I built a Chrome extension to close my 100+ tabs and get stuff done

Hacker News

I built a simple Chrome extension to finally deal with my tab anxiety. I used to have 100s of tabs open — half were tasks, the rest were links I didn’t want to lose. Now, if something’s quick (like replying to an email), I just do it and close the tab.If it’s a task that’ll take time, I add a one-line note with a link (if needed) to the To-do tab.If it’s not a task but something I want to read or save, I add it to the Links tab — and close the tab. This small workflow has been a game-changer. I finally feel okay closing tabs and can focus better.The to-do list forces me to pick just one thing to work on.When I mark a task done, I can even add a link — super useful for things like code reviews or docs I’ve created. This project was entirely created using AI and inspired by the updates page at https://blot.im/news . Would love for you to check it out and let me know what you think!

Visit product View on Hacker NewsApr 23, 2025

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

92%92% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: code review, io · Missing: https docs, excited, just released

39%39% 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

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

18%18% 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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