Vibecode Otter.ai

You are building a lean indie version of Otter.ai.

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 =====
# Otter.ai indie build

## Goal

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

## Scope

Use a meeting bot or local recorder, run transcription, diarize speakers, summarize, then expose search/chat over transcripts.

## 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:
- live bot joining meetings
- speaker diarization quality
- mobile apps
- team/admin controls
- searchable account history
- integrations

If those capabilities are essential, use whisperX 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 meeting transcription and search tool to replace Otter.ai.
Requirements:

- Python stack: whisperX (faster-whisper backend) for transcription, Flask for the UI,
  stdlib sqlite3 for storage.
- A CLI: `otter record` captures the mic to ~/Meetings/YYYY-MM-DD-HHMM/audio.wav; `otter
  import file.m4a` handles recordings made elsewhere.
- Transcribe locally with whisperX, word timestamps plus speaker diarization; label
  speakers SPEAKER_1/2 and let me rename them once per meeting.
- Send the transcript to an LLM (key in .env) for a summary: 5 bullets, decisions made,
  action items with owners. Save transcript.md and summary.md next to the audio.
- Index transcripts into SQLite FTS5; `otter search "budget"` returns matching lines
  with meeting date and timestamp.
- A minimal page on localhost:8787: meeting list, one search box, and an ask box that
  answers questions over a chosen transcript via the LLM.
- Everything stays on my machine except the LLM calls; no accounts, no telemetry.
- Out of scope: a bot that joins Zoom/Meet calls, mobile apps, and team sharing.
  Diarization will be rough on crosstalk, accept it.
- README: Python and ffmpeg install, model download size, and the macOS mic permission.

## Required capabilities

- speech-to-text API or local Whisper
- storage/search index
- calendar/video-call integration if bot-style capture is desired

## 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 Otter.ai.

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 =====
# Otter.ai indie build

## Goal

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

## Scope

Use a meeting bot or local recorder, run transcription, diarize speakers, summarize, then expose search/chat over transcripts.

## 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:
- live bot joining meetings
- speaker diarization quality
- mobile apps
- team/admin controls
- searchable account history
- integrations

If those capabilities are essential, use whisperX 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 meeting transcription and search tool to replace Otter.ai.
Requirements:

- Python stack: whisperX (faster-whisper backend) for transcription, Flask for the UI,
  stdlib sqlite3 for storage.
- A CLI: `otter record` captures the mic to ~/Meetings/YYYY-MM-DD-HHMM/audio.wav; `otter
  import file.m4a` handles recordings made elsewhere.
- Transcribe locally with whisperX, word timestamps plus speaker diarization; label
  speakers SPEAKER_1/2 and let me rename them once per meeting.
- Send the transcript to an LLM (key in .env) for a summary: 5 bullets, decisions made,
  action items with owners. Save transcript.md and summary.md next to the audio.
- Index transcripts into SQLite FTS5; `otter search "budget"` returns matching lines
  with meeting date and timestamp.
- A minimal page on localhost:8787: meeting list, one search box, and an ask box that
  answers questions over a chosen transcript via the LLM.
- Everything stays on my machine except the LLM calls; no accounts, no telemetry.
- Out of scope: a bot that joins Zoom/Meet calls, mobile apps, and team sharing.
  Diarization will be rough on crosstalk, accept it.
- README: Python and ffmpeg install, model download size, and the macOS mic permission.

## Required capabilities

- speech-to-text API or local Whisper
- storage/search index
- calendar/video-call integration if bot-style capture is desired

## 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 Otter.ai.

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 =====
# Otter.ai product brief

## Problem

You can build transcription and summaries, but Otter's value includes live meeting assistant behavior, account sync, speaker workflow, integrations, and mobile/web reliability.

## Product outcome

Use a meeting bot or local recorder, run transcription, diarize speakers, summarize, then expose search/chat over transcripts.

