A minimal notepad – without the cloud or 3rd party libs

Hacker News

I made this tiny notepad website, to write things down in your browser. The Features - Just saves to your browser's localstorage (no cloud or login) - Export to TXT file - Formatting keyboard shortcuts - Works offline - Super fast The Tech - Single HTML file - 20 lines of JS + service worker (for offline usage) - Using pure CSS for modals - Github pages hosting - Basically it's just a "contenteditable div" with some extra features - Source -> https://github.com/benwinding/localnotes.page It's been done before, but I couldn't find a version I liked, that worked offline too. Which is why I just hacked this little thing together. Plus it's fun to make things. This taught me about offline websites, css hacks to make dialogs, inline favicons, and hacking together a prototype without 500mb of "node_modules". Anyway, hope someone finds this useful too!

Visit product View on Hacker NewsNov 29, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: single, tiny, using · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% 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

16%16% 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, 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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