Discover the Best AI Resources
Curated essentials, no noise — just what matters
Exposes a managed cloud browser to an LLM as MCP tools, letting an agent open sessions, navigate, click, read page elements, and pull data from live websites. Built on Stagehand, so steps are written in plain language, not brittle CSS selectors.
Terminal-native AI coding agent that brings conversational, multi-model code assistance into your shell. Integrates with 300+ models and providers, offers an interactive TUI, Zsh ':' plugin, semantic workspace search, and Git-oriented workflows for in-terminal edits, commits, and command suggestions.
A library of specialized AI agents that automate data science steps: loading, cleaning, wrangling, feature engineering, SQL queries, EDA, and ML modeling via H2O and MLflow. Higher-level analyst workflows chain these under a supervisor agent.
Elixir-native autonomous agent framework that models state changes as pure cmd/2 operations and describes side effects with typed directives; integrates with OTP supervision and optional LLM plugins for AI-driven agents.
Connects an AI agent to a Supabase project over MCP to run SQL, manage tables and migrations, deploy Edge Functions, fetch keys and types, and read logs. Read-only mode and project scoping cap what the agent can touch.
A 100-line LLM framework built on one graph abstraction of nodes and flows, with zero dependencies and no vendor wrappers. The tiny core composes agents, workflows, and RAG, and is small enough for a coding agent to read and extend on its own.
Connects AI agents to 50+ apps and databases — Notion, Slack, Salesforce, GitHub, Jira — then continuously syncs and indexes their data behind one search API, with auth, ingestion, and retrieval exposed via MCP, REST, and SDKs.
Native desktop client unifying many model providers (OpenAI, Gemini, Anthropic, Ollama, local LLMs) in one app on Windows, macOS, and Linux. Adds 300+ preset assistants, document/PDF chat, MCP server integration, and WebDAV backup, with no subscription.
A 671B-parameter Mixture-of-Experts language model (37B activated) trained on 14.8T tokens with 128K context, FP8-first training, a Multi-Token Prediction module, and Hugging Face weights—focused on efficient MoE training and long-context use cases.
Build scripts that repackage Anthropic's Claude Desktop into native Linux artifacts (.deb, .rpm, AppImage, AUR, Nix flake), enabling a native Claude client with system tray, global hotkey, and MCP integration for Debian/Ubuntu and other distros.
Captures, transcribes, and summarizes meetings entirely on the user's machine with real-time local transcription and speaker diarization. Privacy-first design keeps audio, transcripts, and models local; supports Ollama, Claude, Groq, OpenRouter or custom OpenAI-compatible endpoints.
Open-weight Mixture-of-Experts LLM with 671B total parameters but 37B activated per token, trained on 14.8T tokens for 2.788M H800 GPU-hours. Matches leading closed models at a fraction of typical training cost via FP8 and architectural tricks.