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How to vibe codeGhostfeed

AI-powered short-form video operations platform for social media growth

ghostfeed.ai

SaaS / AI Content Automation

42/ 100
Serious undertaking

The verdict: can you vibe code Ghostfeed?

Build a simplified personal video generation dashboard with local media queues, or keep paying if you need production-scale UGC automation.

Ghostfeed combines heavy asynchronous media generation pipelines, human approval checkpoints, and an MCP server that lets coding agents drive workspaces natively. While you can stitch together Next.js, Turso, and Fal.ai or Luma for the core video generation loop, building a resilient state machine that handles multi-step AI model orchestration, video frame stitching, and an MCP auth bridge requires substantial backend plumbing.

Estimated effort: 6+ weeks of full-time engineering

What you can't replicate

  • The proprietary reaction library and verified high-performing content templates
  • The managed infrastructure running real US devices and warmed accounts

Founded

Raised

Team

Small bootstrap team (~2 engineers)

Cheapest paid tier

$0/mo

What Ghostfeed does

An automated platform for generating short-form video content at scale—specializing in UGC reactions, slideshow carousels, AI avatar creation, and agentic workflows via a Model Context Protocol (MCP) server.

Core features

  • UGC reaction video cloner with split-scene frame analysis
  • Product slideshow and image carousel automatic generator
  • AI influencer face and character swap pipeline
  • Streamable HTTP MCP (Model Context Protocol) server for coding agent workspace control
  • Durable multi-step job state machine with backpressure and failure classification
  • Timeline editing canvas for fine-tuning video frames and slides
  • Multi-account content scheduling and distribution calendar

The business

Pricing

  • Free$0/mo
  • Starter$29/mo
  • Growth$49/mo
  • Agency$99/mo

Funding

Unknown / bootstrapped

Pay vs build, cumulative

Break-even at month 3 — after that, every month is money kept.

The hard parts of vibe coding Ghostfeed

  • Orchestrating multi-step async video generation pipelines (Kling, Grok, vision frames) with robust retries and idempotency
  • Implementing an MCP server with OAuth 2.1 / PKCE auth supporting dynamic agent interaction
  • Handling human-in-the-loop approval gates before expensive video animation runs
  • Managing complex media storage, video rendering concurrency, and rate-limiting

How to vibecode Ghostfeed

Prerequisites

  • Node.jsfree

    Required for running the Next.js framework and backend workers.

  • GitHubfree

    Code repository hosting and deployment pipelines.

  • Fal.ai AccountUsage-based

    API access for fast image generation and video model inference.

AI coding tools

Recommended stack

FrontendNext.js with Tailwind CSS and shadcn/ui components
BackendNext.js API routes paired with a durable background worker queue (Inngest)
DatabaseTurso (SQLite at the edge) for relational data and job states
Authbetter-auth for self-hosted TypeScript authentication
PaymentsStripe (optional for local personal clones)
OtherVercel AI SDK for LLM orchestration, fal.ai API for video/image generation, Cloudflare R2 for asset storage

Hosting & infrastructure

VercelHosting the Next.js web application and API routes$0-20/mo
Fly.ioRunning long-lived background job workers and the custom MCP server daemon$5-10/mo
CloudflareR2 Object Storage for rendered video clips and image assets$0-5/mo

Build guide

  1. 01Project Scaffolding and Database Schema

    Initialize the Next.js project with Tailwind CSS, configure better-auth with SQLite via Turso, and set up core tables for workspaces, projects, generation jobs, and asset lineages.

    Scaffold a new Next.js project configured with TypeScript, Tailwind CSS, and better-auth. Set up Turso database connectivity using libSQL. Create database migrations and models for: workspaces, projects, jobs (with state machine fields: status, input_version, model_settings, cost, error_reason), assets, and lineage (parent-child relationships). Ensure robust error logging and environment configuration are in place.
  2. 02Durable Job Queue & State Machine

    Implement a durable background worker engine using Inngest or a custom worker state machine to manage multi-step generation pipelines with retry logic and idempotency.

    Build a durable background job execution framework using Inngest or a reliable state machine. Implement explicit state transitions for video and slideshow generation: brief -> scripted -> frame_generating -> frame_review -> approved_for_animation -> animating -> motion_qc -> assembly -> final_review. Ensure every transition records job ID, asset IDs, provider request payloads, timestamps, and failure reasons to prevent double-billing or duplicate asset processing.
  3. 03AI Media Generation & Approval Gates

    Integrate third-party frontier media APIs (fal.ai / Luma) to handle image creation, frame generation, and video animation, incorporating a mandatory human-in-the-loop review gate.

