Quick "Unfollow Everybody" script for Twitter

Hacker News

So I just did my semi-annual "log in to Twitter to see if it's any good yet", and found that in my absence I had followed a few thousand new spam accounts. Sweet. That explains why they emailed me and forced me to change my password last year. Being Awesome, Twitter doesn't have any way to simply toss out all your followers and start from scratch. And none of the spammy apps claiming to do so actually will (even seemingly after charging you money to try). So instead, I just pulled open the Dev console and did this: setInterval(function(){$(".following").children("button").click();window.scrollBy(0,5000);}, 1000); Run it while on https://twitter.com/following, and it'll clean your account out nicely at a rate of about 1000 accts/minute. Figured somebody else might find that useful. Enjoy!

Visit product View on Hacker NewsJan 7, 2014

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, 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: 000, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, new, email · Missing: mac, agents, macos

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, 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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