My first Chrome extension (TabZolo.co)

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I had an idea to build a Chrome extension to help stay on track with browsing the internet in today's multi-tab-able browsers. I wanted a solution that would encourage (ie, force) me to finish my business on a single given tab before going on to doing something else in another. TabZolo is the result of my dabbling with a buddy. Using the chrome.* APIs, when enabled, the extension forces you to use one tab and window (except incognito windows - but I probably shouldn't have told you that) and kills any new tabs/windows that get created instead replacing the current tab with the new URL. You can check it out at www.tabzolo.co. It's completely open-sourced on GitHub too if anyone wants to extend its functionality. What do you think of it? For more on TabZolo: GitHub - https://github.com/dasmall/TabZolo My blog post - http://dazm.co/weekend-project-tabzolo-co/ My co-creator's post - http://leonardocorrea.com/posts/2013-07-14-tabzolo-to-the-rescue/ LifeHacker Mention - http://lifehacker.com/tabzolo-keeps-you-focused-on-one-tab-at-a-time-845544805

Visit product View on Hacker NewsJul 21, 2013

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

79%79% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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.

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.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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