Discover the Best AI Resources
Curated essentials, no noise — just what matters
MCP-native agent framework built around the Model Context Protocol from the start, with end-to-end tested Sampling and Elicitation. Define agents and multi-step workflows in Python, run terminal-first, and swap Anthropic, Google or local models.
Drives your computer from natural language: a vision-language model reads raw screenshots and works the mouse and keyboard like a person, controlling any GUI app without APIs or accessibility hooks. Local or remote operator modes on Windows and macOS.
Bundles a dataset, an interaction harness, and rubric-based reward functions into one RL environment for training and evaluating LLMs — also usable as an eval, synthetic-data pipeline, or agent harness for any OpenAI-compatible endpoint.
Shows that LLM reasoning can be incentivized through pure reinforcement learning, with no human-annotated reasoning traces. Self-reflection, verification, and strategy-switching emerge on their own, and the patterns transfer to distill smaller models.
Multi‑modal closed-ended academic benchmark with 2,500 multiple-choice and short-answer exam questions spanning math, natural sciences, and humanities for automated grading. Curated by subject-matter experts, released under MIT, and includes a canary string to help prevent dataset leakage into model training.
Generates multi-chapter long-form novels with LLMs, automatically linking context and managing foreshadowing for global coherence. Features vector-based retrieval, character/state tracking and a GUI-driven pipeline; requires LLM/embedding API keys.
Runs stateful AI agents as Cloudflare Durable Objects — each keeps its own storage and lifecycle, hibernating when idle and waking on demand. Adds WebSocket state sync, type-safe RPC, resumable LLM streaming, MCP roles, and durable workflows.
Provides a hardware plugin that runs vLLM on Huawei Ascend NPUs by mapping vLLM execution and memory management to the Ascend runtime. Key features: support for Transformer/MoE/embedding/multimodal models, official docs, CI-backed release branches and community maintenance.
Hands-on studio to design, test and deploy declaratively configured multi-agent systems built on the Neuro SAN framework. Ships ready examples, an Agent Network Designer UI (nsflow), CLI tooling, and integrations with major LLMs and external tools for rapid prototyping.
Spins up sandboxed VMs and containers (macOS, Linux, Windows, Android) that an AI agent can fully control through one unified SDK, cloud or local, plus a benchmark suite and background drivers that automate native apps without grabbing the cursor.
Press a configurable shortcut, speak, and have your words transcribed and pasted into the active app. Runs Whisper or the CPU-friendly Parakeet V3 fully offline; a Tauri + Rust build with Silero voice-activity detection and optional GPU acceleration.