Vibecode diclip

You are building a lean indie version of diclip.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# diclip indie build

## Goal

Build the smallest trustworthy replacement for the core diclip workflow for one developer or a tiny team.

## Scope

Transcribe a long video, rank the strongest moments with an LLM, and render vertical clips with burned captions using FFmpeg.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- face-aware vertical reframing with seat tracking
- full in-browser timeline editor with scenes, tracks, and effects
- container-scale render pipeline and yt-dlp import
- credit-based capacity model and priority processing
- ready-to-post hooks and confidence scoring polished for clips

If those capabilities are essential, use diclip instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build me a personal substitute for diclip, not a platform clone.

- Use Python 3.12 + FastAPI for a localhost web app, a plain JavaScript
  frontend, faster-whisper for word-level transcription, and FFmpeg for
  rendering.
- I import an MP4, MOV, WebM, or MP3 file and get a transcript synced to the
  video with speaker timing.
- A ranking step selects the strongest 30-90 second moments: call an LLM API
  key from .env with the transcript, or fall back to a local heuristic
  (keyword density, question marks, pauses) when no key is set.
- Show each candidate clip with its hook line, a confidence score, and a one
  sentence reason, marked ready or pending.
- Let me accept or reject each clip, edit in/out points, and choose 9:16, 1:1,
  or 16:9 output.
- Render clips with FFmpeg using center-crop or blur-pad, burning captions
  styled by speaker. Never overwrite the source. Show progress and a useful
  failure message.
- Store projects as JSON under ~/diclipDIY/projects and renders under
  ~/diclipDIY/exports, with a recent-projects page and a delete action.
- Bind to localhost only. No accounts, telemetry, or network calls after the
  model and transcript are local, except the optional LLM ranking call.
- Deliberately exclude face-aware reframing with seat tracking, the full
  in-browser timeline editor, cloud container rendering, yt-dlp downloads, and
  credit billing.
- Add unit tests for the ranking fallback and caption grouping, plus one smoke
  test that imports a short fixture and produces a playable MP4.
- Include a README with setup, data paths, and an honest note that CPU
  transcription and rendering are slow on long videos.

## Required capabilities

- FFmpeg
- faster-whisper
- LLM API key for moment ranking (optional, falls back to heuristics)
- desktop with enough storage for source media and renders

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a lean indie version of diclip.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# diclip indie build

## Goal

Build the smallest trustworthy replacement for the core diclip workflow for one developer or a tiny team.

## Scope

Transcribe a long video, rank the strongest moments with an LLM, and render vertical clips with burned captions using FFmpeg.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- face-aware vertical reframing with seat tracking
- full in-browser timeline editor with scenes, tracks, and effects
- container-scale render pipeline and yt-dlp import
- credit-based capacity model and priority processing
- ready-to-post hooks and confidence scoring polished for clips

If those capabilities are essential, use diclip instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build me a personal substitute for diclip, not a platform clone.

- Use Python 3.12 + FastAPI for a localhost web app, a plain JavaScript
  frontend, faster-whisper for word-level transcription, and FFmpeg for
  rendering.
- I import an MP4, MOV, WebM, or MP3 file and get a transcript synced to the
  video with speaker timing.
- A ranking step selects the strongest 30-90 second moments: call an LLM API
  key from .env with the transcript, or fall back to a local heuristic
  (keyword density, question marks, pauses) when no key is set.
- Show each candidate clip with its hook line, a confidence score, and a one
  sentence reason, marked ready or pending.
- Let me accept or reject each clip, edit in/out points, and choose 9:16, 1:1,
  or 16:9 output.
- Render clips with FFmpeg using center-crop or blur-pad, burning captions
  styled by speaker. Never overwrite the source. Show progress and a useful
  failure message.
- Store projects as JSON under ~/diclipDIY/projects and renders under
  ~/diclipDIY/exports, with a recent-projects page and a delete action.
- Bind to localhost only. No accounts, telemetry, or network calls after the
  model and transcript are local, except the optional LLM ranking call.
- Deliberately exclude face-aware reframing with seat tracking, the full
  in-browser timeline editor, cloud container rendering, yt-dlp downloads, and
  credit billing.
- Add unit tests for the ranking fallback and caption grouping, plus one smoke
  test that imports a short fixture and produces a playable MP4.
- Include a README with setup, data paths, and an honest note that CPU
  transcription and rendering are slow on long videos.

