Re

Rethinking browser tabs to treat too many tabs open

Hacker News

Rethinking browser tabs to treat too many tabs open

I’ve been living in dozens of tabs (struggling with too many tabs open syndrome), so I finally built Tabby. What it does - Dedupe tabs using the full URL and close duplicates in one go, keeping a sensible tab per group - Export every unique link to a .txt when you need a snapshot - Organize and group tabs by category in windows using page metadata - Preview before apply with progress What I ended up with - Cleaner windows one category per window (semantic sorting could come later) - Closed many tabs that I was hoarding and I’d even forgotten about I have been thinking to add import/export CSV and semantic organizing

Visit product View on Hacker NewsApr 25, 2026

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

86%86% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · 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

15%15% 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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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