Vibecode Speechify

You are building a lean indie version of Speechify.

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 =====
# Speechify indie build

## Goal

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

## Scope

Import text, PDFs, EPUBs, and web pages, extract their readable text, then play it through a local neural TTS engine with highlighting, speed controls, and saved progress.

## 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:
- Speechify's 1000+ hosted voices and consistent quality across devices
- mobile scanning and polished OCR capture
- cross-device sync and offline native apps
- Google Drive, Dropbox, and OneDrive integrations
- voice typing, AI podcasts, and document chat

If those capabilities are essential, use Kokoro 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 a local-first personal document reader that covers Speechify's core loop.
Use Node.js 22, Express, better-sqlite3, and vanilla browser JavaScript; do not offer alternative stacks.
Serve only on 127.0.0.1 by default and require no account.
Import pasted text and TXT, Markdown, HTML, PDF, EPUB, PNG, and JPEG files.
Use Mozilla Readability for HTML, PDF.js for PDFs, epub.js for EPUBs, and Tesseract.js for image OCR.
Normalize extracted content into chapters and paragraphs while preserving headings.
Send sentence-sized chunks to a local OpenAI-compatible Kokoro TTS server and cache the returned audio.
Let the user choose a local voice, playback rate, and volume per document.
Highlight the current sentence and scroll it into view as its audio plays.
Add play, pause, stop, sentence skip, chapter skip, and click-any-paragraph to start.
Save document metadata, extracted text, current position, and reading settings in SQLite.
Build a library with recent documents, progress, search, delete, and plain-text export.
Accept public webpage URLs only after blocking loopback, private, link-local, and non-HTTP addresses, including redirects.
Keep uploaded files and extracted text local; make no cloud calls except a URL the user asks to import.
Do not require a hosted speech API or an API key.
Include clear empty, extracting, ready, playing, paused, and recoverable error states.
Reject oversized or unsupported uploads and use safe generated filenames.
Write focused tests for each parser, saved reading position, and blocked private-network URLs.
Add one end-to-end test that imports a document, starts playback, and resumes its saved position.
Create a README with setup, supported formats, data location, backup, deletion, and test commands.
Do not add accounts, billing, telemetry, analytics, or public hosting.
Deliberately leave out mobile apps, cross-device sync, Speechify's hosted voice catalog, voice cloning, voice typing, AI podcasts, and document chat.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- Node.js 22
- local OpenAI-compatible Kokoro TTS server
- PDF, EPUB, and OCR parsing libraries

## 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 Speechify.

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 =====
# Speechify indie build

## Goal

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

## Scope

Import text, PDFs, EPUBs, and web pages, extract their readable text, then play it through a local neural TTS engine with highlighting, speed controls, and saved progress.

## 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:
- Speechify's 1000+ hosted voices and consistent quality across devices
- mobile scanning and polished OCR capture
- cross-device sync and offline native apps
- Google Drive, Dropbox, and OneDrive integrations
- voice typing, AI podcasts, and document chat

If those capabilities are essential, use Kokoro 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 a local-first personal document reader that covers Speechify's core loop.
Use Node.js 22, Express, better-sqlite3, and vanilla browser JavaScript; do not offer alternative stacks.
Serve only on 127.0.0.1 by default and require no account.
Import pasted text and TXT, Markdown, HTML, PDF, EPUB, PNG, and JPEG files.
Use Mozilla Readability for HTML, PDF.js for PDFs, epub.js for EPUBs, and Tesseract.js for image OCR.
Normalize extracted content into chapters and paragraphs while preserving headings.
Send sentence-sized chunks to a local OpenAI-compatible Kokoro TTS server and cache the returned audio.
Let the user choose a local voice, playback rate, and volume per document.
Highlight the current sentence and scroll it into view as its audio plays.
Add play, pause, stop, sentence skip, chapter skip, and click-any-paragraph to start.
Save document metadata, extracted text, current position, and reading settings in SQLite.
Build a library with recent documents, progress, search, delete, and plain-text export.
Accept public webpage URLs only after blocking loopback, private, link-local, and non-HTTP addresses, including redirects.
Keep uploaded files and extracted text local; make no cloud calls except a URL the user asks to import.
Do not require a hosted speech API or an API key.
Include clear empty, extracting, ready, playing, paused, and recoverable error states.
Reject oversized or unsupported uploads and use safe generated filenames.
Write focused tests for each parser, saved reading position, and blocked private-network URLs.
Add one end-to-end test that imports a document, starts playback, and resumes its saved position.
Create a README with setup, supported formats, data location, backup, deletion, and test commands.
Do not add accounts, billing, telemetry, analytics, or public hosting.
Deliberately leave out mobile apps, cross-device sync, Speechify's hosted voice catalog, voice cloning, voice typing, AI podcasts, and document chat.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- Node.js 22
- local OpenAI-compatible Kokoro TTS server
- PDF, EPUB, and OCR parsing libraries

