Vibecode Krisp

You are building a lean indie version of Krisp.

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 =====
# Krisp indie build

## Goal

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

## Scope

Use an open noise-suppression model as a virtual microphone or post-process recordings before sending them to calls or files.

## 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:
- low-latency virtual microphone
- polished app switching
- model quality
- meeting integrations
- admin controls

If those capabilities are essential, use RNNoise 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 noise-cleanup tool to replace Krisp for recordings, not live calls. Requirements:

- Be honest in the README up front: real-time suppression as a virtual microphone
  needs an OS-level audio driver and millisecond latency. That is Krisp's actual
  product and it is out of scope here. This tool cleans audio files after the fact.
- A CLI: `denoise in.wav` (mp3/m4a accepted, decoded via ffmpeg) writes
  in.clean.wav next to the original, original untouched.
- Use RNNoise for the suppression pass (ffmpeg's arnndn filter with a downloaded
  model file is the easy route), then a loudnorm pass so voice levels come out
  consistent.
- A watch mode with chokidar: drop files into ~/Denoise/in/ and cleaned versions
  appear in ~/Denoise/out/ with the same names.
- Batch mode for whole folders, with a printed before/after noise estimate per file.
- Node wrapping ffmpeg, or a plain bash script if that ships simpler. No server,
  no GUI.
- Fully local and offline: no accounts, no telemetry, no API calls.
- Out of scope: live call processing and a virtual audio device. For live calls the
  README should point me at the OS's built-in voice isolation instead.
- README: ffmpeg and RNNoise model install, one-line usage examples.

## Required capabilities

- audio DSP library
- OS-level virtual audio device
- model/runtime optimized for low latency
- macOS/Windows audio permissions

## 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 Krisp.

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 =====
# Krisp indie build

## Goal

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

## Scope

Use an open noise-suppression model as a virtual microphone or post-process recordings before sending them to calls or files.

## 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:
- low-latency virtual microphone
- polished app switching
- model quality
- meeting integrations
- admin controls

If those capabilities are essential, use RNNoise 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 noise-cleanup tool to replace Krisp for recordings, not live calls. Requirements:

- Be honest in the README up front: real-time suppression as a virtual microphone
  needs an OS-level audio driver and millisecond latency. That is Krisp's actual
  product and it is out of scope here. This tool cleans audio files after the fact.
- A CLI: `denoise in.wav` (mp3/m4a accepted, decoded via ffmpeg) writes
  in.clean.wav next to the original, original untouched.
- Use RNNoise for the suppression pass (ffmpeg's arnndn filter with a downloaded
  model file is the easy route), then a loudnorm pass so voice levels come out
  consistent.
- A watch mode with chokidar: drop files into ~/Denoise/in/ and cleaned versions
  appear in ~/Denoise/out/ with the same names.
- Batch mode for whole folders, with a printed before/after noise estimate per file.
- Node wrapping ffmpeg, or a plain bash script if that ships simpler. No server,
  no GUI.
- Fully local and offline: no accounts, no telemetry, no API calls.
- Out of scope: live call processing and a virtual audio device. For live calls the
  README should point me at the OS's built-in voice isolation instead.
- README: ffmpeg and RNNoise model install, one-line usage examples.

## Required capabilities

- audio DSP library
- OS-level virtual audio device
- model/runtime optimized for low latency
- macOS/Windows audio permissions

## 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 Krisp.

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 =====
# Krisp product brief

## Problem

You can build post-processing and maybe route audio through open models, but real-time low-latency virtual-device noise cancellation is not a one-sitting web app.

## Product outcome

Use an open noise-suppression model as a virtual microphone or post-process recordings before sending them to calls or files.

