Discover the Best AI Resources

nvidia/PhysicalAI-Autonomous-Vehicles

Physics-aware simulated sensor dataset for training and evaluating autonomous-vehicle perception and control models. Includes multimodal sensor streams with physical-scene annotations intended for tasks that require grounding in real-world dynamics.

ccusage

Parses the local JSONL logs that coding-agent CLIs write and turns them into token and cost reports, no API keys or telemetry. Breaks spend down by day, month, session, and Claude's 5-hour billing windows across Claude Code, Codex, Gemini CLI and more.

Claude-Flow

Wraps Claude Code as an MCP server that orchestrates 100+ specialized agents into self-organizing swarms — hierarchical, mesh, or adaptive consensus — backed by persistent vector memory, coordination hooks, and secure cross-machine federation.

ruflo

Wraps Claude Code and Codex with an execution harness that turns one coding agent into coordinated swarms. A single init command adds ~98 agents, an MCP tool server, cross-session vector memory, and cross-machine federation.

Models.dev

Provides a community-curated database of AI model metadata—specs, pricing, and capabilities—and exposes it via a JSON API and a TOML-based contributor workflow for programmatic lookup and integration.

All-in-RAG

Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.

RAG-Anything

Extends RAG beyond text: parses PDFs and Office files containing images, tables, equations, and charts, then queries them through one multimodal knowledge graph. Built on LightRAG, it replaces separate parsing and retrieval tools.

Claude Context

Provides semantic code search for AI coding agents by making an entire codebase available as context via hybrid BM25 + vector retrieval, reducing token costs. Uses incremental indexing, AST-based chunking, and Zilliz/Milvus-backed vectors for large-codebase and IDE workflows.

n8n-MCP

Gives AI coding assistants a queryable index of n8n's 2,000+ workflow nodes — their real properties, operations, and 2,300+ templates — so generated workflow JSON validates instead of hallucinating node names and connections.

π RuView

Turns commodity WiFi Channel State Information into spatial sensing: 17-keypoint pose estimation, presence detection, and contactless breathing/heart-rate monitoring through walls, with no camera. Runs on a mesh of ESP32-S3 nodes (~$9 each).

Nano-vLLM

Reimplements the vLLM inference engine from scratch in ~1,200 lines of readable Python, matching its offline throughput on small models. Prefix caching, tensor parallelism, torch.compile, and CUDA graphs are all kept legible.

Nemotron-Personas-USA

1,000,000 US-focused synthetic persona records (6M persona texts) grounded to demographic, geographic and personality distributions. Contains age, sex, education, occupation and ZCTA/city fields; CC BY 4.0 license for LLM training, diversity augmentation, and bias mitigation.