Vibecode ReplyGain

You are building a lean indie version of ReplyGain.

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 =====
# ReplyGain indie build

## Goal

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

## Scope

Collect public posts from Reddit and Hacker News, match them against product keywords, score likely buying intent with an LLM, and show an editable suggested reply in a local dashboard.

## 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:
- Four monitored platforms in one service, including Bluesky and X
- scheduled scans, deduplication, and long-term lead history
- production alerts through email and Telegram
- connector maintenance when a platform changes its API or feed
- billing, quotas, account management, and multi-website support

If those capabilities are essential, use Reddit JSON API 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, honest personal substitute for ReplyGain in an empty folder.

Stack: Node 22, TypeScript, Express, better-sqlite3, and vanilla HTML/CSS/JS. Do not use React or a hosted database.

Scope: Reddit and Hacker News only.

- Import recent public posts using Reddit JSON endpoints and the Hacker News Algolia API
- Let me enter a product description and keywords
- Store posts in SQLite with source, URL, title, text, author, score, and discoveredAt
- Use an OpenAI-compatible API from .env to classify purchase intent from 0 to 100
- Show a sortable local feed with intent score, source, matched keyword, and a link to the original post
- Generate an editable suggested reply for one selected lead
- Add seen, dismissed, and replied states; never post automatically
- Poll on demand only; no background worker, billing, login, Telegram, email, X, Bluesky, or multi-user features
- Include .env.example, README, seed data, and a smoke test that uses fixtures instead of live APIs
- Explain in README that APIs, rate limits, moderation rules, and platform changes make this a personal prototype
- Finish by listing the exact commands used to install, test, and run the app

## Required capabilities

- Node.js and SQLite
- Reddit and Hacker News public feeds or APIs
- OpenAI-compatible API key

## 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 ReplyGain.

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 =====
# ReplyGain indie build

## Goal

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

## Scope

Collect public posts from Reddit and Hacker News, match them against product keywords, score likely buying intent with an LLM, and show an editable suggested reply in a local dashboard.

## 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:
- Four monitored platforms in one service, including Bluesky and X
- scheduled scans, deduplication, and long-term lead history
- production alerts through email and Telegram
- connector maintenance when a platform changes its API or feed
- billing, quotas, account management, and multi-website support

If those capabilities are essential, use Reddit JSON API 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, honest personal substitute for ReplyGain in an empty folder.

Stack: Node 22, TypeScript, Express, better-sqlite3, and vanilla HTML/CSS/JS. Do not use React or a hosted database.

Scope: Reddit and Hacker News only.

- Import recent public posts using Reddit JSON endpoints and the Hacker News Algolia API
- Let me enter a product description and keywords
- Store posts in SQLite with source, URL, title, text, author, score, and discoveredAt
- Use an OpenAI-compatible API from .env to classify purchase intent from 0 to 100
- Show a sortable local feed with intent score, source, matched keyword, and a link to the original post
- Generate an editable suggested reply for one selected lead
- Add seen, dismissed, and replied states; never post automatically
- Poll on demand only; no background worker, billing, login, Telegram, email, X, Bluesky, or multi-user features
- Include .env.example, README, seed data, and a smoke test that uses fixtures instead of live APIs
- Explain in README that APIs, rate limits, moderation rules, and platform changes make this a personal prototype
- Finish by listing the exact commands used to install, test, and run the app

## Required capabilities

- Node.js and SQLite
- Reddit and Hacker News public feeds or APIs
- OpenAI-compatible API key

## 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 ReplyGain.

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 =====
# ReplyGain product brief

## Problem

A local Reddit keyword watcher with AI scoring and suggested replies is a weekend build. ReplyGain is more than that: it schedules ingestion across four platforms, normalizes and deduplicates posts, ranks purchase intent, sends alerts, generates replies, and keeps platform integrations working as APIs and feeds change. The core workflow is vibecodable, but a reliable multi-platform service has real maintenance and infrastructure gaps.

## Product outcome

Collect public posts from Reddit and Hacker News, match them against product keywords, score likely buying intent with an LLM, and show an editable suggested reply in a local dashboard.

## Target user

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

## Required capabilities

- Node.js and SQLite
- Reddit and Hacker News public feeds or APIs
- OpenAI-compatible API key

## Explicit non-goals for v1

- Four monitored platforms in one service, including Bluesky and X
- scheduled scans, deduplication, and long-term lead history
- production alerts through email and Telegram
- connector maintenance when a platform changes its API or feed
- billing, quotas, account management, and multi-website support

## 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, honest personal substitute for ReplyGain in an empty folder.

Stack: Node 22, TypeScript, Express, better-sqlite3, and vanilla HTML/CSS/JS. Do not use React or a hosted database.

