Discover the Best AI Resources
Curated essentials, no noise — just what matters
Packages reusable GitHub Copilot building blocks — agents, prompts, instructions, and skills — to make AI-assisted coding repeatable and standards-aligned for a team. Built around an RPI (Research, Plan, Implement) workflow in VS Code.
Discovers MCP servers already configured in Cursor, Claude, Codex and other editors, then calls their tools from TypeScript or the CLI. Can also turn any server into a standalone command-line tool or a typed TypeScript client.
Contains training, evaluation, and deployment code plus checkpoints for humanoid whole-body controllers (Decoupled WBC and GEAR‑SONIC). Includes C++ inference, VR teleoperation, data pipelines (Bones‑SEED) and Hugging Face checkpoints for research-to-robot workflows.
Automatically generates complete short-form videos from a single topic: drafts script with an LLM, produces AI images/video, synthesizes multilingual TTS (including voice cloning), adds background music, and composes the final video. Supports local ComfyUI/RunningHub or direct model APIs and customizable templates.
Visual, example-driven guide for using Claude Code: structured learning path, copy‑paste templates, and diagrams that show how to combine slash commands, hooks, subagents and MCP into production workflows.
Drives penetration testing from chat commands, orchestrating 100+ security tools through an MCP-native multi-agent engine on CloudWeGo Eino. Adds attack-chain graphs, risk scoring, and human-in-the-loop approval gates for authorized use.
Curated collection of 70 hands‑on cybersecurity projects, certification roadmaps and learning resources organized into Foundations/Beginner/Intermediate/Advanced tiers. Each project ships source code plus deep learn/ documentation; several focus on AI security (LLM prompt defenses, ML threat detection).
Provides adaptive workflow steering rules for AI coding agents to guide development across Inception, Construction, and Operations phases. Includes opt-in extensions (security, testing), IDE/agent integrations (Cursor, Kiro, Amazon Q, Copilot, Claude), and human-in-the-loop approval points.
Aggregates SEC EDGAR filings into raw files, parsed plaintext, and rich filing metadata for LLM training and retrieval. Includes ~8.05M filings (~590 GB, ~43B tokens), per-filing token counts, and parsed outputs; Apache-2.0.
Bundles your prompt and project files into a single context package and submits that bundle to one or multiple LLMs (GPT‑5.x, Gemini, Claude, etc.) via API or optional browser automation. Key features: multi-model runs, file-globbing and token-aware bundles, session lineage and replay, and a CLI-first workflow for code reviews, audits, and multi-model comparisons.
Runs recurring workplace tasks across 100+ tools (Slack, GitHub, Gmail, Notion, Linear) as scheduled sub-agents that triage errors, draft outreach, and compile daily briefs. Each run executes in an isolated Firecracker microVM with scoped permissions.
Turn plain-English requests into editable draw.io diagrams: the model writes the underlying draw.io XML, which renders live in an embedded canvas. Upload images, PDFs, or text to replicate, refine through chat, and roll back via version history.