Discover the Best AI Resources
Curated essentials, no noise — just what matters
Simulates a trading firm using LLM agents in specialized roles — fundamentals, sentiment, news and technical analysts feed bull/bear researcher debates, then a trader and risk team decide. Works across US, global and crypto markets and 10+ LLM providers.
Provides an open platform of omnimodal world models, datasets, and tools to build Physical AI — joint perception, generation, and action reasoning for robots, autonomous vehicles, and smart infrastructure. Supports images, video, audio, and action-conditioned workflows.
Curates 500+ open-source AI agent use cases, indexed two ways: by industry vertical (healthcare, finance, legal, retail, and more) and by framework (CrewAI, AutoGen, LangGraph, LlamaIndex, Agno). Each entry links a runnable repo.
Provides a shared runtime that composes, extends, and observes services in real time by modeling capabilities as discoverable workers, functions, and triggers. It collapses separate integration surfaces (queues, cron, HTTP, observability) into one live catalog so agents and services can call and trace each other immediately.
Lets teams build, deploy, and manage AI agents from chat, visual workflows, code, knowledge bases, tables, and more than a thousand integrations.
Provides programmatic access to Google Flights via a Python library, CLI, and an MCP server — enabling assistants and apps to search flights with filters (time windows, cabin, stops, airlines) by reverse‑engineered API rather than HTML scraping.
Runs penetration tests autonomously: a multi-agent system (researcher, developer, executor) plans attacks, writes and runs exploit code, and chains 20+ tools like nmap, metasploit and sqlmap in isolated Docker containers — for authorized testing only.
Turns camera, audio, LIDAR and web inputs into robot motion, navigation and speech by routing them through pluggable LLMs and VLMs. Hardware-agnostic Go runtime configured via JSON5, with ROS2/Zenoh middleware for real robots and simulators.
Builds event-driven multi-agent AI systems that use a Solace event mesh for agent-to-agent messaging, task delegation, and artifact exchange. Emphasizes asynchronous orchestration, plugin-based extensibility, and integrations with LLMs and external systems.
Provides end-to-end PyTorch scripts to download/prepare data, implement a transformer from scratch, train LLMs (13M→billion-scale) and generate text. Emphasizes educational clarity and single‑GPU experiments; useful for researchers or hobbyists, but large-scale training still requires substantial compute and engineering.
Connects to Gmail, Calendar, and meeting notes to build a local, Obsidian-compatible Markdown graph it acts on — drafting emails, briefs, and decks. Memory accumulates instead of resetting each session; runs on local or hosted models, extensible via MCP.
Wires retrievers, rerankers, and generators as standalone MCP servers orchestrated in YAML, so iterative RAG logic fits in dozens of lines instead of glue code. Adds loops, conditional branches, one-command web UIs, and shared evaluation benchmarks.