Discover the Best AI Resources
Curated essentials, no noise — just what matters
A curated collection of production-ready Agent Skills that turn tasks—presentation production, image generation, local KB retrieval, article assembly, and web-design—into agent-loadable skill folders. Uses a SKILL.md contract, supports multiple agent runtimes (Claude Code, Cursor, Codex), and offers modular install paths with pinned release zips.
Terminal-native AI coding agent that runs as a single static Go binary and preserves long LLM sessions using DeepSeek-aware prefix caching. Config- and plugin-driven: supports multiple providers, separate planner/executor sessions, and CLI/TUI, desktop and VS Code integrations.
Provides a large-scale multimodal embodied dataset (vision, depth, hand/arm kinematics, tactile) captured with an exoskeleton glove and egocentric sensors; organized as clip-level Zarr volumes for manipulation, imitation learning, and vision–action research. Includes both high-precision glove measurements and natural bare-hand clips; sizable storage required.
Provides a single OpenAI-compatible /v1 API that aggregates the free tiers of 16 LLM providers into one unified endpoint. Features smart routing and automatic failover, per-key free-tier tracking, encrypted key storage, embeddings/media routing, and a Docker one-liner for local use.
Terminal-first developer workspace with an agentic AI side-panel that runs against your API keys or local models. Bundles a native PTY terminal, CodeMirror editor with AI edit diffs, file explorer, git history/graph, and a web preview in a ~7–8MB desktop app with no telemetry.
Runs a local-first AI agent runtime that drives your desktop and automates workflows — browsing, file edits, approved shell commands, document extraction, and scheduled tasks — while integrating local or cloud LLMs (llama.cpp/TurboQuant) and embedding as an HTTP service or Tauri sidecar.
A 284B-parameter Mixture-of-Experts LLM with only 13B activated parameters, designed for 1,000,000-token contexts. Uses hybrid compressed attention and mixed FP4/FP8 precision to reduce long-context KV-cache and per-token FLOPs; aimed at long-document QA, RAG pipelines, and local/high-capacity inference.
Provides a locally runnable, refusal-free variant of Qwen3.6-27B with multiple K_P GGUF quantizations and mmproj multimodal support. The Aggressive flavor skips preambles on edgy prompts—use when you want direct/raw responses for local research, red‑teaming, or offline workflows.
A library of reusable agent skills that generate, inspect, and hand off CAD, robot-description, and fabrication artifacts. Exports STEP/STL/3MF, writes URDF/SDF/SRDF, slices meshes to G-code, previews files in-browser, and includes off-the-shelf STEP part lookup and benchmarks.
Generates conversational and reasoning outputs with support for million‑token contexts; uses a hybrid attention + MoE design to cut long‑context inference FLOPs and KV cache. Suited for long‑document retrieval, coding and complex reasoning; MIT licensed.
A vision-oriented foundation checkpoint for low-latency inference — DeepSeek V4 base in safetensors with FP8 optimizations. Designed for fast image generation and embedding use in inference pipelines; verify license and FP8/runtime compatibility before production use.
Provides instruction-based (before, after) structured 3D latents (SLAT) with aligned RGB views and natural-language edit prompts for training and evaluating instruction-following 3D editing models. Covers part-level semantic edits across seven edit types (deletion, addition, modification, scale, material, color, global) and supplies shard-based NPZ assets and loader code.