Discover the Best AI Resources
Curated essentials, no noise — just what matters
Scaffolds production-ready GenAI agents on Google Cloud from one CLI command, wrapping your agent logic in Terraform, CI/CD, observability, and evaluation. Ships ADK, LangGraph, and multimodal RAG templates for Cloud Run or Vertex AI Agent Engine.
Walks through real LLM workflows across chat, search, deep research, file analysis, coding, voice, images, and generated podcasts. It is most useful as a field guide to the messy AI app layer.
Provides curated ComfyUI workflow templates and subgraph blueprints that package reusable node graphs, preview assets, and publishing pipelines for image/video generation. Includes a browsable Astro site with i18n, CI-driven sync/publish scripts, and PyPI packaging for easy distribution.
Keeps codebases, PDFs, Slack, and docs continuously indexed for RAG and knowledge graphs by recomputing only what changed, not the whole dataset. You declare target state in Python; a Rust engine maintains it with per-row lineage back to the source.
Coordinates role-playing agents to automate real-world tasks — web search and browsing, code execution, document parsing, and multimodal handling. Built on the CAMEL-AI framework; scored 69.09% on the GAIA benchmark, topping open-source frameworks.
Lets AI agents drive GitHub in natural language via MCP: browse repos, triage issues, review pull requests, and trigger Actions runs. Runs as a GitHub-hosted remote OAuth server or a local Go binary, with per-toolset scoping and a read-only mode.
Simulates adversarial attacks against LLMs and AI agents to surface vulnerabilities (e.g., jailbreaks, prompt injection, PII leakage) and ships guardrails to block risky inputs/outputs; runs locally and can be driven from CLI or Python.
Collects the leaked and reverse-engineered system prompts, internal tool definitions, and model configs of 25+ proprietary AI coding assistants — Cursor, v0, Devin, Replit, Windsurf, Claude Code and more. Reveals what each is told to do.
Build and run configurable multi-agent LLM workflows and personal AI agents locally or with cloud LLMs; supports simple TOML-based LLM configuration, optional browser automation, a demo on Hugging Face, and companion RL tuning (OpenManus-RL) for agent training.
High-quality, efficiently verified and filtered web corpus for LLM pretraining — supplies ~1 trillion English tokens and ~120 billion Chinese tokens with English/Chinese Parquet splits. Designed for large-scale pretraining experiments and data-filtering research.
Framework-agnostic library for connecting and optimizing teams of AI agents built in LangChain, LlamaIndex, CrewAI, Semantic Kernel, or Google ADK. Profiles them down to individual tokens, traces execution, and runs built-in evaluation.
Unifies enterprise knowledge into a permission-aware context layer that delivers citation-backed, explainable search and no-code or SDK-driven agentic workflow automation. Supports 50+ connectors, knowledge-graph retrieval, an MCP server, and bring-your-own-model self-hosting.