    Integrate fal.ai and Luma video generation APIs into the backend pipeline. Build a modular service layer that submits generation requests, polls job statuses, handles webhooks or retries, and stores output files directly into Cloudflare R2 storage. Implement a review gate workflow where frame generation pauses for manual user approval before triggering the expensive animation step.
  4. 04Model Context Protocol (MCP) Server Integration

    Build a Streamable HTTP MCP server supporting OAuth 2.1 and PKCE to expose workspace controls and generation pipelines to external AI agents like Claude Code and Cursor.

    Implement a Streamable HTTP Model Context Protocol (MCP) server endpoint at /api/v2/mcp within the Next.js backend. Add support for OAuth 2.1 with PKCE authentication connected to user workspace grants. Expose MCP tools allowing external coding agents (such as Claude Code and Cursor) to list workspaces, create generation jobs, check job statuses, and retrieve asset URLs programmatically.
  5. 05Dashboard UI & Timeline Editing Canvas

    Create the frontend dashboard, workspace switcher, slideshow project creator, and a timeline canvas for fine-tuning video frames and text overlays.

    Build a responsive Next.js dashboard using shadcn/ui. Implement workspace management, a slideshow carousel generator view, a reaction library browser, and an interactive timeline canvas component for fine-tuning slides and video frames. Connect the UI components to backend API routes with real-time status updates reflecting job queue progression.
  6. 06Scheduling & Distribution Pipeline

    Implement content scheduling calendar views and platform integration hooks for publishing approved video assets.

    Build a calendar-based scheduling interface within the dashboard allowing users to queue approved video assets for distribution. Implement backend cron tasks or scheduled jobs that track posting times and simulate distribution workflows, preparing assets for multi-platform export with proper compliance and safety checks.

Cost vs paying for Ghostfeed

What will you build it with?

Est. 12M in / 4M out tokens· Includes access to introductory usage of the default model with dynamic rate limits.$0

Starting total with Claude Code$0 one-time

Starting costs (one-time)

  • Domain registration$12 one-time
  • Initial API credits (fal.ai / Luma)$25 one-time

Total~$37 one-time

Ongoing costs (monthly)

  • Hosting & Infrastructure (Vercel + Fly.io + Cloudflare R2)$15/mo
  • AI Model Generation Usage (fal.ai / LLM APIs)$20/mo

Total~$35/mo

Paying for Ghostfeed

$49/mo (Growth tier)

Your time to build

45-60 hours

AI tool credits

$20/mo (Claude Pro)

Break-even

Comparable to paid tiers, but built for learning and personal customization rather than cost savings.

Own Ghostfeed? Wear the score

Ghostfeed vibe-codeability badgePut this badge on your site or README — it links back to this report.

<a href="https://vibeityourself.com/app/ghostfeed"><img src="https://vibeityourself.com/badge/ghostfeed" alt="Ghostfeed vibe-codeability score" /></a>
[![Ghostfeed vibe-codeability score](https://vibeityourself.com/badge/ghostfeed)](https://vibeityourself.com/app/ghostfeed)

Vibe code Ghostfeed: FAQ

Can you vibe code Ghostfeed yourself?
Serious undertaking — 42/100 vibecodeable. Build a simplified personal video generation dashboard with local media queues, or keep paying if you need production-scale UGC automation.
How long does it take to vibe code Ghostfeed?
6+ weeks of full-time engineering — roughly 45-60 hours of hands-on time with an AI coding agent.
How do you build your own Ghostfeed?
Scoped to personal use: Next.js with Tailwind CSS and shadcn/ui components on the front, Next.js API routes paired with a durable background worker queue (Inngest) behind it, Turso (SQLite at the edge) for relational data and job states for data. Follow the 6-step build guide on this page — each step has a paste-ready prompt for an AI coding agent.
How do you code your own Ghostfeed without being an expert?
Use an AI coding tool (Claude Code or Cursor) and work in small steps: scaffold, data model, core screens, then deploy. Realistic effort: 6+ weeks of full-time engineering. The prompts on this page are written so the AI does the heavy lifting.
How much does it cost to vibe code Ghostfeed instead of paying?
About ~$37 one-time to start and ~$35/mo to run, versus $49/mo (Growth tier) for Ghostfeed. Break-even: Comparable to paid tiers, but built for learning and personal customization rather than cost savings..
What stack should you use to vibe code Ghostfeed?
Next.js with Tailwind CSS and shadcn/ui components; Next.js API routes paired with a durable background worker queue (Inngest); Turso (SQLite at the edge) for relational data and job states; plus Vercel AI SDK for LLM orchestration, fal.ai API for video/image generation, Cloudflare R2 for asset storage.

Methodology

This report was generated by VibeItYourself's standard pipeline: we scrape ghostfeed.ai (content, branding, screenshot), deep-research the company with AI + web search (pricing, funding, team, engineering complexity), then score rebuild feasibility 0–100 against the same rubric used for every app — scoped to a personal-use clone, not a competing business. How scoring works. Verdicts are honest by design: what you can't replicate is listed above.

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