A Chrome extension to save snippets online

Hacker News

Hi HN! A while back I made a post talking about a Chrome extension I had bootstrapped. At the time it could only be self-hosted and run locally with docker. In order to make it more accessible, I’ve taken the steps to deploy the web app and submit the extension to the chrome store! Hopefully you’ll find collecting snippets from online articles as fun as I do. I haven’t enabled email verification for now so you don’t even have to use a real email to create your account. I added a primitive networking feature so you can add your friends by their username and see what they've saved recently! It’s more or less a passion project/useless tech, and probably has a few bugs, but it exists! Please let me know if you have any thoughts or feedback. Of course I have endless ideas to expand it (friend list view, toggle the highlighting of popular snippets (or saved by friends) on websites, Youtube integration: video snippets, private snippets) but I'm helplessly pulled forwards to my next silly idea. Thanks for reading!

Visit product View on Hacker NewsMar 24, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, dock, email · Missing: mac, agents, macos

84%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.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% 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: host · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

17%17% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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