AI Tools & Frameworks Directory

A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example The post The Medallion Data Architecture: An Introduction appeared first on Towards Data Science.

This chapter is divided into eight parts; they are: • Metrics for LLM Inference • Measuring a Single Request • Warmup and Synchronization • Measuring GPU Work with CUDA Events • Measuring Memory Usage • Measuring Concurrent Requests • Multiple GPUs and Multiple Machines • Cost per Token The most com...

From quantization to speculative decoding, here are seven engineering strategies to ship faster, more responsive generative AI applications in production.

Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops). Knowing which layer you are standing on is half of build...

Read about MiniMax's own architecture, and see how it runs a real task against the actual API. Learn the pieces of the MiniMax story that weren't covered in the launch post.

This chapter is divided into nine parts; they are: • Reading Logits from a Model • Greedy Decoding • Temperature Sampling • Top-$k$ Sampling • Nucleus Sampling • Repetition Penalties • Beam Search • Stop Conditions • Structured Output Constraints The model returns a vector of logits for every positi...

How use Claude to craft an outstanding resume that lands offers The post How Claude Help Me Build My $200k+ ML Resume appeared first on Towards Data Science.

A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse. The post I Replaced a 15-Minute Booking Process with a LangGraph AI Agent appeared first on Towards Data Science.

Imagine hiring an AI assistant to handle important tasks, only to find that it quietly ignores your instructions because it believes it knows better. This is known as agentic misalignment, where an AI intentionally pursues its own objective instead of the one set by its operator. To understand how o...

Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mi...

A Beginner's Guide to Working with Claude Design • 5 Best AI Tools for Data Analysis You Should Try in 2026 • 5 Books That Will Deepen Your Understanding of Large Language Models • Is KimiClaw a Useful Tool?

A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior The post Put the Agent Inside the Workflow appeared first on Towards Data Science.

How a seemingly harmless move to a multi-agent architecture quietly tripled our LLM costs and what actually fixed it. The post The 3× Token Bill We Didn’t See Coming appeared first on Towards Data Science.

This chapter is divided into four parts; they are: • Autoregressive Generation • Prefill and Decode • A Simple KV Cache • Memory Usage of the KV Cache A decoder-only transformer model predicts the next token from the tokens that came before it.

Building a voice-controlled AI agents isn't hard, this article breaks the pipeline into its real components: streaming speech recognition, turn detection, streaming generation, interruption handling, and tool calling under voice constraints and shows what each one is responsible for.

Check out these five books on building, fine-tuning, and deploying large language models.

July 2026 was the busiest month for frontier model releases the field has seen. Four major labs shipped flagship or near-flagship models, two well funded newcomers shipped their first, and the largest open weight model ever published went up for download, all inside thirty one days. Read as a list, ...

I used Claude Code daily for months before realizing that claude --help hides many of its most useful capabilities. I kept restarting fresh sessions, repeatedly explaining the same project structure, simply because I did not know a better workflow existed. While debugging an unrelated issue, I disco...

In this article, you will learn the seven architectural components that separate a production-grade agentic AI system from a demo script, and how each one...

Discover 7 essential machine learning algorithms that every data scientist should know before reaching for LLMs and generative AI, with simple explanations and practical Python code.

Prompt engineering helps you write better prompts—but it doesn’t help you change them safely. This article explores a common production failure where a simple variable rename breaks every live call, and introduces a lightweight static analysis tool that treats prompts like contracts, catching breaki...

Learn why clear business goals, data quality, simple models, careful validation, realistic costs, and human judgment matter more than chasing the latest technology.

Building an ALNS heuristic in Python for vehicle routing, time windows, capacity constraints, and mandatory driver breaks. The post Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search appeared first on Towards Data Science.