SceneFiend — scene and monologue discovery for actors

Starting point

SceneFiend started with a specific problem from the craft of acting: actors need material that fits the room, their taste, and the ways they want to stretch—not another undifferentiated list of titles. The goal was to build a real product around that selection and preparation workflow.

What had to work

The system needed authentication, personalization, structured AI output, a curated knowledge base, privacy boundaries, deployment infrastructure, and a release path. It also needed clear places where human judgment remained in charge.

What was built

The work moved from product concept to working prototype, then to MVP, then to a credible 1.0 release-candidate path. Product decisions and implementation stayed connected: when tests exposed a product gap, the architecture changed; when the architecture exposed a constraint, the product scope changed.

Development workflow

The build used an AI-native development workflow. Product planning, engineering, QA, documentation, infrastructure, and release planning did not run as separate phases. They moved together, so each track produced artifacts the others could test.

Development leverage

For this scope, the effective development pace was far faster than I would expect from a conventional early-startup team running product, engineering, QA, documentation, infrastructure, and release planning in sequence.

The leverage came from keeping the work integrated and making decisions against working software instead of waiting for a long sequential build. It is a retrospective on this project, not a guarantee for the next one.

Human judgment

The point was not to remove human judgment. The point was to make the product specific enough that the system could support judgment without pretending to replace it.

Outcome

The result is a built product with personalized scene and monologue recommendations, a curated public library, shareable piece pages, and a saved-book workflow for rehearsal and audition preparation. It is opening carefully in a small actor beta at SceneFiend.app.

What this means for small teams

A founder with a sharp domain insight no longer has to choose between a shallow no-code mockup and a long engineering build. With the right process, a small team can build the first useful system, test its limits, and make go/no-go decisions while the opportunity is still alive.