Scope: Reddit and Hacker News only.

- Import recent public posts using Reddit JSON endpoints and the Hacker News Algolia API
- Let me enter a product description and keywords
- Store posts in SQLite with source, URL, title, text, author, score, and discoveredAt
- Use an OpenAI-compatible API from .env to classify purchase intent from 0 to 100
- Show a sortable local feed with intent score, source, matched keyword, and a link to the original post
- Generate an editable suggested reply for one selected lead
- Add seen, dismissed, and replied states; never post automatically
- Poll on demand only; no background worker, billing, login, Telegram, email, X, Bluesky, or multi-user features
- Include .env.example, README, seed data, and a smoke test that uses fixtures instead of live APIs
- Explain in README that APIs, rate limits, moderation rules, and platform changes make this a personal prototype
- Finish by listing the exact commands used to install, test, and run the app

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

# ReplyGain indie build

## Goal

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

## Scope

Collect public posts from Reddit and Hacker News, match them against product keywords, score likely buying intent with an LLM, and show an editable suggested reply in a local dashboard.

## 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:
- Four monitored platforms in one service, including Bluesky and X
- scheduled scans, deduplication, and long-term lead history
- production alerts through email and Telegram
- connector maintenance when a platform changes its API or feed
- billing, quotas, account management, and multi-website support

If those capabilities are essential, use Reddit JSON API 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, honest personal substitute for ReplyGain in an empty folder.

Stack: Node 22, TypeScript, Express, better-sqlite3, and vanilla HTML/CSS/JS. Do not use React or a hosted database.

Scope: Reddit and Hacker News only.

- Import recent public posts using Reddit JSON endpoints and the Hacker News Algolia API
- Let me enter a product description and keywords
- Store posts in SQLite with source, URL, title, text, author, score, and discoveredAt
- Use an OpenAI-compatible API from .env to classify purchase intent from 0 to 100
- Show a sortable local feed with intent score, source, matched keyword, and a link to the original post
- Generate an editable suggested reply for one selected lead
- Add seen, dismissed, and replied states; never post automatically
- Poll on demand only; no background worker, billing, login, Telegram, email, X, Bluesky, or multi-user features
- Include .env.example, README, seed data, and a smoke test that uses fixtures instead of live APIs
- Explain in README that APIs, rate limits, moderation rules, and platform changes make this a personal prototype
- Finish by listing the exact commands used to install, test, and run the app

## Required capabilities

- Node.js and SQLite
- Reddit and Hacker News public feeds or APIs
- OpenAI-compatible API key

## 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.

# ReplyGain product brief

## Problem

A local Reddit keyword watcher with AI scoring and suggested replies is a weekend build. ReplyGain is more than that: it schedules ingestion across four platforms, normalizes and deduplicates posts, ranks purchase intent, sends alerts, generates replies, and keeps platform integrations working as APIs and feeds change. The core workflow is vibecodable, but a reliable multi-platform service has real maintenance and infrastructure gaps.

## Product outcome

Collect public posts from Reddit and Hacker News, match them against product keywords, score likely buying intent with an LLM, and show an editable suggested reply in a local dashboard.

## Target user

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

## Required capabilities

- Node.js and SQLite
- Reddit and Hacker News public feeds or APIs
- OpenAI-compatible API key

## Explicit non-goals for v1

- Four monitored platforms in one service, including Bluesky and X
- scheduled scans, deduplication, and long-term lead history
- production alerts through email and Telegram
- connector maintenance when a platform changes its API or feed
- billing, quotas, account management, and multi-website support

## 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, honest personal substitute for ReplyGain in an empty folder.

Stack: Node 22, TypeScript, Express, better-sqlite3, and vanilla HTML/CSS/JS. Do not use React or a hosted database.

Scope: Reddit and Hacker News only.

- Import recent public posts using Reddit JSON endpoints and the Hacker News Algolia API
- Let me enter a product description and keywords
- Store posts in SQLite with source, URL, title, text, author, score, and discoveredAt
- Use an OpenAI-compatible API from .env to classify purchase intent from 0 to 100
- Show a sortable local feed with intent score, source, matched keyword, and a link to the original post
- Generate an editable suggested reply for one selected lead
- Add seen, dismissed, and replied states; never post automatically
- Poll on demand only; no background worker, billing, login, Telegram, email, X, Bluesky, or multi-user features
- Include .env.example, README, seed data, and a smoke test that uses fixtures instead of live APIs
- Explain in README that APIs, rate limits, moderation rules, and platform changes make this a personal prototype
- Finish by listing the exact commands used to install, test, and run the app

## 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 ReplyGain 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