## Target user

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

## Required capabilities

- audio DSP library
- OS-level virtual audio device
- model/runtime optimized for low latency
- macOS/Windows audio permissions

## Explicit non-goals for v1

- low-latency virtual microphone
- polished app switching
- model quality
- meeting integrations
- admin controls

## 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 noise-cleanup tool to replace Krisp for recordings, not live calls. Requirements:

- Be honest in the README up front: real-time suppression as a virtual microphone
  needs an OS-level audio driver and millisecond latency. That is Krisp's actual
  product and it is out of scope here. This tool cleans audio files after the fact.
- A CLI: `denoise in.wav` (mp3/m4a accepted, decoded via ffmpeg) writes
  in.clean.wav next to the original, original untouched.
- Use RNNoise for the suppression pass (ffmpeg's arnndn filter with a downloaded
  model file is the easy route), then a loudnorm pass so voice levels come out
  consistent.
- A watch mode with chokidar: drop files into ~/Denoise/in/ and cleaned versions
  appear in ~/Denoise/out/ with the same names.
- Batch mode for whole folders, with a printed before/after noise estimate per file.
- Node wrapping ffmpeg, or a plain bash script if that ships simpler. No server,
  no GUI.
- Fully local and offline: no accounts, no telemetry, no API calls.
- Out of scope: live call processing and a virtual audio device. For live calls the
  README should point me at the OS's built-in voice isolation instead.
- README: ffmpeg and RNNoise model install, one-line usage examples.

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

# Krisp indie build

## Goal

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

## Scope

Use an open noise-suppression model as a virtual microphone or post-process recordings before sending them to calls or files.

## 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:
- low-latency virtual microphone
- polished app switching
- model quality
- meeting integrations
- admin controls

If those capabilities are essential, use RNNoise 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 noise-cleanup tool to replace Krisp for recordings, not live calls. Requirements:

- Be honest in the README up front: real-time suppression as a virtual microphone
  needs an OS-level audio driver and millisecond latency. That is Krisp's actual
  product and it is out of scope here. This tool cleans audio files after the fact.
- A CLI: `denoise in.wav` (mp3/m4a accepted, decoded via ffmpeg) writes
  in.clean.wav next to the original, original untouched.
- Use RNNoise for the suppression pass (ffmpeg's arnndn filter with a downloaded
  model file is the easy route), then a loudnorm pass so voice levels come out
  consistent.
- A watch mode with chokidar: drop files into ~/Denoise/in/ and cleaned versions
  appear in ~/Denoise/out/ with the same names.
- Batch mode for whole folders, with a printed before/after noise estimate per file.
- Node wrapping ffmpeg, or a plain bash script if that ships simpler. No server,
  no GUI.
- Fully local and offline: no accounts, no telemetry, no API calls.
- Out of scope: live call processing and a virtual audio device. For live calls the
  README should point me at the OS's built-in voice isolation instead.
- README: ffmpeg and RNNoise model install, one-line usage examples.

## Required capabilities

- audio DSP library
- OS-level virtual audio device
- model/runtime optimized for low latency
- macOS/Windows audio permissions

## 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.

# Krisp product brief

## Problem

You can build post-processing and maybe route audio through open models, but real-time low-latency virtual-device noise cancellation is not a one-sitting web app.

## Product outcome

Use an open noise-suppression model as a virtual microphone or post-process recordings before sending them to calls or files.

## Target user

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

## Required capabilities

- audio DSP library
- OS-level virtual audio device
- model/runtime optimized for low latency
- macOS/Windows audio permissions

## Explicit non-goals for v1

- low-latency virtual microphone
- polished app switching
- model quality
- meeting integrations
- admin controls

## 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 noise-cleanup tool to replace Krisp for recordings, not live calls. Requirements:

- Be honest in the README up front: real-time suppression as a virtual microphone
  needs an OS-level audio driver and millisecond latency. That is Krisp's actual
  product and it is out of scope here. This tool cleans audio files after the fact.
- A CLI: `denoise in.wav` (mp3/m4a accepted, decoded via ffmpeg) writes
  in.clean.wav next to the original, original untouched.
- Use RNNoise for the suppression pass (ffmpeg's arnndn filter with a downloaded
  model file is the easy route), then a loudnorm pass so voice levels come out
  consistent.
- A watch mode with chokidar: drop files into ~/Denoise/in/ and cleaned versions
  appear in ~/Denoise/out/ with the same names.
- Batch mode for whole folders, with a printed before/after noise estimate per file.
- Node wrapping ffmpeg, or a plain bash script if that ships simpler. No server,
  no GUI.
- Fully local and offline: no accounts, no telemetry, no API calls.
- Out of scope: live call processing and a virtual audio device. For live calls the
  README should point me at the OS's built-in voice isolation instead.
- README: ffmpeg and RNNoise model install, one-line usage examples.

## 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 Krisp 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