Discover the Best AI Resources
Curated essentials, no noise — just what matters
Generates protocol-bound GEP prompts that guide iterative evolution of AI agent behavior from runtime logs, producing auditable EvolutionEvents and reusable Genes/Capsules. Node.js-based and works offline; optional EvoMap network integration enables skill sharing, worker pools and leaderboards while git provides rollback and validation.
Self-hosted personal AI agent runtime that runs chats, tools, automations and long-term memory for persistent workflows. Small, readable core with a bundled WebUI, multi-chat integrations, an OpenAI-compatible API and a Python SDK for easy extension and deployment.
Centralized operations dashboard for OpenClaw agent fleets — orchestrate boards and tasks, manage agent lifecycles, enforce approval-driven governance, and operate gateway-connected runtimes from a single UI and API.
Provides a workflow layer for OpenAI Codex CLI to bootstrap stronger Codex sessions, add reusable agent roles/skills, and manage durable project state under .omx. Includes team runtime, canonical skills, and monitoring surfaces.
Multimodal OCR and document-understanding toolkit for recognizing complex layouts, tables, formulas and code. Uses Multi-Token Prediction and stable RL for better training; ships as a 0.9B-parameter model with a Python SDK and deployment guides for vLLM, SGLang and Ollama.
Automates decompiling APK/AAR/JAR and extracting HTTP APIs — Retrofit endpoints, OkHttp calls, hardcoded URLs, and auth patterns — so you can document and reproduce an app's network surface without source code. Integrates jadx/Vineflower/Fernflower and scripts for call-flow tracing.
Enables research-grade character animation with neural networks in a single NumPy/PyTorch environment — train models, run inference, and visualize results without leaving Python. Includes ECS-style architecture, mocap import (GLB/FBX/BVH), built-in renderer, and headless/standalone modes for rapid prototyping.
Collection of small, composable agent skills that extend LLM-based agents for planning, development, and tooling — installable via npx and designed to turn higher-level tasks (PRDs, TDD, refactors, triage) into reproducible agent actions.
An open, intuition-first textbook that teaches the maths, computing, and practical foundations needed for AI engineering. Organized into focused chapters (vectors, matrices, calculus, ML, NLP, CV, GPU/Inference, ML systems) with code-first explanations and interview-ready emphasis.
A curated collection of reusable 'skills' that let LLM-driven coding agents perform common .NET/C# tasks — build diagnosis, debugging, testing, data access, upgrades, MAUI, and AI/ML workflows. Implements the Agent Skills standard and is published for agent marketplaces (Copilot CLI, Claude Code, Cursor).
Provides 100 real-world, open-ended research tasks paired with expert-written rubrics (around 40 weighted criteria per task) to evaluate long-form, web-browsing research agents on factual accuracy, analysis depth, presentation, and citation quality.
Provides a Spotify-like local UI for running ACE-Step 1.5 to generate full songs (including vocals), batch variations, and manage a local music library, with reference-audio styling and built-in editing/stem tools for users running the model locally.