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FOREST is a research agent for long-horizon science, where evidence gets lost, errors compound, and goals drift. It explores ideas, reads literature, forms hypotheses, runs real experiments, analyzes results, revises paths, and builds a full paper—all in an editable graph you can inspect, branch, redirect, with claims and figures linked to evidence

  • ai-agents
  • ai-scientist
  • automated-research
  • autoresearch

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

TranslatedA basic tutorial for getting started with AI Agents from zero background. It is not just a tutorial but also an Agent that can run locally: read and write files, execute commands, MCP, long-term memory, with a built-in web interface to see every step clearly. Starting from a minimal 100-line loop, each lesson adds one capability, and in the end it breaks down mainstream Agents such as Claude Code and Codex

A Claude skill for directing, building and verifying code-rendered videos (HTML/Canvas → MP4): principles, gated workflow, QA scripts and evals.

Official repository for the paper “RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution”

Photoreal Blender scenes from a one-paragraph brief, built by AI agents: image→3D props on fal, scripted layout, lighting, camera and grade.