Vibecode BlitzReels

You are building a lean indie version of BlitzReels.

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 =====
# BlitzReels indie build

## Goal

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

## Scope

Import one long video, transcribe it, rank clip candidates, let the user adjust boundaries and captions, then export a 9:16 MP4.

## 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:
- reliable ingestion and recovery for large or malformed recordings
- production-tuned clip selection and timestamp alignment
- subject-aware speaker and screen reframing
- complete NLE, caption styles, paid templates, and keyframed timeline editing
- public API, CLI, agent integrations, cloud rendering, retries, and share workflows

If those capabilities are essential, use auto-editor 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 the closest honest personal substitute for BlitzReels in an empty repository.
Use exactly Node.js 22, TypeScript, React, Remotion, SQLite, FFmpeg, and the OpenAI API.
Run as a single-user local web app with one documented command.
The core loop is import one user-owned long video, transcribe it, rank five clip candidates, edit one, and export 9:16 MP4.
Preserve the original file and store projects, transcripts, candidates, and edits in SQLite.
Use ffprobe for duration and stream metadata before accepting a file.
Extract audio with FFmpeg and transcribe it with word timestamps.
Ask a text model for five 30 to 90 second candidates with exact boundaries, titles, hooks, and reasons.
Reject overlapping or out-of-range candidates and show the model output as suggestions, never facts.
Let the user adjust in and out points, edit caption text, and preview the vertical composition.
Use a static center crop with a manual horizontal offset; do not claim subject tracking.
Render burned captions from verified word timings with one readable style.
Export through Remotion to a new file and never overwrite source media.
Show import, probing, transcription, analysis, preview, rendering, success, and recoverable failure states.
Persist job state so an interrupted transcription or render can be retried safely.
Put the OpenAI key in .env, ship .env.example, and never log secrets or transcript contents.
Add safe filenames, upload size limits, input validation, and explicit local data deletion.
Write focused tests for time-range validation, candidate overlap, caption grouping, and project persistence.
Add one end-to-end smoke test using a tiny generated video fixture.
Create a README with setup, FFmpeg installation, architecture, data location, costs, backup, and limitations.
Deliberately exclude smart reframing, B-roll generation, voice isolation, silence cleanup, and a complete multitrack NLE.
Deliberately exclude the public API, CLI, agent integrations, accounts, teams, cloud rendering, share pages, and social publishing.
Do not call this a BlitzReels clone; label it a narrow local clip finder and exporter.
Run the tests and a real sample render before finishing, then report the exact commands and output path.

## Required capabilities

- Node.js 22
- FFmpeg and ffprobe
- OpenAI API key in .env
- local disk space for source and rendered media

## 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 BlitzReels.

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 =====
# BlitzReels indie build

## Goal

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

## Scope

Import one long video, transcribe it, rank clip candidates, let the user adjust boundaries and captions, then export a 9:16 MP4.

## 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:
- reliable ingestion and recovery for large or malformed recordings
- production-tuned clip selection and timestamp alignment
- subject-aware speaker and screen reframing
- complete NLE, caption styles, paid templates, and keyframed timeline editing
- public API, CLI, agent integrations, cloud rendering, retries, and share workflows

If those capabilities are essential, use auto-editor 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 the closest honest personal substitute for BlitzReels in an empty repository.
Use exactly Node.js 22, TypeScript, React, Remotion, SQLite, FFmpeg, and the OpenAI API.
Run as a single-user local web app with one documented command.
The core loop is import one user-owned long video, transcribe it, rank five clip candidates, edit one, and export 9:16 MP4.
Preserve the original file and store projects, transcripts, candidates, and edits in SQLite.
Use ffprobe for duration and stream metadata before accepting a file.
Extract audio with FFmpeg and transcribe it with word timestamps.
Ask a text model for five 30 to 90 second candidates with exact boundaries, titles, hooks, and reasons.
Reject overlapping or out-of-range candidates and show the model output as suggestions, never facts.
Let the user adjust in and out points, edit caption text, and preview the vertical composition.
Use a static center crop with a manual horizontal offset; do not claim subject tracking.
Render burned captions from verified word timings with one readable style.
Export through Remotion to a new file and never overwrite source media.
Show import, probing, transcription, analysis, preview, rendering, success, and recoverable failure states.
Persist job state so an interrupted transcription or render can be retried safely.
Put the OpenAI key in .env, ship .env.example, and never log secrets or transcript contents.
Add safe filenames, upload size limits, input validation, and explicit local data deletion.
Write focused tests for time-range validation, candidate overlap, caption grouping, and project persistence.
Add one end-to-end smoke test using a tiny generated video fixture.
Create a README with setup, FFmpeg installation, architecture, data location, costs, backup, and limitations.
Deliberately exclude smart reframing, B-roll generation, voice isolation, silence cleanup, and a complete multitrack NLE.
Deliberately exclude the public API, CLI, agent integrations, accounts, teams, cloud rendering, share pages, and social publishing.
Do not call this a BlitzReels clone; label it a narrow local clip finder and exporter.
Run the tests and a real sample render before finishing, then report the exact commands and output path.

