Vibecode SocialFaktory

You are building a lean indie version of SocialFaktory.

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 =====
# SocialFaktory indie build

## Goal

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

## Scope

Turn a text brief into a script and a rendered short video via hosted AI model APIs, then queue and publish it through a self-hosted Postiz.

## 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:
- pre-approved platform OAuth apps (TikTok/Instagram/YouTube reviews take weeks)
- prompt pipelines tuned per model, format, and tone
- an automated QA pass before anything ships
- multi-brand workspaces and a scheduling calendar
- a flat bill · DIY pays every model invoice directly, and video models are not cheap

If those capabilities are essential, use Postiz 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 AI video factory to replace SocialFaktory. Requirements:

- Node + Express + better-sqlite3; a localhost dashboard with a brief form
  (topic, tone, target duration) and a queue of generated videos.
- Script step: send the brief to the Anthropic API and get back a hook line,
  a spoken script, and a visual prompt; keep the system prompt in
  prompts/script.txt so I can tune it without touching code.
- Video step: submit the visual prompt to fal.ai's queue API for a
  text-to-video model with native audio, poll until done, download the mp4
  into media/ and record the actual dollar cost per generation in the DB.
- Review page: each finished video gets a player plus approve, regenerate,
  and discard buttons; nothing publishes without an explicit approve.
- Publish step: on approve, POST the video and caption to my self-hosted
  Postiz via its public API (POSTIZ_URL and POSTIZ_API_KEY in .env) with a
  scheduled time · Postiz owns the platform OAuth; never call TikTok,
  Instagram, or YouTube APIs directly.
- A node-cron tick retries failed generations 3 times with backoff and marks
  rows before submitting so nothing ever double-bills.
- A running-costs box on the dashboard summing this month's model spend.
- Out of scope: multi-brand workspaces, teams, analytics, auto-QA, and any
  direct platform integrations (say so in the README).
- README: getting the two API keys, pointing at a Postiz instance, and what
  a single video roughly costs to generate.

## Required capabilities

- Anthropic or OpenAI API key for scripts
- video-generation API key (fal.ai or similar)
- self-hosted Postiz for the platform OAuth and publishing
- always-on box

## 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 SocialFaktory.

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 =====
# SocialFaktory indie build

## Goal

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

## Scope

Turn a text brief into a script and a rendered short video via hosted AI model APIs, then queue and publish it through a self-hosted Postiz.

## 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:
- pre-approved platform OAuth apps (TikTok/Instagram/YouTube reviews take weeks)
- prompt pipelines tuned per model, format, and tone
- an automated QA pass before anything ships
- multi-brand workspaces and a scheduling calendar
- a flat bill · DIY pays every model invoice directly, and video models are not cheap

If those capabilities are essential, use Postiz 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 AI video factory to replace SocialFaktory. Requirements:

- Node + Express + better-sqlite3; a localhost dashboard with a brief form
  (topic, tone, target duration) and a queue of generated videos.
- Script step: send the brief to the Anthropic API and get back a hook line,
  a spoken script, and a visual prompt; keep the system prompt in
  prompts/script.txt so I can tune it without touching code.
- Video step: submit the visual prompt to fal.ai's queue API for a
  text-to-video model with native audio, poll until done, download the mp4
  into media/ and record the actual dollar cost per generation in the DB.
- Review page: each finished video gets a player plus approve, regenerate,
  and discard buttons; nothing publishes without an explicit approve.
- Publish step: on approve, POST the video and caption to my self-hosted
  Postiz via its public API (POSTIZ_URL and POSTIZ_API_KEY in .env) with a
  scheduled time · Postiz owns the platform OAuth; never call TikTok,
  Instagram, or YouTube APIs directly.
- A node-cron tick retries failed generations 3 times with backoff and marks
  rows before submitting so nothing ever double-bills.
- A running-costs box on the dashboard summing this month's model spend.
- Out of scope: multi-brand workspaces, teams, analytics, auto-QA, and any
  direct platform integrations (say so in the README).
- README: getting the two API keys, pointing at a Postiz instance, and what
  a single video roughly costs to generate.

