Vibecode ThumblifyAI

You are building a lean indie version of ThumblifyAI.

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 =====
# ThumblifyAI indie build

## Goal

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

## Scope

Generate YouTube thumbnail concepts and images using AI models with custom prompts and creator-focused workflows.

## 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:
- specialized thumbnail-specific system prompts
- style matching and recreation features
- custom AI training and personalization features
- creator-focused workflow and UI/UX
- thumbnail inspiration system

If those capabilities are essential, use Adobe Express Free 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 YouTube thumbnail generator inspired by ThumblifyAI. Requirements:

- Node + Express, one localhost page; no accounts, no telemetry, keys in .env.
- Input: video title, a face/subject photo upload, and a style picker with 5
  presets (bold MrBeast-style, minimal tech, tutorial, vlog, gaming).
- Generation: send the title + style prompt to an image model API (key in
  .env; support OpenAI Images or Replicate, one flag to choose). Composite
  the uploaded face onto the generated background with sharp: auto-cutout
  via an rembg CLI call, drop shadow, rim light.
- Text layer: the title (or a punchier 3-5 word hook the LLM suggests)
  rendered with sharp over the composite, thick outline, 2 font choices.
- Output 1280x720 PNG under 2MB, plus a 3-variant grid so I can pick; save
  every result to thumbnails/ with the prompt used, so styles are repeatable.
- A history page of past generations with one-click re-run.
- Out of scope: accounts, teams, A/B testing against YouTube analytics, and
  bulk generation. This is one thumbnail at a time for my own channel.
- README: which API keys I need, rembg install, and rough per-image cost.

## Required capabilities

- Image generation API key
- LLM 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 ThumblifyAI.

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 =====
# ThumblifyAI indie build

## Goal

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

## Scope

Generate YouTube thumbnail concepts and images using AI models with custom prompts and creator-focused workflows.

## 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:
- specialized thumbnail-specific system prompts
- style matching and recreation features
- custom AI training and personalization features
- creator-focused workflow and UI/UX
- thumbnail inspiration system

If those capabilities are essential, use Adobe Express Free 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 YouTube thumbnail generator inspired by ThumblifyAI. Requirements:

- Node + Express, one localhost page; no accounts, no telemetry, keys in .env.
- Input: video title, a face/subject photo upload, and a style picker with 5
  presets (bold MrBeast-style, minimal tech, tutorial, vlog, gaming).
- Generation: send the title + style prompt to an image model API (key in
  .env; support OpenAI Images or Replicate, one flag to choose). Composite
  the uploaded face onto the generated background with sharp: auto-cutout
  via an rembg CLI call, drop shadow, rim light.
- Text layer: the title (or a punchier 3-5 word hook the LLM suggests)
  rendered with sharp over the composite, thick outline, 2 font choices.
- Output 1280x720 PNG under 2MB, plus a 3-variant grid so I can pick; save
  every result to thumbnails/ with the prompt used, so styles are repeatable.
- A history page of past generations with one-click re-run.
- Out of scope: accounts, teams, A/B testing against YouTube analytics, and
  bulk generation. This is one thumbnail at a time for my own channel.
- README: which API keys I need, rembg install, and rough per-image cost.

## Required capabilities

- Image generation API key
- LLM 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 ThumblifyAI.

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 =====
# ThumblifyAI product brief

## Problem

A basic AI thumbnail generator can be built quickly using existing image models, but recreating ThumblifyAI requires much more than connecting an image API. The product's advantage comes from specialized thumbnail-focused system prompts, AI workflows, creator-focused features, thumbnail inspiration, style matching, AI customization features, and continuous iteration around generating clickable YouTube thumbnails.

## Product outcome

Generate YouTube thumbnail concepts and images using AI models with custom prompts and creator-focused workflows.

