AI Tools & Frameworks Directory

Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.

Compiling and running C code with Emscripten and GitHub Codespaces — no local installation required. The post Your First WebAssembly Program and Web App (Written, Tested, and Deployed Entirely in the Web Browser) appeared first on Towards Data Science.

How to build sentiment-aware word representations from IMDb reviews using semantic learning, star ratings, and linear SVM classification The post Learning Word Vectors for Sentiment Analysis: A Python Reproduction appeared first on Towards Data Science.

This article discusses how to implement an infrastructure for measuring and controlling overly verbose LLM responses.

Learn how to build an AI-powered Learning Management System from scratch using Ollama, FastAPI, and React. A step-by-step guide for beginner and intermediate developers.

Learn FastAPI through templates, examples, guides, auth tools, microservices, full-stack starters, and machine learning projects.

Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.

A step-by-step guide to understanding distributed data, lazy logic, and your first DataFrame. The post PySpark for Beginners: Mastering the Basics appeared first on Towards Data Science.

A practitioner's argument that meeting summarizers fail in the same way regressions fail when you skip the part where you ask what the data can support. The post LLM Summarizers Skip the Identification Step appeared first on Towards Data Science.

From tokenisation to evaluation :  how modern language models actually work in practice The post The Must-Know Topics for an LLM Engineer appeared first on Towards Data Science.

Three weeks into testing, a learner told me my AI tutor gave her the wrong answer. Not obviously wrong — just outdated enough to mislead. That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most ...

Memory shapes how humans think and how AI agents act. Without it, an agent only responds to the current input; with it, it can keep context, recall past actions, and reuse useful knowledge. AI memory spans short-term, episodic, semantic, and long-term memory, each with different design trade-offs ar...

The end of model-centric thinking in data science The post From Data Scientist to AI Architect appeared first on Towards Data Science.

Standard prompt attacks are merely the beginning. A structured framework to map and mitigate the backend attack vectors of agentic workflows.  The post The AI Agent Security Surface: What Gets Exposed When You Add Tools and Memory appeared first on Towards Data Science.

If you are feeding structured data into an LLM, there is a good chance you are paying a JSON tax.

A practitioner's guide to causal attribution when two churn drivers arrive at once. The post When Customers Churn at Renewal: Was It the Price or the Project? appeared first on Towards Data Science.

AI agents have evolved beyond passive chatbots.

How hook implementation gives Claude Code, Codex, and Cursor persistent memory via Neo4j, without locking you into any one of them. The post Unified Agentic Memory Across Harnesses Using Hooks appeared first on Towards Data Science.

If you’re an aspiring AI engineer looking to sharpen your skills, building AI agents is one of the most effective ways to get hands-on experience. AI agents represent practical applications of AI across domains, from personal assistants and recommendation systems to financial traders. Here are 10 AI...

A practical guide to modern type annotations in Python for data science The post The Joy of Typing appeared first on Towards Data Science.

Learn how to build a holistic pipeline for rigorous, statistical EDA, validating several important data properties.

Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language ...

Because there's only one reality to model! The post How Major Reasoning Models Converge to the Same “Brain” as They Model Reality Increasingly Better appeared first on Towards Data Science.

This article is a quick, everyday tour of seven distributions you'll actually recognize once you know what to look for. No heavy math. No gatekeeping.