## Target user

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

## Required capabilities

- speech-to-text API or local Whisper
- storage/search index
- calendar/video-call integration if bot-style capture is desired

## Explicit non-goals for v1

- live bot joining meetings
- speaker diarization quality
- mobile apps
- team/admin controls
- searchable account history
- integrations

## 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 meeting transcription and search tool to replace Otter.ai.
Requirements:

- Python stack: whisperX (faster-whisper backend) for transcription, Flask for the UI,
  stdlib sqlite3 for storage.
- A CLI: `otter record` captures the mic to ~/Meetings/YYYY-MM-DD-HHMM/audio.wav; `otter
  import file.m4a` handles recordings made elsewhere.
- Transcribe locally with whisperX, word timestamps plus speaker diarization; label
  speakers SPEAKER_1/2 and let me rename them once per meeting.
- Send the transcript to an LLM (key in .env) for a summary: 5 bullets, decisions made,
  action items with owners. Save transcript.md and summary.md next to the audio.
- Index transcripts into SQLite FTS5; `otter search "budget"` returns matching lines
  with meeting date and timestamp.
- A minimal page on localhost:8787: meeting list, one search box, and an ask box that
  answers questions over a chosen transcript via the LLM.
- Everything stays on my machine except the LLM calls; no accounts, no telemetry.
- Out of scope: a bot that joins Zoom/Meet calls, mobile apps, and team sharing.
  Diarization will be rough on crosstalk, accept it.
- README: Python and ffmpeg install, model download size, and the macOS mic permission.

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

# Otter.ai indie build

## Goal

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

## Scope

Use a meeting bot or local recorder, run transcription, diarize speakers, summarize, then expose search/chat over transcripts.

## 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:
- live bot joining meetings
- speaker diarization quality
- mobile apps
- team/admin controls
- searchable account history
- integrations

If those capabilities are essential, use whisperX 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 meeting transcription and search tool to replace Otter.ai.
Requirements:

- Python stack: whisperX (faster-whisper backend) for transcription, Flask for the UI,
  stdlib sqlite3 for storage.
- A CLI: `otter record` captures the mic to ~/Meetings/YYYY-MM-DD-HHMM/audio.wav; `otter
  import file.m4a` handles recordings made elsewhere.
- Transcribe locally with whisperX, word timestamps plus speaker diarization; label
  speakers SPEAKER_1/2 and let me rename them once per meeting.
- Send the transcript to an LLM (key in .env) for a summary: 5 bullets, decisions made,
  action items with owners. Save transcript.md and summary.md next to the audio.
- Index transcripts into SQLite FTS5; `otter search "budget"` returns matching lines
  with meeting date and timestamp.
- A minimal page on localhost:8787: meeting list, one search box, and an ask box that
  answers questions over a chosen transcript via the LLM.
- Everything stays on my machine except the LLM calls; no accounts, no telemetry.
- Out of scope: a bot that joins Zoom/Meet calls, mobile apps, and team sharing.
  Diarization will be rough on crosstalk, accept it.
- README: Python and ffmpeg install, model download size, and the macOS mic permission.

## Required capabilities

- speech-to-text API or local Whisper
- storage/search index
- calendar/video-call integration if bot-style capture is desired

## 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.

# Otter.ai product brief

## Problem

You can build transcription and summaries, but Otter's value includes live meeting assistant behavior, account sync, speaker workflow, integrations, and mobile/web reliability.

## Product outcome

Use a meeting bot or local recorder, run transcription, diarize speakers, summarize, then expose search/chat over transcripts.

## Target user

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

## Required capabilities

- speech-to-text API or local Whisper
- storage/search index
- calendar/video-call integration if bot-style capture is desired

## Explicit non-goals for v1

- live bot joining meetings
- speaker diarization quality
- mobile apps
- team/admin controls
- searchable account history
- integrations

## 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 meeting transcription and search tool to replace Otter.ai.
Requirements:

- Python stack: whisperX (faster-whisper backend) for transcription, Flask for the UI,
  stdlib sqlite3 for storage.
- A CLI: `otter record` captures the mic to ~/Meetings/YYYY-MM-DD-HHMM/audio.wav; `otter
  import file.m4a` handles recordings made elsewhere.
- Transcribe locally with whisperX, word timestamps plus speaker diarization; label
  speakers SPEAKER_1/2 and let me rename them once per meeting.
- Send the transcript to an LLM (key in .env) for a summary: 5 bullets, decisions made,
  action items with owners. Save transcript.md and summary.md next to the audio.
- Index transcripts into SQLite FTS5; `otter search "budget"` returns matching lines
  with meeting date and timestamp.
- A minimal page on localhost:8787: meeting list, one search box, and an ask box that
  answers questions over a chosen transcript via the LLM.
- Everything stays on my machine except the LLM calls; no accounts, no telemetry.
- Out of scope: a bot that joins Zoom/Meet calls, mobile apps, and team sharing.
  Diarization will be rough on crosstalk, accept it.
- README: Python and ffmpeg install, model download size, and the macOS mic permission.

## 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 Otter.ai 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 · this prompt is generated from the build plan · improve it via PR