Static template — Save to persist, or Regenerate once the LLM client lands.
Problem
Users following rohitg00/ai-engineering-from-scratch hit friction the moment they try to apply the trend to their own workflow. Agent frameworks expose traces, but builders still struggle to understand failed tool calls and hidden state transitions.
Audience
Indie hackers + small dev teams already tracking rohitg00/ai-engineering-from-scratch. They have intent but not the patience to plumb config + auth + storage.
Outcome
Bring time-to-first-value from hours to under five minutes with sensible defaults and a one-click deploy path.
MVP scope
1) one-click deploy, 2) opinionated config schema, 3) example dataset/seed, 4) a 90-second demo + docs.
Tech stack
Next.js + TS, HOSTUP or self-hosted runtime, Postgres for state, Clerk for auth. Reuse rohitg00/ai-engineering-from-scratch as a dependency, no fork.
Distribution
Post on the rohitg00/ai-engineering-from-scratch Discord/GitHub Discussions, Hacker News Show HN, plus a Twitter/X thread tagged with the upstream handle.
Risks
Upstream breaking changes; license edge cases; users wanting features that conflict with the simplicity of the starter.
Success signals
≥ 50 deploys in week 1, ≥ 10 GitHub stars on the starter repo, ≥ 1 upstream PR linking back to it.