## 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 Speechify.

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 =====
# Speechify product brief

## Problem

Speechify's core reading loop is a weekend build: import documents, extract text, read it aloud with a natural local voice, highlight the current sentence, and save progress. The gap is product depth, including the size and consistency of Speechify's hosted voice catalog, OCR and mobile capture, cross-device sync, cloud-drive integrations, voice typing, AI podcasts, and document chat.

## Product outcome

Import text, PDFs, EPUBs, and web pages, extract their readable text, then play it through a local neural TTS engine with highlighting, speed controls, and saved progress.

## Target user

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

## Required capabilities

- Node.js 22
- local OpenAI-compatible Kokoro TTS server
- PDF, EPUB, and OCR parsing libraries

## Explicit non-goals for v1

- Speechify's 1000+ hosted voices and consistent quality across devices
- mobile scanning and polished OCR capture
- cross-device sync and offline native apps
- Google Drive, Dropbox, and OneDrive integrations
- voice typing, AI podcasts, and document chat

## 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 a local-first personal document reader that covers Speechify's core loop.
Use Node.js 22, Express, better-sqlite3, and vanilla browser JavaScript; do not offer alternative stacks.
Serve only on 127.0.0.1 by default and require no account.
Import pasted text and TXT, Markdown, HTML, PDF, EPUB, PNG, and JPEG files.
Use Mozilla Readability for HTML, PDF.js for PDFs, epub.js for EPUBs, and Tesseract.js for image OCR.
Normalize extracted content into chapters and paragraphs while preserving headings.
Send sentence-sized chunks to a local OpenAI-compatible Kokoro TTS server and cache the returned audio.
Let the user choose a local voice, playback rate, and volume per document.
Highlight the current sentence and scroll it into view as its audio plays.
Add play, pause, stop, sentence skip, chapter skip, and click-any-paragraph to start.
Save document metadata, extracted text, current position, and reading settings in SQLite.
Build a library with recent documents, progress, search, delete, and plain-text export.
Accept public webpage URLs only after blocking loopback, private, link-local, and non-HTTP addresses, including redirects.
Keep uploaded files and extracted text local; make no cloud calls except a URL the user asks to import.
Do not require a hosted speech API or an API key.
Include clear empty, extracting, ready, playing, paused, and recoverable error states.
Reject oversized or unsupported uploads and use safe generated filenames.
Write focused tests for each parser, saved reading position, and blocked private-network URLs.
Add one end-to-end test that imports a document, starts playback, and resumes its saved position.
Create a README with setup, supported formats, data location, backup, deletion, and test commands.
Do not add accounts, billing, telemetry, analytics, or public hosting.
Deliberately leave out mobile apps, cross-device sync, Speechify's hosted voice catalog, voice cloning, voice typing, AI podcasts, and document chat.
Finish by running the tests and listing the exact commands used.

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

# Speechify indie build

## Goal

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

## Scope

Import text, PDFs, EPUBs, and web pages, extract their readable text, then play it through a local neural TTS engine with highlighting, speed controls, and saved progress.

## 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:
- Speechify's 1000+ hosted voices and consistent quality across devices
- mobile scanning and polished OCR capture
- cross-device sync and offline native apps
- Google Drive, Dropbox, and OneDrive integrations
- voice typing, AI podcasts, and document chat

