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ZenmarkAI – AI-powered bookmark organizer for all browsers

Hacker News

ZenmarkAI – AI-powered bookmark organizer for all browsers

Hey HN, I've been struggling with messy bookmarks for years, so I built ZenmarkAI ( https://www.zenmarkai.com ) to solve this problem once and for all. ZenmarkAI uses AI to automatically categorize your bookmarks into intelligent folders within minutes. No more manual sorting or cluttered bookmark bars! Key features: - Works with Chrome, Firefox, Safari, Edge, and Brave - Customizable categories and organization - Easy export to any browser - No installation required - Drag & drop interface - Dark mode support - Choose from multiple LLMs - Pay-as-you-go model (no subscriptions) I'd love to get your feedback and hear about your bookmark organization struggles. What features would make this even more useful for you?

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
51%51% 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
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
21%21% 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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