Discover the Best AI Resources
Curated essentials, no noise — just what matters
A 26M-parameter LLM distilled for reliable function-call generation on tiny devices, with open weights, local finetuning tooling, and a web playground for on-device testing. Pretrained at scale then post-trained on a single-shot function-call dataset for tool integration.
One-command installer that gives AI agents the ability to read and search the web and social platforms (web pages, Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaohongShu) by installing and wiring upstream CLIs and MCP connectors while keeping credentials local.
Provides a deployable personal AI assistant that runs locally or in the cloud, supports multi-channel chat, extensible Skills/Plugins, and local-model runtimes. Key features include three-layer memory, kernel-level sandboxing and tool/file guards, and bundled QwenPaw-Flash local models for zero-API deployments.
Runs autonomous AI agents in isolated containers with declarative YAML policies that enforce filesystem, network, process, and inference controls. Provides credential providers, inference routing, hot-reloadable network/policy updates, and formal verification of policy changes for auditable agent execution.
Paired brain MRI scans and radiology text annotations for multimodal vision–language research. Provides image-level labels and image–text pairs suited for VQA, classification, and image-to-text tasks; CC BY-NC-SA 4.0 and ~10K–100K samples — research/non-commercial use.
Runs local AI models on Apple Silicon as an OpenAI‑compatible server, emphasizing low latency, prompt caching, and reliable tool-calling. Optimized for M1–M4 Macs with multimodal support and drop‑in compatibility for IDEs and agent frameworks.
Unmixes green‑screen pixels with a neural model to recover straight (unmultiplied) foreground color and a clean linear alpha for every pixel, preserving hair, motion blur and translucency. Produces VFX‑standard EXR outputs, supports optional AlphaHint generators (GVM/VideoMaMa) and Docker/consumer‑GPU optimizations.
Converts scene intent into production-ready Seedance 2.0 prompts, reference-role mappings, and IP-safe rewrites for multimodal (text/image/audio/video) video generation. Ships as a modular agent-skill OS with multilingual examples, troubleshooting tools, and pro filmmaker handoff artifacts.
Provides a set of task-focused agent “skills” — small folders of instructions that teach agents how to perform common Flutter development workflows (integration tests, widget previews, routing, localization). Maintained by the Flutter team to reduce mistakes and make repeatable dev tasks reliable.
Provides persistent, searchable memory for coding agents (Claude Code, Cursor, Gemini CLI, etc.), automatically capturing tool usage and session facts. Combines BM25, vector embeddings and a knowledge graph for hybrid retrieval, reducing token costs and re-explaining between sessions.
Provides a structured library of 754 cybersecurity skills (agentskills.io format) mapped to MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND and NIST AI RMF — so AI agents can load practitioner workflows and decision logic across 20+ platforms.