## Required capabilities

- FFmpeg
- faster-whisper
- LLM API key for moment ranking (optional, falls back to heuristics)
- desktop with enough storage for source media and renders

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a production product version of diclip.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== PRODUCT.md =====
# diclip product brief

## Problem

The core loop is genuinely one-shottable: transcribe, rank the strongest moments, cut vertical clips with burned captions. The gaps are execution and infra. Face-aware reframing with seat tracking, a full in-browser timeline editor, and a container-scale render pipeline are not a one-session build. A competent agent can produce a usable personal clipper quickly, but matching diclip's reframe quality and editing depth is a multi-day project.

## Product outcome

Transcribe a long video, rank the strongest moments with an LLM, and render vertical clips with burned captions using FFmpeg.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- FFmpeg
- faster-whisper
- LLM API key for moment ranking (optional, falls back to heuristics)
- desktop with enough storage for source media and renders

## Explicit non-goals for v1

- face-aware vertical reframing with seat tracking
- full in-browser timeline editor with scenes, tracks, and effects
- container-scale render pipeline and yt-dlp import
- credit-based capacity model and priority processing
- ready-to-post hooks and confidence scoring polished for clips

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

===== ARCHITECTURE.md =====
# Architecture

## Starting brief

Build me a personal substitute for diclip, not a platform clone.

- Use Python 3.12 + FastAPI for a localhost web app, a plain JavaScript
  frontend, faster-whisper for word-level transcription, and FFmpeg for
  rendering.
- I import an MP4, MOV, WebM, or MP3 file and get a transcript synced to the
  video with speaker timing.
- A ranking step selects the strongest 30-90 second moments: call an LLM API
  key from .env with the transcript, or fall back to a local heuristic
  (keyword density, question marks, pauses) when no key is set.
- Show each candidate clip with its hook line, a confidence score, and a one
  sentence reason, marked ready or pending.
- Let me accept or reject each clip, edit in/out points, and choose 9:16, 1:1,
  or 16:9 output.
- Render clips with FFmpeg using center-crop or blur-pad, burning captions
  styled by speaker. Never overwrite the source. Show progress and a useful
  failure message.
- Store projects as JSON under ~/diclipDIY/projects and renders under
  ~/diclipDIY/exports, with a recent-projects page and a delete action.
- Bind to localhost only. No accounts, telemetry, or network calls after the
  model and transcript are local, except the optional LLM ranking call.
- Deliberately exclude face-aware reframing with seat tracking, the full
  in-browser timeline editor, cloud container rendering, yt-dlp downloads, and
  credit billing.
- Add unit tests for the ranking fallback and caption grouping, plus one smoke
  test that imports a short fixture and produces a playable MP4.
- Include a README with setup, data paths, and an honest note that CPU
  transcription and rendering are slow on long videos.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

===== AGENTS.md =====
# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

===== MILESTONES.md =====
# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

===== OPERATIONS.md =====
# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted diclip capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# diclip indie build

## Goal

Build the smallest trustworthy replacement for the core diclip workflow for one developer or a tiny team.

## Scope

Transcribe a long video, rank the strongest moments with an LLM, and render vertical clips with burned captions using FFmpeg.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- face-aware vertical reframing with seat tracking
- full in-browser timeline editor with scenes, tracks, and effects
- container-scale render pipeline and yt-dlp import
- credit-based capacity model and priority processing
- ready-to-post hooks and confidence scoring polished for clips

If those capabilities are essential, use diclip instead of pretending the gap is solved.

# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

# Build plan

## Original build brief

Build me a personal substitute for diclip, not a platform clone.

