Tab Flow (a Chrome extension for tab management)

Hacker News

Hey all! I've been a lurker here for six months or so, but I've recently finished the initial version of a Chrome extension that some of you may find useful, especially if you're a tab hoarder like me. Tab Flow lets you make sense of your mess of tabs by searching, batch-bookmarking, batch-moving, and batch-closing them. Don't know where your HN homepage tab is? Open the extension, search for it, and click the link to be taken directly to the tab. Want to move all your reddit tabs to a new window? Search for them, and click the "Move" link. I really only wrote this for myself, but if it can make someone else's life easier, why not share it? Let me know if there are any changes you'd recommend, or any features you think are missing. Constructive criticism is always welcome. As is customary (or should be) please direct trolling comments to /dev/null.

Visit product View on Hacker NewsMay 16, 2013

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

89%89% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, 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

30%30% 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 · Missing: arr, mrr, revenue

20%20% 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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