learn how to apply a structured decision tree to choose the right agentic design pattern for any AI system you are building.
TL;DR:
Share Post Share In this article, you will learn how to apply a structured decision tree to choose the right agentic design pattern for any AI system you are building.