- Use Python 3.12 + FastAPI for a localhost web app, a plain JavaScript
  frontend, faster-whisper for word-level transcription, and FFmpeg for
  rendering.
- I import an MP4, MOV, WebM, or MP3 file and get a transcript synced to the
  video with speaker timing.
- A ranking step selects the strongest 30-90 second moments: call an LLM API
  key from .env with the transcript, or fall back to a local heuristic
  (keyword density, question marks, pauses) when no key is set.
- Show each candidate clip with its hook line, a confidence score, and a one
  sentence reason, marked ready or pending.
- Let me accept or reject each clip, edit in/out points, and choose 9:16, 1:1,
  or 16:9 output.
- Render clips with FFmpeg using center-crop or blur-pad, burning captions
  styled by speaker. Never overwrite the source. Show progress and a useful
  failure message.
- Store projects as JSON under ~/diclipDIY/projects and renders under
  ~/diclipDIY/exports, with a recent-projects page and a delete action.
- Bind to localhost only. No accounts, telemetry, or network calls after the
  model and transcript are local, except the optional LLM ranking call.
- Deliberately exclude face-aware reframing with seat tracking, the full
  in-browser timeline editor, cloud container rendering, yt-dlp downloads, and
  credit billing.
- Add unit tests for the ranking fallback and caption grouping, plus one smoke
  test that imports a short fixture and produces a playable MP4.
- Include a README with setup, data paths, and an honest note that CPU
  transcription and rendering are slow on long videos.

## Required capabilities

- FFmpeg
- faster-whisper
- LLM API key for moment ranking (optional, falls back to heuristics)
- desktop with enough storage for source media and renders

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

# diclip product brief

## Problem

The core loop is genuinely one-shottable: transcribe, rank the strongest moments, cut vertical clips with burned captions. The gaps are execution and infra. Face-aware reframing with seat tracking, a full in-browser timeline editor, and a container-scale render pipeline are not a one-session build. A competent agent can produce a usable personal clipper quickly, but matching diclip's reframe quality and editing depth is a multi-day project.

## Product outcome

Transcribe a long video, rank the strongest moments with an LLM, and render vertical clips with burned captions using FFmpeg.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- FFmpeg
- faster-whisper
- LLM API key for moment ranking (optional, falls back to heuristics)
- desktop with enough storage for source media and renders

## Explicit non-goals for v1

- face-aware vertical reframing with seat tracking
- full in-browser timeline editor with scenes, tracks, and effects
- container-scale render pipeline and yt-dlp import
- credit-based capacity model and priority processing
- ready-to-post hooks and confidence scoring polished for clips

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

# Architecture

## Starting brief

Build me a personal substitute for diclip, not a platform clone.

- Use Python 3.12 + FastAPI for a localhost web app, a plain JavaScript
  frontend, faster-whisper for word-level transcription, and FFmpeg for
  rendering.
- I import an MP4, MOV, WebM, or MP3 file and get a transcript synced to the
  video with speaker timing.
- A ranking step selects the strongest 30-90 second moments: call an LLM API
  key from .env with the transcript, or fall back to a local heuristic
  (keyword density, question marks, pauses) when no key is set.
- Show each candidate clip with its hook line, a confidence score, and a one
  sentence reason, marked ready or pending.
- Let me accept or reject each clip, edit in/out points, and choose 9:16, 1:1,
  or 16:9 output.
- Render clips with FFmpeg using center-crop or blur-pad, burning captions
  styled by speaker. Never overwrite the source. Show progress and a useful
  failure message.
- Store projects as JSON under ~/diclipDIY/projects and renders under
  ~/diclipDIY/exports, with a recent-projects page and a delete action.
- Bind to localhost only. No accounts, telemetry, or network calls after the
  model and transcript are local, except the optional LLM ranking call.
- Deliberately exclude face-aware reframing with seat tracking, the full
  in-browser timeline editor, cloud container rendering, yt-dlp downloads, and
  credit billing.
- Add unit tests for the ranking fallback and caption grouping, plus one smoke
  test that imports a short fixture and produces a playable MP4.
- Include a README with setup, data paths, and an honest note that CPU
  transcription and rendering are slow on long videos.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted diclip capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

$ choose a build depth, inspect the files, then open the complete pack in your agent