Notes

please read the pinned comments in the demo video in yt some narration mistakes highlighted

at the end I wanted to show the reports dwight generate in the video both change request report and approved report are there

Inspiration

I kept watching devs drown in PR review and task triage while AI tools just generated more code. I wanted an AI teammate that actually owns a column on the board, not another chatbot.

What it does

DAVE is a Kanban workspace where three AI teammates (Michael, Jim, Dwight) sit alongside humans. Michael breaks a project into tasks, Jim writes code and opens PRs, and Dwight reviews every PR before a task can move to Done. Humans and agents share the same board, so nothing gets lost between "AI wrote it" and "shipped."

How we built it

Next.js + Supabase for auth, data, and realtime. GitHub OAuth for repo access. Each agent is a server-side module that calls the LLM with project context, repo snapshots, and diffs, then writes structured results back to the board. Dwight streams a live activity log so you can watch him review.

Challenges we ran into

no cost challege - used free gemini ai api and open source providers for vibe engineering as well

Getting agents to act on the board without a human in the loop was harder than the LLM part — verdicts had to flip task status, move columns, and bounce tasks back reliably. GitHub token expiry mid-review also needed graceful re-auth instead of silent failure.

Accomplishments that we're proud of

A full loop works end to end: project setup → task breakdown → code + PR → automated review → done. Dwight's change-request flow now lets the dev explain what they fixed, so re-reviews aren't blind.

What we learned

Agents need tight, well-defined jobs. "Review this PR against this task" works; "help with the project" doesn't. Structured JSON outputs beat freeform chat for anything that drives UI state.

What's next for DAVE

More agent roles, deeper GitHub Actions integration, and letting teams define their own agents with custom rules.

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