## Required capabilities

- Anthropic or OpenAI API key for scripts
- video-generation API key (fal.ai or similar)
- self-hosted Postiz for the platform OAuth and publishing
- always-on box

## 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 SocialFaktory.

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 =====
# SocialFaktory product brief

## Problem

The generation half is honest API glue: script writing is an LLM call and the video itself comes from hosted model APIs (fal.ai and friends), so a personal brief-to-video pipeline is very buildable · but you pay those per-generation model invoices yourself either way. The publishing half is the same wall every scheduler hits: TikTok, Instagram, and YouTube OAuth apps are review-gated for weeks, which is why SocialFaktory itself publishes through Postiz. The honest DIY is a generation script feeding a self-hosted Postiz, and what's left over is the tuned prompt pipelines, the QA pass, and someone else eating the platform-review pain.

## Product outcome

Turn a text brief into a script and a rendered short video via hosted AI model APIs, then queue and publish it through a self-hosted Postiz.

## Target user

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

## Required capabilities

- Anthropic or OpenAI API key for scripts
- video-generation API key (fal.ai or similar)
- self-hosted Postiz for the platform OAuth and publishing
- always-on box

## Explicit non-goals for v1

- pre-approved platform OAuth apps (TikTok/Instagram/YouTube reviews take weeks)
- prompt pipelines tuned per model, format, and tone
- an automated QA pass before anything ships
- multi-brand workspaces and a scheduling calendar
- a flat bill · DIY pays every model invoice directly, and video models are not cheap

## 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 AI video factory to replace SocialFaktory. Requirements:

- Node + Express + better-sqlite3; a localhost dashboard with a brief form
  (topic, tone, target duration) and a queue of generated videos.
- Script step: send the brief to the Anthropic API and get back a hook line,
  a spoken script, and a visual prompt; keep the system prompt in
  prompts/script.txt so I can tune it without touching code.
- Video step: submit the visual prompt to fal.ai's queue API for a
  text-to-video model with native audio, poll until done, download the mp4
  into media/ and record the actual dollar cost per generation in the DB.
- Review page: each finished video gets a player plus approve, regenerate,
  and discard buttons; nothing publishes without an explicit approve.
- Publish step: on approve, POST the video and caption to my self-hosted
  Postiz via its public API (POSTIZ_URL and POSTIZ_API_KEY in .env) with a
  scheduled time · Postiz owns the platform OAuth; never call TikTok,
  Instagram, or YouTube APIs directly.
- A node-cron tick retries failed generations 3 times with backoff and marks
  rows before submitting so nothing ever double-bills.
- A running-costs box on the dashboard summing this month's model spend.
- Out of scope: multi-brand workspaces, teams, analytics, auto-QA, and any
  direct platform integrations (say so in the README).
- README: getting the two API keys, pointing at a Postiz instance, and what
  a single video roughly costs to generate.

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

# SocialFaktory indie build

## Goal

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

## Scope

Turn a text brief into a script and a rendered short video via hosted AI model APIs, then queue and publish it through a self-hosted Postiz.

## 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:
- pre-approved platform OAuth apps (TikTok/Instagram/YouTube reviews take weeks)
- prompt pipelines tuned per model, format, and tone
- an automated QA pass before anything ships
- multi-brand workspaces and a scheduling calendar
- a flat bill · DIY pays every model invoice directly, and video models are not cheap

If those capabilities are essential, use Postiz 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 AI video factory to replace SocialFaktory. Requirements:

