Q-base

Local-first AI orchestration that routes tasks across multi-agent lanes, optimizes execution bundles, and captures evidence so teams can automate software delivery with speed and accountability.

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Inspiration

We wanted AI-assisted development to be reliable, auditable, and team-ready, not just fast. Q-Base was built to turn prompt chaos into structured execution with routing, bounded parallel lanes, and evidence-backed outputs.

What it does

Q-Base routes tasks to the best lane, optimizes multi-lane execution bundles, tracks runtime state/telemetry, and returns structured decision data so teams can review and trust how work was automated.

How we built it

We built a local-first runtime with API endpoints for chat, routing, and optimization; a lane orchestrator; a bounded selector model; and VS Code integration for day-to-day operator workflow.

Challenges we ran into

Balancing flexible input with deterministic routing, controlling parallelism without overload, integrating API orchestration cleanly into VS Code, and maintaining clarity across multiple legacy/archive project artifacts.

Accomplishments that we're proud of

We delivered a functioning multi-lane automation system, live route/optimize flows, editor-native control in VS Code, and clear evidence-oriented outputs that make automation transparent and operationally useful.

What we learned

Orchestration quality matters as much as model quality. Local-first design improves trust and resilience. Structured outputs and strong documentation are critical for adoption in real engineering teams.

What's next for Q-base

We plan to improve adaptive lane policies, deepen execution/evidence dashboards, strengthen one-command onboarding, and expand the VS Code-first operator experience for larger team workflows.

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