Discover the Best AI Resources
Curated essentials, no noise — just what matters
Indexes codebases into a persistent, queryable knowledge graph for AI coding agents, enabling full-repo indexing in minutes and sub-millisecond structural queries. Bundles 158 vendored tree-sitter grammars, a Hybrid LSP resolver, built-in embeddings, and 14 MCP tools for search, trace, and architecture analysis.
A 26M-parameter LLM distilled for reliable function-call generation on tiny devices, with open weights, local finetuning tooling, and a web playground for on-device testing. Pretrained at scale then post-trained on a single-shot function-call dataset for tool integration.
One-command installer that gives AI agents the ability to read and search the web and social platforms (web pages, Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaohongShu) by installing and wiring upstream CLIs and MCP connectors while keeping credentials local.
Paired brain MRI scans and radiology text annotations for multimodal vision–language research. Provides image-level labels and image–text pairs suited for VQA, classification, and image-to-text tasks; CC BY-NC-SA 4.0 and ~10K–100K samples — research/non-commercial use.
Runs local AI models on Apple Silicon as an OpenAI‑compatible server, emphasizing low latency, prompt caching, and reliable tool-calling. Optimized for M1–M4 Macs with multimodal support and drop‑in compatibility for IDEs and agent frameworks.
Unmixes green‑screen pixels with a neural model to recover straight (unmultiplied) foreground color and a clean linear alpha for every pixel, preserving hair, motion blur and translucency. Produces VFX‑standard EXR outputs, supports optional AlphaHint generators (GVM/VideoMaMa) and Docker/consumer‑GPU optimizations.
Provides a set of task-focused agent “skills” — small folders of instructions that teach agents how to perform common Flutter development workflows (integration tests, widget previews, routing, localization). Maintained by the Flutter team to reduce mistakes and make repeatable dev tasks reliable.
Provides persistent, searchable memory for coding agents (Claude Code, Cursor, Gemini CLI, etc.), automatically capturing tool usage and session facts. Combines BM25, vector embeddings and a knowledge graph for hybrid retrieval, reducing token costs and re-explaining between sessions.
Provides a structured library of 754 cybersecurity skills (agentskills.io format) mapped to MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND and NIST AI RMF — so AI agents can load practitioner workflows and decision logic across 20+ platforms.
Provides a suite of Claude Code skills that guide the full academic pipeline—research, write, review, revise, and finalize—while enforcing integrity gates (citation verification, anti-hallucination checks) and keeping a human-in-the-loop workflow.
Orchestrates autonomous coding agents to run isolated implementation tasks end-to-end: spawn runs from project boards that produce CI results, PR review feedback, complexity analysis, and walkthrough videos, and safely land accepted PRs. Experimental engineering preview for trusted environments; best for teams using harness engineering.