Vibecode Wireflow

You are building a lean indie version of Wireflow.

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 =====
# Wireflow indie build

## Goal

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

## Scope

Wire model calls into a graph on a canvas, hit run, then layer and time the generated pieces into a finished cut.

## 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:
- a compositor and keyframe timeline, with hosted rendering
- live multiplayer on the canvas and inside the editor
- hundreds of model schemas kept current for you
- paid steps that never fire by accident, and jobs that survive a closed tab
- a library of ready-made pipelines to fork, and boards you can share

If those capabilities are essential, use ComfyUI 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 local visual pipeline runner for AI media generation. Requirements:

- One Vite + React app and one Express server in the same repo, SQLite via
  better-sqlite3, no auth, no accounts, runs on localhost.
- Canvas on React Flow: drag nodes from a palette, wire outputs to inputs,
  pan and zoom, autosave the graph to SQLite on every change.
- Six node types only: Text input, Image import (upload to ./storage), Prompt
  template (interpolates {{upstream}} values), Image generate, Video generate,
  Preview. Generate nodes call fal.ai through @fal-ai/client, FAL_KEY in .env.
- Model config lives in models.json: per model, its fal path and which fields
  are wired ports versus typed-in settings. Ship exactly two entries,
  fal-ai/flux/dev and fal-ai/kling-video/v1/standard/text-to-video.
- Run = topological sort, execute layer by layer, store each node's output in
  SQLite keyed by node id plus a hash of its resolved inputs. Re-running only
  re-executes nodes whose input hash changed.
- Generate nodes NEVER run on edit. Only the Run button or a per-node run
  button submits a job. This rule is the whole point, do not add reactive
  auto-execution anywhere.
- Submit through fal's queue API and poll by request id, persisting the request
  id BEFORE the first poll, so a finished job survives a server restart.
- README covers where FAL_KEY goes, that every run spends real money at
  fal.ai, and how to add a third model to models.json.

Excluded on purpose: multiplayer, credits and billing, iterators or fan-out
over arrays, video assembly, hosted deploys, and any provider besides fal.ai.

## Required capabilities

- fal.ai API key or another hosted model provider
- Node 20
- somewhere to run it that stays up while jobs finish
- a GPU only if you self-host the models instead

## 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 Wireflow.

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 =====
# Wireflow indie build

## Goal

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

## Scope

Wire model calls into a graph on a canvas, hit run, then layer and time the generated pieces into a finished cut.

## 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:
- a compositor and keyframe timeline, with hosted rendering
- live multiplayer on the canvas and inside the editor
- hundreds of model schemas kept current for you
- paid steps that never fire by accident, and jobs that survive a closed tab
- a library of ready-made pipelines to fork, and boards you can share

If those capabilities are essential, use ComfyUI 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 local visual pipeline runner for AI media generation. Requirements:

- One Vite + React app and one Express server in the same repo, SQLite via
  better-sqlite3, no auth, no accounts, runs on localhost.
- Canvas on React Flow: drag nodes from a palette, wire outputs to inputs,
  pan and zoom, autosave the graph to SQLite on every change.
- Six node types only: Text input, Image import (upload to ./storage), Prompt
  template (interpolates {{upstream}} values), Image generate, Video generate,
  Preview. Generate nodes call fal.ai through @fal-ai/client, FAL_KEY in .env.
- Model config lives in models.json: per model, its fal path and which fields
  are wired ports versus typed-in settings. Ship exactly two entries,
  fal-ai/flux/dev and fal-ai/kling-video/v1/standard/text-to-video.
- Run = topological sort, execute layer by layer, store each node's output in
  SQLite keyed by node id plus a hash of its resolved inputs. Re-running only
  re-executes nodes whose input hash changed.
- Generate nodes NEVER run on edit. Only the Run button or a per-node run
  button submits a job. This rule is the whole point, do not add reactive
  auto-execution anywhere.
- Submit through fal's queue API and poll by request id, persisting the request
  id BEFORE the first poll, so a finished job survives a server restart.
- README covers where FAL_KEY goes, that every run spends real money at
  fal.ai, and how to add a third model to models.json.

Excluded on purpose: multiplayer, credits and billing, iterators or fan-out
over arrays, video assembly, hosted deploys, and any provider besides fal.ai.

## Required capabilities

- fal.ai API key or another hosted model provider
- Node 20
- somewhere to run it that stays up while jobs finish
- a GPU only if you self-host the models instead

## 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 Wireflow.

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 =====
# Wireflow product brief

## Problem

Chaining a few model calls is a script, and ComfyUI already gives you a free node canvas. What you will not get in a weekend is the part after generation: a track and keyframe timeline with hosted rendering, and live multiplayer inside it. The graph is vibecodable. The editor attached to it is not.

## Product outcome

Wire model calls into a graph on a canvas, hit run, then layer and time the generated pieces into a finished cut.

## Target user

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

## Required capabilities

- fal.ai API key or another hosted model provider
- Node 20
- somewhere to run it that stays up while jobs finish
- a GPU only if you self-host the models instead

## Explicit non-goals for v1

- a compositor and keyframe timeline, with hosted rendering
- live multiplayer on the canvas and inside the editor
- hundreds of model schemas kept current for you
- paid steps that never fire by accident, and jobs that survive a closed tab
- a library of ready-made pipelines to fork, and boards you can share

## 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 local visual pipeline runner for AI media generation. Requirements:

