AI Tools & Frameworks Directory

AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries ex...

Enterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval questions at all The post Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File appeared fi...

I have written enough specs for Claude Code now to have hit the failure mode nobody warns you about.  The spec was fine. The plan was fine. Claude worked through the tasks, ran the test suite, and reported everything passing. I looked at the diff properly the next morning and found it had converted ...

Turning a demo agent into something that can keep real booking data The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels The post Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline appeared...

Agentic AI research in 2026 has moved past the basic question of whether a model can be called a tool. The harder questions are whether an agent can finish long workflows, survive live websites, verify its own work, recover from failure, and improve its process over time. The five papers below map t...

Turning Codex from an interactive assistant into a programmable automation component The post Running Codex as a Headless Agent appeared first on Towards Data Science.

A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science.

Run Muse Glimmer locally on an RTX 3090 GPU using llama.cpp, DFlash speculative decoding, and Pi for fast, private, agentic AI coding.

See how AI agents are autonomously handling support, coding, supply chains, healthcare, and fraud detection today.

What a production incident taught me about trusting a model to judge another model's work The post The LLM Judge That Kept Agreeing With Itself appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14A] - Three questions tell you which shape a document collection has, and each shape wants a different architecture The post Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One appeared first on Towards Data Science.

Why retrieval quality should be a property of the system, not of the question's wording? Rebuilding knowledge layer with graph traversal on every query, bitemporal edges, and two-threshold entity resolution. The post Making the Knowledge Layer a Graph You Actually Traverse appeared first on Towards ...

This article walks through five tools, one for each layer of the stack from building the agent's logic to running all of it at scale.

Conceptual overview and walkthrough of a solution approach in Python The post Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision appeared first on Towards Data Science.

Building the Responsible AI, security, and governance layers required for enterprise-ready agents The post From Prototype to Production: The Architecture Behind Secure & Governed AI Agents appeared first on Towards Data Science.

5 principles that determine whether an agent system succeeds in production, explained through one I built for a $100M+ company. The post Building Enterprise Agent Systems that People can Trust, Verify and Improve appeared first on Towards Data Science.

Download Ollama, pull and serve Qwen3.8-27B, and launch it with OpenCode using just three command lines.

Adding more communication pathways between agents doesn’t necessarily improve multi-agent performance. In a controlled, reproducible experiment across 50 runs, recovery remained remarkably stable from 20% to 100% relationship density. But as the network became denser, the fraction of edges actually ...

In this article, you will learn three practical strategies for managing small context windows in large language models, along with working Python examples that demonstrate...

Developers launch new models every week, but most barely change how you work. Kimi K3 is different—not because of benchmark charts, but because of a few small API changes that fundamentally affect how you use it. The first is reasoning_effort, which defaults to maximum, alongside 131,072 max_complet...

But by keeping these limits in mind, and planning for them, we can effectively use these small, local models for the following broad operations scenarios.

This article covers five Python libraries that turn tedious data cleaning into something expressive and genuinely enjoyable.

In this article, you will learn seven concrete regression tests for catching the orchestration-layer failure modes that matter most before deploying an AI agent to...