AI Tools & Frameworks Directory

Agents make dozens of small decisions before producing useful output: which source to trust, which tool to call, what context to keep and when to stop. Jev brings those hidden choices into the open by turning them into typed decisions with probabilities, instead of relying on free-form text alone.  ...

In this article, you will learn how to choose the right agentic AI framework for your production system by working through a structured decision-tree based...

Compare five AI coding assistants across real tasks to find which tool fits your workflow.

A practical framework for turning AI into a thinking partner The post How I Use AI to Learn New Topics Faster: An AI-Assisted Learning Framework appeared first on Towards Data Science.

The fight is usually framed as whether data centers get built. The harder question underneath it is "who pays and who decides?" The costs are intertwined.

A controlled test of Gemini, DeepSeek, ChatGPT, and Claude on leakage, reporting delays, promotion effects, and structural breaks. The post I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did. appeared first on Towards Data Science.

Meta has entered the AI agent race in a big way.

Most AI coding demos stop at task managers, weather apps, or simple chatbots. For this project, we take on something more demanding: building an enterprise customer-support platform that can investigate complaints, retrieve relevant policies, recommend resolutions, and keep risky actions behind huma...

A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it.

Trying to answer the question of "Can LLM agents discover?" through the lens of creativity The post Measuring the Creativity Potential of LLM Agents appeared first on Towards Data Science.

This is the Python material that does not get left behind. Almost none of it is replaced by a framework later. Learn it, and keep this cheat sheet close by as a handy reference.

Coordinating agent capability development with the application it powers The post Where the Agent Development Lifecycle Fits appeared first on Towards Data Science.

Reduce costs, improve response quality, and build leaner AI applications with these prompt engineering strategies.

Describe your data needs in plain language and turn websites into a continuous source of fresh data.

Docling takes documents in whatever inconsistent format they arrive in, and converts them into one unified, structured representation that both people and AI systems can work with reliably, rather than everyone downstream having to guess at what a wall of extracted text actually meant.

I traced 2,500 listing checks with Weights & Biases Weave, removed avoidable model work one change at a time, and scored every version against the same answers. The post Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches? appeared first on Towards Data Science.

Chain Visualization: Reading the Trace Waterfall The spans from the last section don't mean much as a raw list.

In this article, you will learn how to add a lightweight temporal reasoning layer to a Graph-RAG system so that it can distinguish fresh facts...

Every dev team has repeatable setup tasks: spinning up a new project, running a deployment checklist, generating a file structure, or typing the same instructions into Claude again and again. Claude Code helps reduce that friction with slash commands, including built-in commands and custom commands ...

OpenAI’s agents reached a proposed solution in 88 hours. But the human research that came before, and the controversy that followed, raise a harder question: what actually counts as an AI discovery?

Coding agents give us more time for discovery and our review practices should follow the analysis The post Insight Is Still the Currency of Data Science appeared first on Towards Data Science.

Ollama pulls model weights, keeps an HTTP server on port 11434, and hands any client an OpenAI-shaped endpoint pointed at your own machine. Learn how to manage, configure, and optimize using Ollama right here.

The real shift is bigger than productivity: AI is reshaping ownership, judgment, and the career path of data scientists. The post AI Made Data Scientists Faster. Now It’s Expanding the Job. appeared first on Towards Data Science.

This is a summary of, and insights into, what I found digging into the recently-released Superwhisper S1 family of voice-to-text models.