- Node + Express + better-sqlite3; a localhost dashboard with a brief form
  (topic, tone, target duration) and a queue of generated videos.
- Script step: send the brief to the Anthropic API and get back a hook line,
  a spoken script, and a visual prompt; keep the system prompt in
  prompts/script.txt so I can tune it without touching code.
- Video step: submit the visual prompt to fal.ai's queue API for a
  text-to-video model with native audio, poll until done, download the mp4
  into media/ and record the actual dollar cost per generation in the DB.
- Review page: each finished video gets a player plus approve, regenerate,
  and discard buttons; nothing publishes without an explicit approve.
- Publish step: on approve, POST the video and caption to my self-hosted
  Postiz via its public API (POSTIZ_URL and POSTIZ_API_KEY in .env) with a
  scheduled time · Postiz owns the platform OAuth; never call TikTok,
  Instagram, or YouTube APIs directly.
- A node-cron tick retries failed generations 3 times with backoff and marks
  rows before submitting so nothing ever double-bills.
- A running-costs box on the dashboard summing this month's model spend.
- Out of scope: multi-brand workspaces, teams, analytics, auto-QA, and any
  direct platform integrations (say so in the README).
- README: getting the two API keys, pointing at a Postiz instance, and what
  a single video roughly costs to generate.

## Required capabilities

- Anthropic or OpenAI API key for scripts
- video-generation API key (fal.ai or similar)
- self-hosted Postiz for the platform OAuth and publishing
- always-on box

## 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.

# SocialFaktory product brief

## Problem

The generation half is honest API glue: script writing is an LLM call and the video itself comes from hosted model APIs (fal.ai and friends), so a personal brief-to-video pipeline is very buildable · but you pay those per-generation model invoices yourself either way. The publishing half is the same wall every scheduler hits: TikTok, Instagram, and YouTube OAuth apps are review-gated for weeks, which is why SocialFaktory itself publishes through Postiz. The honest DIY is a generation script feeding a self-hosted Postiz, and what's left over is the tuned prompt pipelines, the QA pass, and someone else eating the platform-review pain.

## Product outcome

Turn a text brief into a script and a rendered short video via hosted AI model APIs, then queue and publish it through a self-hosted Postiz.

## Target user

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

## Required capabilities

- Anthropic or OpenAI API key for scripts
- video-generation API key (fal.ai or similar)
- self-hosted Postiz for the platform OAuth and publishing
- always-on box

## Explicit non-goals for v1

- pre-approved platform OAuth apps (TikTok/Instagram/YouTube reviews take weeks)
- prompt pipelines tuned per model, format, and tone
- an automated QA pass before anything ships
- multi-brand workspaces and a scheduling calendar
- a flat bill · DIY pays every model invoice directly, and video models are not cheap

## 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 AI video factory to replace SocialFaktory. Requirements:

- Node + Express + better-sqlite3; a localhost dashboard with a brief form
  (topic, tone, target duration) and a queue of generated videos.
- Script step: send the brief to the Anthropic API and get back a hook line,
  a spoken script, and a visual prompt; keep the system prompt in
  prompts/script.txt so I can tune it without touching code.
- Video step: submit the visual prompt to fal.ai's queue API for a
  text-to-video model with native audio, poll until done, download the mp4
  into media/ and record the actual dollar cost per generation in the DB.
- Review page: each finished video gets a player plus approve, regenerate,
  and discard buttons; nothing publishes without an explicit approve.
- Publish step: on approve, POST the video and caption to my self-hosted
  Postiz via its public API (POSTIZ_URL and POSTIZ_API_KEY in .env) with a
  scheduled time · Postiz owns the platform OAuth; never call TikTok,
  Instagram, or YouTube APIs directly.
- A node-cron tick retries failed generations 3 times with backoff and marks
  rows before submitting so nothing ever double-bills.
- A running-costs box on the dashboard summing this month's model spend.
- Out of scope: multi-brand workspaces, teams, analytics, auto-QA, and any
  direct platform integrations (say so in the README).
- README: getting the two API keys, pointing at a Postiz instance, and what
  a single video roughly costs to generate.

## 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 SocialFaktory 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