After months of a perpetually red CI pipeline, today marks a turning point for Parthenon: 92 commits, a full-spectrum codebase review, a complete AtlanticHealth patient synthesis pipeline, and — most satisfying of all — every CI job green. Here's how we got there.
A quieter day on the Parthenon platform — today's commits centered on documentation infrastructure rather than feature work, with automated help content synchronization and the daily development blog pipeline keeping the platform's knowledge base fresh and up to date.
Eighteen Docker services. Three environment files. A reverse proxy with auto-TLS. Database admin GUI. Container management dashboard. Enterprise SSO. And if you want the full stack? One command:
python3 install.py --with-infrastructure
This is the story of how we built Acropolis — the infrastructure layer that turns Parthenon from a research application into a production platform — and what we learned when we decided to ship it inside the same repository.
Every platform has a weak link. For Parthenon, it was the R container.
PHP handled 200 concurrent API requests without breaking a sweat. Python served AI inference with async workers. PostgreSQL managed million-row queries across six schemas. Redis cached sessions at sub-millisecond latency. And then there was R — single-threaded, fragile, running bare Rscript as PID 1 with no supervision, no timeouts, and a health check that lied.
This is the story of how we tore it down and built Darkstar — a production-grade R analytics engine that runs OHDSI HADES analyses concurrently, recovers from crashes automatically, and executes 35% faster than the container it replaced.
A big architectural day on Parthenon: we shipped the new Workbench launcher experience and drafted the formal plan to collapse our multi-database mess into a single, schema-isolated parthenon database. A noticeably cleaner codebase on the other side.
A massive 116-commit push today centered almost entirely on maturing the Evidence Investigation workbench — from retiring the old FinnGen UI to hardening the investigation experience with proper navigation, KPI metrics, URL-synced state, and ARIA accessibility. We also landed multi-dataset support in Morpheus and set the stage for one of the most requested features on the roadmap: volcano plots powered by the newly-renamed Darkstar R runtime.
A big day on the quality and resilience front: 34 commits landed in Parthenon focused on a comprehensive codebase health audit, a major expansion of our Playwright E2E test suite, and a fork of the StudyAgent submodule. No flashy new features today — instead, we did the unglamorous but essential work of making sure what we've already built actually works, is safe to change, and won't silently break in production.
The R runtime was the single most fragile component in the entire Parthenon stack. Every other service — PHP, Python AI, Solr, Redis, PostgreSQL — could handle concurrent requests gracefully. The R container could not. A single CohortMethod estimation on 1 million patients takes 5-30 minutes. During that time, the entire R process was locked — health checks timed out, status queries hung, and any other analysis request queued behind it with no feedback. This devlog covers the six-phase hardening effort that replaced the entire R runtime infrastructure in a single day.
Abby can now orchestrate complex multi-step research workflows with independent steps running in parallel. High-risk tools (modify concept sets, update cohort criteria, execute SQL) join the toolkit with safety validation. Dry run mode simulates actions before execution. Workflow templates encode OHDSI best practices into one-click study designs.
Abby can now take actions, not just answer questions. "Build me a diabetes cohort" generates a reviewable multi-step plan — create concept sets, define the cohort, generate the patient count — that executes with one click after user approval. Every action is logged with checkpoint data for rollback. Phase 4 adds supervised autonomy with safety rails.