Why We Built This
The problem
AI coding assistants are blind. They see one repository at a time.
At Arvore, we have 9 repositories — backend APIs in NestJS and Elixir, frontends in Next.js and React, shared libraries, infrastructure configs. They all depend on each other.
When we asked an AI assistant to implement a feature, it didn’t know:
- That the API contract changed in the backend yesterday
- What the database schema looks like
- How the frontend consumes the endpoint it’s building
- What our deploy patterns and testing conventions are
We spent more time explaining context than the AI spent writing code. Copy-pasting schemas, describing file structures, re-explaining the same conventions over and over.
Then the team got smaller
External circumstances forced us to reduce our engineering team from 30 to 10. Not by choice — it was a reality we had to face.
With a third of the team and the same product ambitions, we couldn’t afford to keep working the old way. Features that took 3 to 6 months were no longer viable on that timeline. We needed a fundamentally different approach.
That’s when we went all-in on AI-powered development. Not as a nice-to-have, but as a survival strategy.
What we tried first
We tried all the standard approaches:
- Long system prompts — Hit context limits, became stale, hard to maintain
- README-driven context — Too generic, agents ignored most of it
- Manual file references — Tedious, easy to forget critical files
- Separate AI tools per repo — No cross-repo awareness, fragmented workflows
None of them solved the fundamental problem: the AI couldn’t see the full picture.
The insight
The breakthrough was simple. Two lines of config:
# .gitignore — repos excluded from hub's git
api
frontend
# .cursorignore — but included for AI context
!api/
!frontend/Git sees separate repositories. The AI sees one workspace. Each repo keeps its own history. Zero migration.
But context alone wasn’t enough. We needed the AI to follow a process, not just answer questions. So we built the agent orchestration layer:
- One YAML file declares everything — repos, services, MCPs, workflow pipeline
- A CLI generates editor configs from that YAML
- The editor becomes the runtime — the orchestrator agent follows the pipeline, calling sub-agents in order
No daemon. No server. No platform. The AI editor runs the entire workflow.
What happened
With 10 engineers and Repo Hub, we didn’t just maintain our pace — we accelerated.
Before:
- 30 engineers
- 3-6 months per feature
- Manual context management
- Ad-hoc AI usage with inconsistent results
- Most time spent on coordination, not building
After:
- 10 product engineers
- Weeks per feature
- Automatic cross-repo context
- Structured agent pipelines with consistent output
- Most time spent on review, security, and product decisions
The role naturally evolved. Our engineers stopped spending time writing boilerplate and started focusing on what requires human judgment: security, architecture, code review, and product decisions. We started calling them product engineers — because that’s what they are.
We’re growing again
The lesson wasn’t “you need fewer people.” The lesson was: with the right tools and the right process, every engineer on your team becomes dramatically more effective.
We’re actively hiring. We want more product engineers who can work this way — people who understand product deeply, know technology well, and can direct AI to build at a pace that wasn’t possible before.
Role details: Product Engineer (Hiring) →
The tools will keep changing. Editors, models, providers — none of it is permanent. What matters is the discipline: structured workflows, encoded knowledge, connected infrastructure, and human review at every step.
Why we open-sourced it
We built Repo Hub to solve our own problem. But the problem isn’t unique to Arvore.
Every team using AI coding assistants hits the same wall: context fragmentation, workflow inconsistency, manual overhead. The solution shouldn’t be proprietary.
Repo Hub is MIT licensed and designed for adoption. If your team manages multiple repositories and uses AI for development, this framework is for you.