## Required capabilities

- Node.js 22
- FFmpeg and ffprobe
- OpenAI API key in .env
- local disk space for source and rendered media

## 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 BlitzReels.

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 =====
# BlitzReels product brief

## Problem

A toy clip finder is a one-session build. A credible BlitzReels replacement is not: the product combines reliable long-video ingestion, timestamp-accurate AI selection, smart reframing, a complete nonlinear editor, browser preview and export parity, public API and CLI contracts, agent integrations, storage, and render orchestration. Its founder reports that the production system took a year even with prior Remotion experience and paid templates.

## Product outcome

Import one long video, transcribe it, rank clip candidates, let the user adjust boundaries and captions, then export a 9:16 MP4.

## Target user

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

## Required capabilities

- Node.js 22
- FFmpeg and ffprobe
- OpenAI API key in .env
- local disk space for source and rendered media

## Explicit non-goals for v1

- reliable ingestion and recovery for large or malformed recordings
- production-tuned clip selection and timestamp alignment
- subject-aware speaker and screen reframing
- complete NLE, caption styles, paid templates, and keyframed timeline editing
- public API, CLI, agent integrations, cloud rendering, retries, and share workflows

## 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 the closest honest personal substitute for BlitzReels in an empty repository.
Use exactly Node.js 22, TypeScript, React, Remotion, SQLite, FFmpeg, and the OpenAI API.
Run as a single-user local web app with one documented command.
The core loop is import one user-owned long video, transcribe it, rank five clip candidates, edit one, and export 9:16 MP4.
Preserve the original file and store projects, transcripts, candidates, and edits in SQLite.
Use ffprobe for duration and stream metadata before accepting a file.
Extract audio with FFmpeg and transcribe it with word timestamps.
Ask a text model for five 30 to 90 second candidates with exact boundaries, titles, hooks, and reasons.
Reject overlapping or out-of-range candidates and show the model output as suggestions, never facts.
Let the user adjust in and out points, edit caption text, and preview the vertical composition.
Use a static center crop with a manual horizontal offset; do not claim subject tracking.
Render burned captions from verified word timings with one readable style.
Export through Remotion to a new file and never overwrite source media.
Show import, probing, transcription, analysis, preview, rendering, success, and recoverable failure states.
Persist job state so an interrupted transcription or render can be retried safely.
Put the OpenAI key in .env, ship .env.example, and never log secrets or transcript contents.
Add safe filenames, upload size limits, input validation, and explicit local data deletion.
Write focused tests for time-range validation, candidate overlap, caption grouping, and project persistence.
Add one end-to-end smoke test using a tiny generated video fixture.
Create a README with setup, FFmpeg installation, architecture, data location, costs, backup, and limitations.
Deliberately exclude smart reframing, B-roll generation, voice isolation, silence cleanup, and a complete multitrack NLE.
Deliberately exclude the public API, CLI, agent integrations, accounts, teams, cloud rendering, share pages, and social publishing.
Do not call this a BlitzReels clone; label it a narrow local clip finder and exporter.
Run the tests and a real sample render before finishing, then report the exact commands and output path.

## 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 BlitzReels capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# BlitzReels indie build

## Goal

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

## Scope

Import one long video, transcribe it, rank clip candidates, let the user adjust boundaries and captions, then export a 9:16 MP4.

## 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:
- reliable ingestion and recovery for large or malformed recordings
- production-tuned clip selection and timestamp alignment
- subject-aware speaker and screen reframing
- complete NLE, caption styles, paid templates, and keyframed timeline editing
- public API, CLI, agent integrations, cloud rendering, retries, and share workflows

