# Build brief — a focused alternative to Runway

> **Verdict:** Not faithfully · **Buildability:** 21/100 · **Category:** AI Video
> **Source:** https://www.canitbevibecoded.com/runway
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Runway. Verify current pricing and capabilities before acting.

## Context

**Runway** — Generative video and creative AI tools for images, video, and editing. It currently costs $15/mo.

A UI around video models is buildable; the video-generation model quality, compute, safety, editing stack, and continuous research are not a solo project.

This brief describes a focused, single-operator replacement for the part of Runway that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Wrap open-source/video APIs to generate or edit clips, manage credits, and export assets.

- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- Storage.
- Video rendering.
- Safety filters.
- Prompt/workflow UI.

### Data and integrations

- GPU/hosted video API.

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build me a thin video-generation workbench over hosted models, in place of
Runway. Requirements:

- A local web page on localhost:3300: Node + Express, a prompt box, a model
  picker, and a gallery of past generations.
- Generate via the Replicate API (token in .env) against current open video
  models (Wan/LTX/Hunyuan class). Pick one provider and stick to it; fal.ai
  only if it has a model Replicate lacks.
- Poll the job, download the mp4 to ~/generations/YYYY-MM-DD/, and record
  prompt, model, params, cost, and file path in SQLite (better-sqlite3).
- Image-to-video: upload a still and pass it as the conditioning frame.
- Show a month-to-date spend total computed from per-job cost so usage stays
  honest.
- No accounts, no telemetry; prompts and outputs stay local except the API
  calls themselves.
- Out of scope: editing timelines, inpainting, and anything realtime. Do not
  build model hosting or a job queue beyond simple polling.
- README: where to get the Replicate token, rough dollars per second of video
  on the default model, and a note that this rents models rather than replacing
  Runway, whose frontier video models and compute cannot be rebuilt solo.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- Frontier video models.
- Compute.
- Asset history.
- Model updates.
- Model quality and inference operations are part of the product.
- Reliability at the vendor's scale is an operations problem, not a prompt.

## What you still own after launch

- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [diffusers](https://github.com/huggingface/diffusers) — Open-source library for diffusion models; useful for experimentation, not a Runway replace

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Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/runway