Discover the Best AI Resources
Curated essentials, no noise — just what matters
Official MCP server for data.gouv.fr, France's national open-data portal: lets chatbots search datasets, query CSV/XLS via the Tabular API, and browse cataloged third-party APIs, all read-only over a public HTTP endpoint that needs no key.
Catalogs reusable Agent Skills for Codex — folders of instructions, scripts, and resources an agent loads to perform specific tasks. Tiers: .system (ships with Codex), .curated, and .experimental. Now deprecated in favor of OpenAI plugins.
Converts images (and other conditions) into high-fidelity, fully textured 3D assets using a 4B-parameter generative model and a field‑free sparse voxel format (O‑Voxel). Handles arbitrary topology, PBR materials, and near real-time mesh/voxel conversions; requires Linux and an NVIDIA GPU with >=24GB memory.
Collects ~200,000 human responses to 20 visual/semantic association questions (e.g., Bouba–Kiki), with per-response image options and demographic metadata — useful for cross‑cultural perception and evaluation of multimodal systems, but not guaranteed as a rigorously controlled experimental sample.
Forecasts how social scenarios might unfold by running multi-agent simulations: thousands of LLM agents with memory and personalities, seeded from real data, that you steer by injecting variables to 'rehearse the future' in a digital sandbox.
An open large language model pairing DeepSeek Sparse Attention (DSA) for cheaper long-context inference with a scaled RL pipeline. Authors claim parity with GPT-5, with a high-compute Speciale variant surpassing it and rivaling Gemini-3.0-Pro on reasoning.
A configuration layer for the OpenCode and Codex CLI coding agents. One install adds specialized sub-agents, lifecycle hooks, and bundled MCP servers — web search, docs lookup, code search — turning a bare agent into a harness for large codebases.
Orchestrates multi-model LLM agents and developer workflows as an OpenCode plugin — runs background specialists, LSP/AST-aware refactors, hash-anchored edits, and built-in MCPs. Designed for agent-driven code automation and multi-model orchestration.
Embeds into an app like SQLite, persisting to a local file with no server or separate process. Combines dense and sparse vectors, full-text search, and scalar filters in one hybrid query; C++ core with Python, Node, Go, Rust, and Dart bindings.
Embeds Claude Code into Obsidian so a Claude-based agent can read, edit, search, and run bash inside your vault. Features include inline edits with diff previews, image analysis, slash-commands, skills/plugins, MCP integrations, and plan-mode.
Normalizes wearable data — heart rate, sleep, activity, steps — from Garmin, Whoop, Apple Health and more behind one self-hosted API, so you write one integration instead of one per provider. Natural-language AI health automations are planned.