Po
PostOwl, in-place editable blog powered by SvelteKit and SQLite
Hacker NewsPostOwl, in-place editable blog powered by SvelteKit and SQLite
Hi HN, I came up with the idea for PostOwl about ten years ago! Today I can finally share it with the world. A huge thank you to Michael Aufreiter who wrote most of the code and inspired the editing interface with his editable.webite PostOwl is open-source and there are docs for deploying your own site for free. Give it a spin and let us know what you think!
Share cardActual performance
19points
8comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos84%84% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive36%36% 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, personal31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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