AI Tools & Frameworks Directory

How to run the backend locally with Docker or in the cloud The post Connecting My LangGraph AI Agent to Postgres appeared first on Towards Data Science.

As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter? The post Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch appeared first on Towards Data Science.

What happens when you stop feeding a model context and let it go find its own, walking a knowledge graph within strict limits, and what four models and one wrong prediction revealed about whether that is worth doing. The post Stop Giving Your AI Agent a Search Box and Start Giving It Typed Tools, Ha...

Enterprise Document Intelligence [Vol.1 #2D] - What data scientists say when asked, what the model actually does under the hood, and why the honest answer changes your architecture decisions in enterprise RAG The post How Does a RAG Reranker Really Work? appeared first on Towards Data Science.

Why most agents are just flowcharts in disguise, and what to build instead. The post Is Agentic AI Just Automation? appeared first on Towards Data Science.

We're excited to share some big news: We completely rebuilt the TDS website and our contributor portal. Whether you come here to read, to write, or both, here's what you can expect from our new site. The post A New Towards Data Science: A Faster Site and a Brand-New Contributor Portal appeared first...

Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.

A practical guide to navigate hierarchies, find routes, detect cycles and calculate degrees of separation The post Recursive CTEs: SQL’s Hidden Graph Traversal Engine appeared first on Towards Data Science.

Understanding Codex hooks The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.

Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch. The post Can an LLM Forget the Right Things? ...

Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acc...

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...

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...

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.

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 ...

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.

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 ...