Discover the Best AI Resources

Code Puppy

Interactive terminal-native AI coding agent that generates, edits, and runs code while managing multiple model providers and optional durable execution. Privacy-first design with local model support, round-robin model distribution, and a flexible agent system for custom workflows.

memvid

Packages an AI agent's memory — data, embeddings, search indexes, and metadata — into one portable .mv2 file, replacing multi-service RAG stacks. Combines BM25 and HNSW search with temporal queries and sub-millisecond local reads, fully offline.

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.

MS MARCO Translations Dataset (ai4bharat/MSMARCO-XI)

Provides MS MARCO queries, passages and answers translated into 14 Indic languages while keeping the original English content and per-example translation metadata. Includes train/validation splits, passage selection flags, and translation model parameters for multilingual IR, QA and RAG research.

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.

Decepticon

Autonomous Red Team agent that plans and executes realistic attack chains (reconnaissance, exploitation, pivoting, C2) while producing Rules of Engagement and an OPPLAN and running actions inside a hardened sandbox; intended for authorized red-team engagements and defensive verification.

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.