Discover the Best AI Resources
Curated essentials, no noise — just what matters
Provides modular, AI-optimized SKILL.md instructions that ground LLMs in Android development workflows and can be installed into agent environments via the Android CLI; focuses on tasks where LLMs underperform.
Provides modular “skills” that help designers and engineers audit, find, and improve UI animations and interaction decisions — optimized for use by AI agents or human-in-the-loop workflows. Distills domain-expert rules into actionable SKILL.md modules (review, improve, find opportunities, vocabulary).
Transforms articulated 3D asset creation into a programmatic, LLM-driven code-generation workflow that produces objects with semantic parts, robust geometry, and physical joints. Includes CLI generation, a local viewer, and pipelines for large-scale dataset contribution.
Orchestrates parallel CLI-based AI agents in isolated git worktrees so you can run multiple coding agents side-by-side, review AI-generated diffs, and link PRs/CI to each worktree. Desktop client with a mobile companion and BYO model subscriptions.
Pretrained image-model checkpoint hosted on Hugging Face by Facebook (Meta) for vision experiments and transfer learning. Includes downloadable weights and metadata under CC BY‑NC 4.0 — suitable for research and prototyping but restricted for commercial use.
Orchestrates LLM-powered coding agents in isolated sandboxes to automate code edits and review pipelines. Provider-agnostic (Docker, Podman, Vercel), supports branch strategies, session capture, reusable sandboxes and structured outputs.
Review-first terminal diff viewer that opens changesets in an interactive TUI with multi-file review stream, sidebar navigation, and inline AI/agent annotations. Supports split/stack responsive layouts, watch mode, and Git/Jujutsu pager integration.
Evaluates job postings and produces tailored CVs, cover letters, and interview prep using a Claude Code-driven agent workflow. Distinguishes itself with a drafter–reviewer loop, mandatory PDF compilation and ATS text-layer verification, plus extensible portal scrapers and LaTeX templates.
Hands-on, phase-based curriculum for building end-to-end AI systems from first principles — implement algorithms, run tests, and ship reusable artifacts (prompts, skills, agents, MCP servers) across Python, TypeScript, Rust, and Julia under an MIT license.
Centralizes trip planning on a self-hosted server: collaborative itinerary editor with interactive maps, expenses, packing lists, reservations and PWA/offline support. Includes a built-in MCP (Model Context Protocol) server so AI assistants can read and automate trips via OAuth 2.1.
A 228,557-example dataset of reasoning traces segmented into blocks with iterative, compressed "memento" summaries so LLMs can learn to manage long context. Includes a training-ready subset and a `full` subset with sentence/block-level annotations for research and SFT.