Snipets – A browser extension to remember what I read online

Hacker News

Hi HN! I frequently struggled with reading online articles or content, because however impressed or interested I was by what I read, I wished I could stow away the strong points to read again another day. So I booted up a little chrome extension projet, that read whatever text was currently highlighted by the mouse, sent it to a local API, and saved it to a database. After some debugging, I had my first snippets. Almost immediately, reading articles became a treasure hunt, a collection game, storing nuggets of wisdom I could come back to later. I also wrote a little web interface in Vue to explore and search my snippets, they all have a backlink to the original article, and can be grouped by the title of the page they were found on. The API is a python fastapi project, and the database is RavenDB. I've been having some fun working on it (and actually using it! Which is a first for my side projects). If I had to start over, I would use postgres instead of ravendb though. Thanks for reading!

Visit product View on Hacker NewsNov 22, 2025

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

79%79% 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

61%61% predicted probability of success on Indie Hackers, 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

41%41% 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, intuitive

39%39% 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

37%37% 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

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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