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LinkSnatch – Dead simple bookmarks on the go

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LinkSnatch – Dead simple bookmarks on the go

Hi HN, I have been meaning to have something simple using which I can dump links I come across for a long time. Something pretty straight-forward that just works. So, I built LinkSnatch in a week and launched an MVP. Here are all the features it currently have: - A beautiful interface with minimal distractions. - Extracts URL metadata using jsonlink.io and saves it to the browser's local storage. - Save and search links all from a single place. - Dark mode. - Doesn't track you. - No signup needed. It's also an open-source project: https://github.com/amitmerchant1990/linksnatch

Visit productView on Hacker NewsFeb 20, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, open · Missing: mac, agents, macos
80%80% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
50%50% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
46%46% 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
45%45% 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
12%12% 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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