Sterna is a full-stack AI workspace: a Django/DRF backend pairing a React 19 + TypeScript frontend with FastAPI microservices, a Docker sandbox for code execution, and Kubernetes/Terraform infrastructure. Users run several models side by side in one conversation, hand GitHub issues to a sandboxed coding agent that writes reviewed implementation plans and pull requests through an in-browser IDE, query their own documents through pgvector RAG, join live multi-agent voice rooms, and see the measured tokens, cost, and latency on every single message.
The core was finished about eight months before release; what followed was polish. Rather than fund the hosting and on-call that a public SaaS demands, I chose to open-source the whole thing under AGPL-3.0 — the code is the product, self-hostable from one compose file, with recorded demos of every feature in the repository.
The codebase itself was driven by an autonomous Claude Code task-runner enforcing per-task quality gates, gate-repair loops, and structured failure records, making the development process a case study in agentic software engineering.