- One Vite + React app and one Express server in the same repo, SQLite via
  better-sqlite3, no auth, no accounts, runs on localhost.
- Canvas on React Flow: drag nodes from a palette, wire outputs to inputs,
  pan and zoom, autosave the graph to SQLite on every change.
- Six node types only: Text input, Image import (upload to ./storage), Prompt
  template (interpolates {{upstream}} values), Image generate, Video generate,
  Preview. Generate nodes call fal.ai through @fal-ai/client, FAL_KEY in .env.
- Model config lives in models.json: per model, its fal path and which fields
  are wired ports versus typed-in settings. Ship exactly two entries,
  fal-ai/flux/dev and fal-ai/kling-video/v1/standard/text-to-video.
- Run = topological sort, execute layer by layer, store each node's output in
  SQLite keyed by node id plus a hash of its resolved inputs. Re-running only
  re-executes nodes whose input hash changed.
- Generate nodes NEVER run on edit. Only the Run button or a per-node run
  button submits a job. This rule is the whole point, do not add reactive
  auto-execution anywhere.
- Submit through fal's queue API and poll by request id, persisting the request
  id BEFORE the first poll, so a finished job survives a server restart.
- README covers where FAL_KEY goes, that every run spends real money at
  fal.ai, and how to add a third model to models.json.

Excluded on purpose: multiplayer, credits and billing, iterators or fan-out
over arrays, video assembly, hosted deploys, and any provider besides fal.ai.

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

# Wireflow indie build

## Goal

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

## Scope

Wire model calls into a graph on a canvas, hit run, then layer and time the generated pieces into a finished cut.

## 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:
- a compositor and keyframe timeline, with hosted rendering
- live multiplayer on the canvas and inside the editor
- hundreds of model schemas kept current for you
- paid steps that never fire by accident, and jobs that survive a closed tab
- a library of ready-made pipelines to fork, and boards you can share

If those capabilities are essential, use ComfyUI 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 local visual pipeline runner for AI media generation. Requirements:

- One Vite + React app and one Express server in the same repo, SQLite via
  better-sqlite3, no auth, no accounts, runs on localhost.
- Canvas on React Flow: drag nodes from a palette, wire outputs to inputs,
  pan and zoom, autosave the graph to SQLite on every change.
- Six node types only: Text input, Image import (upload to ./storage), Prompt
  template (interpolates {{upstream}} values), Image generate, Video generate,
  Preview. Generate nodes call fal.ai through @fal-ai/client, FAL_KEY in .env.
- Model config lives in models.json: per model, its fal path and which fields
  are wired ports versus typed-in settings. Ship exactly two entries,
  fal-ai/flux/dev and fal-ai/kling-video/v1/standard/text-to-video.
- Run = topological sort, execute layer by layer, store each node's output in
  SQLite keyed by node id plus a hash of its resolved inputs. Re-running only
  re-executes nodes whose input hash changed.
- Generate nodes NEVER run on edit. Only the Run button or a per-node run
  button submits a job. This rule is the whole point, do not add reactive
  auto-execution anywhere.
- Submit through fal's queue API and poll by request id, persisting the request
  id BEFORE the first poll, so a finished job survives a server restart.
- README covers where FAL_KEY goes, that every run spends real money at
  fal.ai, and how to add a third model to models.json.

Excluded on purpose: multiplayer, credits and billing, iterators or fan-out
over arrays, video assembly, hosted deploys, and any provider besides fal.ai.

## Required capabilities

- fal.ai API key or another hosted model provider
- Node 20
- somewhere to run it that stays up while jobs finish
- a GPU only if you self-host the models instead

## 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.

# Wireflow product brief

## Problem

Chaining a few model calls is a script, and ComfyUI already gives you a free node canvas. What you will not get in a weekend is the part after generation: a track and keyframe timeline with hosted rendering, and live multiplayer inside it. The graph is vibecodable. The editor attached to it is not.

## Product outcome

Wire model calls into a graph on a canvas, hit run, then layer and time the generated pieces into a finished cut.

## Target user

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

## Required capabilities

- fal.ai API key or another hosted model provider
- Node 20
- somewhere to run it that stays up while jobs finish
- a GPU only if you self-host the models instead

## Explicit non-goals for v1

- a compositor and keyframe timeline, with hosted rendering
- live multiplayer on the canvas and inside the editor
- hundreds of model schemas kept current for you
- paid steps that never fire by accident, and jobs that survive a closed tab
- a library of ready-made pipelines to fork, and boards you can share

## 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 local visual pipeline runner for AI media generation. Requirements:

- One Vite + React app and one Express server in the same repo, SQLite via
  better-sqlite3, no auth, no accounts, runs on localhost.
- Canvas on React Flow: drag nodes from a palette, wire outputs to inputs,
  pan and zoom, autosave the graph to SQLite on every change.
- Six node types only: Text input, Image import (upload to ./storage), Prompt
  template (interpolates {{upstream}} values), Image generate, Video generate,
  Preview. Generate nodes call fal.ai through @fal-ai/client, FAL_KEY in .env.
- Model config lives in models.json: per model, its fal path and which fields
  are wired ports versus typed-in settings. Ship exactly two entries,
  fal-ai/flux/dev and fal-ai/kling-video/v1/standard/text-to-video.
- Run = topological sort, execute layer by layer, store each node's output in
  SQLite keyed by node id plus a hash of its resolved inputs. Re-running only
  re-executes nodes whose input hash changed.
- Generate nodes NEVER run on edit. Only the Run button or a per-node run
  button submits a job. This rule is the whole point, do not add reactive
  auto-execution anywhere.
- Submit through fal's queue API and poll by request id, persisting the request
  id BEFORE the first poll, so a finished job survives a server restart.
- README covers where FAL_KEY goes, that every run spends real money at
  fal.ai, and how to add a third model to models.json.

Excluded on purpose: multiplayer, credits and billing, iterators or fan-out
over arrays, video assembly, hosted deploys, and any provider besides fal.ai.

## 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 Wireflow 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