AI Tools & Frameworks Directory

Agents got good. The hard part now is everything around them. The post Your Agent Doesn't Need More Freedom appeared first on Towards Data Science.

A four-level technical roadmap for building agentic AI systems, from single cognitive loops and stateful memory through multi-agent swarms and production observability.

Real repositories, seeded agent decisions: what 50,400 runs show about wasted turns, patch churn, and which harness rules help The post Coding Agents Can Solve the Task — But How Much Work Did They Waste? appeared first on Towards Data Science.

Five AutoHotkey v2 scripts that handle repetitive Windows desktop work around data analysis: launching a project workspace, converting clipboard IDs to Python or SQL lists, fixing file paths, and snapping windows into place.

In this article, you will learn how to build a lightweight, end-to-end RAG pipeline using scikit-learn for vector retrieval and open-source models for embeddings and...

Re-reading my own IBM patent on real-time concert feedback as what it actually was: an agentic loop with multimodal perception, memory-grounded inference, and a human in the loop. What it got right, and what a decade of hindsight teaches agent builders today. The post We Specified an AI Agent in 201...

Explore 10 Hugging Face models used across text, vision, audio, and retrieval in 2026.

In this article, you will learn what the major AI agent benchmarks measure, how they differ from traditional model benchmarks, and how to use them...

Three vision AI models ignored the pretty image, yet GPT-4o invented answers and Claude kept answering B. The post I Built a Trap for AI Image Judges. They Walked Into a Different One. appeared first on Towards Data Science.

Add architectural guardrails around how agents read untrusted sources and use them in the workflow. The post How Can AI Agents Read Untrusted Sources Safely? appeared first on Towards Data Science.

How a decision-first model can catch risky tool calls before they turn into real-world actions The post Can TypeSafe's Jev Make AI Agents Safer Without Another LLM? appeared first on Towards Data Science.

MCP just got simpler. Learn how to build a stateless MCP server in Python and expose tools, resources, and prompts over HTTP.

The cost of control The post Where Does the Money Go Across Long-Running Coding Agents? appeared first on Towards Data Science.

EmbeddingGemma 2 launched on October 6, 2026 under Apache 2.0. It is a sub-1B model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space.  This article covers the architecture, the benchmarks, and runnable scripts to provide measured results.  Specifications ...

AI Agents for Beginners, Part 1 The post Build Your First AI Agent with One Tool Call appeared first on Towards Data Science.

Build a production-grade data science toolkit without spending a dollar, using open-source AI tools that match, and sometimes exceed, their paid alternatives.

Most discussions about AI SEO are still focused on content.How do I get cited by ChatGPT?How do I optimize for Perplexity?What format does an LLM prefer?I think that may only be the first layer.The deeper shift is structural.Today’s websites are largely organized around pages.But an AI agent is tryi...

Your models can be both cutting-edge and compliant if you stop treating governance as the final hurdle and start building it into every stage of the ML pipeline.

Docker Agent is an open-source, Apache 2.0-licensed CLI plugin built by Docker Engineering, installed and run as docker agent. Its own tagline states the goal plainly: run AI agents like containers.

AI agents can now write CUDA kernels that outperform PyTorch—but proving those speedups are real is the harder problem. I put them to the test on an NVIDIA DGX Spark and found that benchmark design matters just as much as the code. The post AI Agents Beat PyTorch: Writing Faster CUDA Kernels appeare...

A diffusion head for probabilistic time series forecasting: why a forecast can get the mean and the variance right and still describe a future that never happens, and how to fix it. The post Your Model's MSE Is Lying to You III: Time Series Diffusion appeared first on Towards Data Science.

An exploration of the new data science roles emerging after the Large Language Model explosion. The post AI Engineers, Applied Scientists, Forward Deployed Engineers: Let's Unpack. appeared first on Towards Data Science.

A well-built MMM can still be wrong, because the spend it learns from never held the information it needs. This article tests whether changing when you spend, not how much, can fix that. The post How Wrong Is Your Marketing Mix Model (MMM)? appeared first on Towards Data Science.

Same job postings, different titles. Here's what each role actually builds.