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Explore 9,800+ official and community MCP servers for Claude, Codex, Cursor, and other AI agents. Find the right tools and setup guides for your workflow.

Pull up-to-date, version-specific documentation and code examples for any library directly into Cursor, Claude Code, Codex, Devin Desktop, Antigravity and other AI coding tools.

Model-agnostic MCP server that indexes codebases with tree-sitter and lets any AI agent — Claude, GPT, Gemini — retrieve exact functions, classes, and constants instead of whole files. 95%+ token reduction, measured.

AI-assisted development changes when the source of truth shifts from code to specifications. Here’s why spec-driven development matters.

Hack the text output of Claude Code to make life a little bit sillier.

Mike Chambers explains why AI agents fail in production and why observability, evaluations, and the harness matter more than the model.

Think anarchy, not oligarchy.

Run hybrid queries in Qdrant: fuse dense, sparse, and multivector results with RRF or DBSF, layer custom scoring with Formula Query, and pick the right method for your data.

Learn how to turn text into numbers, unlocking use cases like search, clustering, and more with OpenAI API embeddings.

Enterprise Document Intelligence [Vol.1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context

When code gets cheap, the hard part is deciding what “correct” means and building a reliable way to check it.

A high-level approach for building AI products from the “outside in”

Coding agents dominate the conversation, but the harness your agent needs depends on its action and context complexity. Here’s how to build for the job in front of you.

Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why'

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 mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely.

A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.

One of the patterns I've found particularly helpful with LLMs is the diverge-then-converge pattern. LLMs excel at both of those things separately, and when we put them together, they are an excellent tool to help us solve problems.

AI tools accelerate output, but human engineers still need better structures, roles, and workflows to avoid cognitive exhaustion.

How post-training transformed language models with raw intelligence into the AI assistants used by billions of people today

Open source voice cloning. Local-first. Free forever.

Handy is a cross platform, open-source, speech-to-text application for your computer

Jake Van Clief, giving you the Cliff notes on the new AI age.

MCP servers are powerful: they let clients (IDEs, agents) interact with remote or local features and...

Thinking Machines' first open weights model is a 975 billion parameter alternative to Chinese LLMs

The seven layers that survive production, and the open source pick for each

Interpretability research on Claude's internal thoughts

AI-assisted development has made frontend work feel much faster. A developer can ask for a form, a dashboard card, a table, a modal, or a responsive layout and

Learn why vague prompts create hidden rework, how acceptance criteria help, and when BDD-style tests are worth the upfront effort.