AllPub – Smart Cross-platform publishing tool

Hacker News

Hi HN, I'm a solo developer who built AllPub ( https://allpub.co ) to solve my own problem: spending 6+ hours/week manually cross-posting content. What it does: Connects to your Notion/Dev.to/Hashnode workspace and cross-publishes your content with one click. Built with: Next.js,Tailwind, Supabase, Clerk, Mailerlite. Used GitHub Copilot heavily for API integrations and boilerplate. Hardest technical challenge: Coordinating rate limits and OAuth refresh cycles across 3+ platforms simultaneously. Each platform handles authentication differently, so graceful degradation for partial failures was complex. Currently awaiting for more feedback... This is my first real SaaS product. Happy to answer questions about the architecture or the build process. Try it: https://allpub.co

Visit product View on Hacker NewsOct 29, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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