Too many browser tabs! I solved it with a simple electron app

Hacker News

There are a few sites I visit very frequently and I find myself always opening a new tab. I'm used to an IDE where if you click on a file on the left it takes you to a new editor, and if the editor already exists it just takes you to that. So I thought I'd make something similar for a web browser. I admit I used AI. One hour well spent because now I don't have the too-many-tabs problem anymore. Maybe this solution already exists? I couldn't find it. Enjoy! And if you decide to tweak it, please submit your work back in a PR! Or of course you're welcome to fork it. Please share your improvements!

Visit product View on Hacker NewsApr 21, 2026

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4points

2comments

Did not reach leaderboard

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

73%73% 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

73%73% 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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% 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

44%44% 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

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.

Incorrect prediction on native model

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