Goal-driven development with AI agents — from specs to merged PRs across dev and office repos. RAPID generates specs, Ralph implements via TDD, Polyforge and Office-forge orchestrate across repos, issues sync cross-repo, progress traces back to goals.
This ecosystem connects a goal artifact through spec generation, autonomous implementation, cross-repo coordination, and traceability — end to end.
- Agentic Software Development, Autonomous Coding
- Self-Evolving Agents, Compound Learning
- Multi-Repo Orchestration, Cross-Repo Issue Sync
- AI Agent Evaluation, MAS Benchmarking
- Goal-Driven Lifecycle Management, OKR Traceability
- GitHub Actions, CI/CD Automation (REGISTRY → OBSERVE → TRANSFORM → ORCHESTRATE → DISTRIBUTE → IMPLEMENT)
- Claude Code Plugins, MCP Integrations
- Agent UI (AG-UI/A2UI), Voice (TTS/STT)
- Robotics, Bio-Lab Automation
- Inductive Priors, Automatic Differentiation
- Data Centric vs Model Centric
- QML, Barren Plateaus
- Agentx Agentbeats Writeup
- AI Agents-eval Comprehensive Analysis
- AI Agents-eval Enhancement Recommendations
- AI Agents-eval Papers Meta Review
- Kaggle Playgrounds
- Kaggle Competitions
- Codewars Python
- AdventOfcode