## Target user

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

## Required capabilities

- Image generation API key
- LLM API key

## Explicit non-goals for v1

- specialized thumbnail-specific system prompts
- style matching and recreation features
- custom AI training and personalization features
- creator-focused workflow and UI/UX
- thumbnail inspiration system

## 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 YouTube thumbnail generator inspired by ThumblifyAI. Requirements:

- Node + Express, one localhost page; no accounts, no telemetry, keys in .env.
- Input: video title, a face/subject photo upload, and a style picker with 5
  presets (bold MrBeast-style, minimal tech, tutorial, vlog, gaming).
- Generation: send the title + style prompt to an image model API (key in
  .env; support OpenAI Images or Replicate, one flag to choose). Composite
  the uploaded face onto the generated background with sharp: auto-cutout
  via an rembg CLI call, drop shadow, rim light.
- Text layer: the title (or a punchier 3-5 word hook the LLM suggests)
  rendered with sharp over the composite, thick outline, 2 font choices.
- Output 1280x720 PNG under 2MB, plus a 3-variant grid so I can pick; save
  every result to thumbnails/ with the prompt used, so styles are repeatable.
- A history page of past generations with one-click re-run.
- Out of scope: accounts, teams, A/B testing against YouTube analytics, and
  bulk generation. This is one thumbnail at a time for my own channel.
- README: which API keys I need, rembg install, and rough per-image cost.

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

# ThumblifyAI indie build

## Goal

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

## Scope

Generate YouTube thumbnail concepts and images using AI models with custom prompts and creator-focused workflows.

## 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:
- specialized thumbnail-specific system prompts
- style matching and recreation features
- custom AI training and personalization features
- creator-focused workflow and UI/UX
- thumbnail inspiration system

If those capabilities are essential, use Adobe Express Free 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 YouTube thumbnail generator inspired by ThumblifyAI. Requirements:

- Node + Express, one localhost page; no accounts, no telemetry, keys in .env.
- Input: video title, a face/subject photo upload, and a style picker with 5
  presets (bold MrBeast-style, minimal tech, tutorial, vlog, gaming).
- Generation: send the title + style prompt to an image model API (key in
  .env; support OpenAI Images or Replicate, one flag to choose). Composite
  the uploaded face onto the generated background with sharp: auto-cutout
  via an rembg CLI call, drop shadow, rim light.
- Text layer: the title (or a punchier 3-5 word hook the LLM suggests)
  rendered with sharp over the composite, thick outline, 2 font choices.
- Output 1280x720 PNG under 2MB, plus a 3-variant grid so I can pick; save
  every result to thumbnails/ with the prompt used, so styles are repeatable.
- A history page of past generations with one-click re-run.
- Out of scope: accounts, teams, A/B testing against YouTube analytics, and
  bulk generation. This is one thumbnail at a time for my own channel.
- README: which API keys I need, rembg install, and rough per-image cost.

## Required capabilities

- Image generation API key
- LLM 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.

# ThumblifyAI product brief

## Problem

A basic AI thumbnail generator can be built quickly using existing image models, but recreating ThumblifyAI requires much more than connecting an image API. The product's advantage comes from specialized thumbnail-focused system prompts, AI workflows, creator-focused features, thumbnail inspiration, style matching, AI customization features, and continuous iteration around generating clickable YouTube thumbnails.

## Product outcome

Generate YouTube thumbnail concepts and images using AI models with custom prompts and creator-focused workflows.

## Target user

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

## Required capabilities

- Image generation API key
- LLM API key

## Explicit non-goals for v1

- specialized thumbnail-specific system prompts
- style matching and recreation features
- custom AI training and personalization features
- creator-focused workflow and UI/UX
- thumbnail inspiration system

## 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 YouTube thumbnail generator inspired by ThumblifyAI. Requirements:

- Node + Express, one localhost page; no accounts, no telemetry, keys in .env.
- Input: video title, a face/subject photo upload, and a style picker with 5
  presets (bold MrBeast-style, minimal tech, tutorial, vlog, gaming).
- Generation: send the title + style prompt to an image model API (key in
  .env; support OpenAI Images or Replicate, one flag to choose). Composite
  the uploaded face onto the generated background with sharp: auto-cutout
  via an rembg CLI call, drop shadow, rim light.
- Text layer: the title (or a punchier 3-5 word hook the LLM suggests)
  rendered with sharp over the composite, thick outline, 2 font choices.
- Output 1280x720 PNG under 2MB, plus a 3-variant grid so I can pick; save
  every result to thumbnails/ with the prompt used, so styles are repeatable.
- A history page of past generations with one-click re-run.
- Out of scope: accounts, teams, A/B testing against YouTube analytics, and
  bulk generation. This is one thumbnail at a time for my own channel.
- README: which API keys I need, rembg install, and rough per-image cost.

## 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 ThumblifyAI 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