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ContentRepurpose.pro – Paste once to get LinkedIn/X/Email/IG drafts

Hacker News

ContentRepurpose.pro – Paste once to get LinkedIn/X/Email/IG drafts

We built ContentRepurpose.pro after getting tired of rewriting the same idea for LinkedIn, X, email, and IG. It’s a simple web app: paste a paragraph → get platform-specific drafts you can tweak, then export to Notion (page) or Trello (card). BYOK works via OpenRouter if you prefer your own key. Stack/notes: • Frontend: vanilla + Tailwind; auth via Supabase. • Backend: Cloudflare Worker (one file) handling CORS, auth identity, quotas, Stripe (Checkout/Portal/Webhook), OpenRouter calls, and OAuth for Notion/Trello. • Quotas: demo (IP) 5/mo; signed-in free 5/day; paid tiers raise caps. BYOK limits separate from platform pool. • Models: rotating pool by default; BYOK uses whatever model you choose on OpenRouter. We don’t store user keys (BYOK is per-request header). • Exports: Notion page with simple headings/paragraphs; Trello card title/description. (Labels/checklists and Notion DB template are likely next.) Things we’d love feedback on: • Are the LinkedIn/X outputs “ready to post” length-wise? • Presets UX (saving/renaming/recall) — any friction? • Export ergonomics for Notion/Trello — what would make it cleaner? Live demo works without login (limited). Pricing is on the site; cancel anytime. Happy to answer technical questions and share more implementation details in the comments.

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Actual performance

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Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, stripe · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
19%19% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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