AI Completes the Sprint

In PrePitch development, we used Claude Code + Rebar to decompose the sprint like a human and run it in parallel.

Sprint 1 already had the following work completed:

• streaming text responses
• Sales / VC / PE / Interview dashboard cards
• radar chart scoring
• backend support for coaching notes by criteria

Additionally, ClaudeSession persistence significantly reduced latency.

• Average latency: 18.7s → 5.3s
• Time to first token: approx. 2.2s on cached turns

In Sprint 2, Claude mapped the codebase, split the specs, checked for file conflicts, and then decomposed the work into parallel streams.

• KB compression
• interview benchmark fix
• voice UX polish
• debrief / coaching / comparison / practice weakness flow

The results were clean.

• KB compression: 247K chars → 79K chars
• interview benchmark fix completed
• voice UX polish completed
• coaching display completed
• score comparison completed
• practice weakness flow completed

When running the full test suite, there were 0 new failures introduced in Sprint 2, and the remaining failures were pre-existing issues.

The biggest practical win was KB compression. By reducing the text from 247K → 79K, we estimated that turn 1 latency could be lowered to approximately 7.4s → 3s.

The core message is that AI has entered the implementation layer not just for simple code assistance, but for sprint decomposition, parallel execution, and completion verification. While oversight is still necessary, the emphasis is on the tangible feeling of having crossed a threshold in practical usage.