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.
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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 .
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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 .
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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…
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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 .
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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.
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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 .
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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 .
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 .
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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 .
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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.
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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 .
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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 .
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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 .
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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.
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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 .
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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 .
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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 .
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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.
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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 .
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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.
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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 .
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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 .
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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 .
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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 .
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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 .
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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.
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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 .
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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 .
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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 .
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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…
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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.
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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 .
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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 .
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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 .
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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.
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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.