If those capabilities are essential, use auto-editor 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 the closest honest personal substitute for BlitzReels in an empty repository.
Use exactly Node.js 22, TypeScript, React, Remotion, SQLite, FFmpeg, and the OpenAI API.
Run as a single-user local web app with one documented command.
The core loop is import one user-owned long video, transcribe it, rank five clip candidates, edit one, and export 9:16 MP4.
Preserve the original file and store projects, transcripts, candidates, and edits in SQLite.
Use ffprobe for duration and stream metadata before accepting a file.
Extract audio with FFmpeg and transcribe it with word timestamps.
Ask a text model for five 30 to 90 second candidates with exact boundaries, titles, hooks, and reasons.
Reject overlapping or out-of-range candidates and show the model output as suggestions, never facts.
Let the user adjust in and out points, edit caption text, and preview the vertical composition.
Use a static center crop with a manual horizontal offset; do not claim subject tracking.
Render burned captions from verified word timings with one readable style.
Export through Remotion to a new file and never overwrite source media.
Show import, probing, transcription, analysis, preview, rendering, success, and recoverable failure states.
Persist job state so an interrupted transcription or render can be retried safely.
Put the OpenAI key in .env, ship .env.example, and never log secrets or transcript contents.
Add safe filenames, upload size limits, input validation, and explicit local data deletion.
Write focused tests for time-range validation, candidate overlap, caption grouping, and project persistence.
Add one end-to-end smoke test using a tiny generated video fixture.
Create a README with setup, FFmpeg installation, architecture, data location, costs, backup, and limitations.
Deliberately exclude smart reframing, B-roll generation, voice isolation, silence cleanup, and a complete multitrack NLE.
Deliberately exclude the public API, CLI, agent integrations, accounts, teams, cloud rendering, share pages, and social publishing.
Do not call this a BlitzReels clone; label it a narrow local clip finder and exporter.
Run the tests and a real sample render before finishing, then report the exact commands and output path.

## Required capabilities

- Node.js 22
- FFmpeg and ffprobe
- OpenAI API key in .env
- local disk space for source and rendered media

## 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.

# BlitzReels product brief

## Problem

A toy clip finder is a one-session build. A credible BlitzReels replacement is not: the product combines reliable long-video ingestion, timestamp-accurate AI selection, smart reframing, a complete nonlinear editor, browser preview and export parity, public API and CLI contracts, agent integrations, storage, and render orchestration. Its founder reports that the production system took a year even with prior Remotion experience and paid templates.

## Product outcome

Import one long video, transcribe it, rank clip candidates, let the user adjust boundaries and captions, then export a 9:16 MP4.

## Target user

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

## Required capabilities

- Node.js 22
- FFmpeg and ffprobe
- OpenAI API key in .env
- local disk space for source and rendered media

## Explicit non-goals for v1

- reliable ingestion and recovery for large or malformed recordings
- production-tuned clip selection and timestamp alignment
- subject-aware speaker and screen reframing
- complete NLE, caption styles, paid templates, and keyframed timeline editing
- public API, CLI, agent integrations, cloud rendering, retries, and share workflows

## 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 the closest honest personal substitute for BlitzReels in an empty repository.
Use exactly Node.js 22, TypeScript, React, Remotion, SQLite, FFmpeg, and the OpenAI API.
Run as a single-user local web app with one documented command.
The core loop is import one user-owned long video, transcribe it, rank five clip candidates, edit one, and export 9:16 MP4.
Preserve the original file and store projects, transcripts, candidates, and edits in SQLite.
Use ffprobe for duration and stream metadata before accepting a file.
Extract audio with FFmpeg and transcribe it with word timestamps.
Ask a text model for five 30 to 90 second candidates with exact boundaries, titles, hooks, and reasons.
Reject overlapping or out-of-range candidates and show the model output as suggestions, never facts.
Let the user adjust in and out points, edit caption text, and preview the vertical composition.
Use a static center crop with a manual horizontal offset; do not claim subject tracking.
Render burned captions from verified word timings with one readable style.
Export through Remotion to a new file and never overwrite source media.
Show import, probing, transcription, analysis, preview, rendering, success, and recoverable failure states.
Persist job state so an interrupted transcription or render can be retried safely.
Put the OpenAI key in .env, ship .env.example, and never log secrets or transcript contents.
Add safe filenames, upload size limits, input validation, and explicit local data deletion.
Write focused tests for time-range validation, candidate overlap, caption grouping, and project persistence.
Add one end-to-end smoke test using a tiny generated video fixture.
Create a README with setup, FFmpeg installation, architecture, data location, costs, backup, and limitations.
Deliberately exclude smart reframing, B-roll generation, voice isolation, silence cleanup, and a complete multitrack NLE.
Deliberately exclude the public API, CLI, agent integrations, accounts, teams, cloud rendering, share pages, and social publishing.
Do not call this a BlitzReels clone; label it a narrow local clip finder and exporter.
Run the tests and a real sample render before finishing, then report the exact commands and output path.

## 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 BlitzReels 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