If those capabilities are essential, use Kokoro 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 a local-first personal document reader that covers Speechify's core loop.
Use Node.js 22, Express, better-sqlite3, and vanilla browser JavaScript; do not offer alternative stacks.
Serve only on 127.0.0.1 by default and require no account.
Import pasted text and TXT, Markdown, HTML, PDF, EPUB, PNG, and JPEG files.
Use Mozilla Readability for HTML, PDF.js for PDFs, epub.js for EPUBs, and Tesseract.js for image OCR.
Normalize extracted content into chapters and paragraphs while preserving headings.
Send sentence-sized chunks to a local OpenAI-compatible Kokoro TTS server and cache the returned audio.
Let the user choose a local voice, playback rate, and volume per document.
Highlight the current sentence and scroll it into view as its audio plays.
Add play, pause, stop, sentence skip, chapter skip, and click-any-paragraph to start.
Save document metadata, extracted text, current position, and reading settings in SQLite.
Build a library with recent documents, progress, search, delete, and plain-text export.
Accept public webpage URLs only after blocking loopback, private, link-local, and non-HTTP addresses, including redirects.
Keep uploaded files and extracted text local; make no cloud calls except a URL the user asks to import.
Do not require a hosted speech API or an API key.
Include clear empty, extracting, ready, playing, paused, and recoverable error states.
Reject oversized or unsupported uploads and use safe generated filenames.
Write focused tests for each parser, saved reading position, and blocked private-network URLs.
Add one end-to-end test that imports a document, starts playback, and resumes its saved position.
Create a README with setup, supported formats, data location, backup, deletion, and test commands.
Do not add accounts, billing, telemetry, analytics, or public hosting.
Deliberately leave out mobile apps, cross-device sync, Speechify's hosted voice catalog, voice cloning, voice typing, AI podcasts, and document chat.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- Node.js 22
- local OpenAI-compatible Kokoro TTS server
- PDF, EPUB, and OCR parsing libraries

## 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.

# Speechify product brief

## Problem

Speechify's core reading loop is a weekend build: import documents, extract text, read it aloud with a natural local voice, highlight the current sentence, and save progress. The gap is product depth, including the size and consistency of Speechify's hosted voice catalog, OCR and mobile capture, cross-device sync, cloud-drive integrations, voice typing, AI podcasts, and document chat.

## Product outcome

Import text, PDFs, EPUBs, and web pages, extract their readable text, then play it through a local neural TTS engine with highlighting, speed controls, and saved progress.

## Target user

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

## Required capabilities

- Node.js 22
- local OpenAI-compatible Kokoro TTS server
- PDF, EPUB, and OCR parsing libraries

## Explicit non-goals for v1

- Speechify's 1000+ hosted voices and consistent quality across devices
- mobile scanning and polished OCR capture
- cross-device sync and offline native apps
- Google Drive, Dropbox, and OneDrive integrations
- voice typing, AI podcasts, and document chat

## 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 a local-first personal document reader that covers Speechify's core loop.
Use Node.js 22, Express, better-sqlite3, and vanilla browser JavaScript; do not offer alternative stacks.
Serve only on 127.0.0.1 by default and require no account.
Import pasted text and TXT, Markdown, HTML, PDF, EPUB, PNG, and JPEG files.
Use Mozilla Readability for HTML, PDF.js for PDFs, epub.js for EPUBs, and Tesseract.js for image OCR.
Normalize extracted content into chapters and paragraphs while preserving headings.
Send sentence-sized chunks to a local OpenAI-compatible Kokoro TTS server and cache the returned audio.
Let the user choose a local voice, playback rate, and volume per document.
Highlight the current sentence and scroll it into view as its audio plays.
Add play, pause, stop, sentence skip, chapter skip, and click-any-paragraph to start.
Save document metadata, extracted text, current position, and reading settings in SQLite.
Build a library with recent documents, progress, search, delete, and plain-text export.
Accept public webpage URLs only after blocking loopback, private, link-local, and non-HTTP addresses, including redirects.
Keep uploaded files and extracted text local; make no cloud calls except a URL the user asks to import.
Do not require a hosted speech API or an API key.
Include clear empty, extracting, ready, playing, paused, and recoverable error states.
Reject oversized or unsupported uploads and use safe generated filenames.
Write focused tests for each parser, saved reading position, and blocked private-network URLs.
Add one end-to-end test that imports a document, starts playback, and resumes its saved position.
Create a README with setup, supported formats, data location, backup, deletion, and test commands.
Do not add accounts, billing, telemetry, analytics, or public hosting.
Deliberately leave out mobile apps, cross-device sync, Speechify's hosted voice catalog, voice cloning, voice typing, AI podcasts, and document chat.
Finish by running the tests and listing the exact commands used.

## 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 Speechify 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