Social mediaCAN I VIBECODE
Missinglettr?
BUILD FIRST
- One specific customer type and one primary use case
- A vertical slice that completes the main job without manual workarounds
- Visible limits, review steps, and a fallback when an external dependency fails
LEAVE FOR LATER
- Broad third-party integrations
- High-scale collaboration, syncing, and permissions
- Automation that can cause harm without human review
RESEARCH NOTES
What this tool actually does.
Missinglettr is a B2B SaaS social media marketing platform centered on automated 'drip campaigns' for promoting a blog's existing content. Users connect a blog via RSS or URL; the platform detects new content, then AI generates a default sequence of 9 unique social posts (text + image + link) per article, scheduled across a year. It pulls engaging snippets, extracts in-article images, generates branded quote-bubble graphics, and suggests trending hashtags. Before anything publishes, users must review and approve the drafted campaign, editing text/images and adjusting schedule per post. Supported destinations include Facebook pages/profiles, Twitter, and LinkedIn pages/profiles. Additional features include a social media calendar, analytics, automatic reposting of best performers, free stock photo library, custom URL shortener, and a separate content-curation tool. It also syncs content f
RESEARCHED BUILD PROMPT
Give this to your coding agent.
Build a self-hosted web app inspired by Missinglettr's core workflow: convert each blog post into a scheduled campaign of 9 unique social posts spread across 12 months. Workflow: user adds a blog RSS feed or URL → app fetches articles with title, body text, and images → an LLM generates 9 distinct post variants per article (varied angle, snippet, hashtags, one image) → a review queue shows drafts with inline editing of text/image/date-time before batch approval → a job scheduler publishes approved posts at their scheduled times via platform APIs (X/Twitter, LinkedIn, Facebook Pages) → a calendar view shows upcoming/sent posts → basic per-post metrics (likes, shares, clicks) are pulled from platform APIs. Technical requirements: PostgreSQL, a job queue (e.g., BullMQ) for scheduled publishing, OAuth2 per platform, LLM API integration with an explicit prompt template and temperature control to avoid duplicate phrasing. Use React/Next.js for the front end and Node or Python backend; reuse open-source API client libraries. Do NOT copy Missinglettr's brand name, logo, color scheme, or UI layout; create an original clean design. Out of scope: billing/multi-tenancy, mobile apps, third-party content curation, team permissions, built-in stock photo DB (use placeholders or user uploads). Deliver a README with setup steps, env vars, DB migrations, OAuth instructions, scheduler/rate-limit caveats, LLM prompt template, and known limitations (per-platform character/media constraints).