I made a script to automate removing your Twitter/X followers

Hacker News

I wanted a way to get my old Twitter account down to zero following and zero followers. I removed all the people I follow a while back, but couldn't find a quick, free and easy way to remove the people *following* *me*. I didn't want to pay for API access just for this. So I wrote a Python "bot" that uses Selenium to act like a human, clicking the "remove follower" button. I was (mildly) surprised how easy it is to make something that can fake being a human on Twitter. It has delays to reduce the likelihood it will trigger anti-bot detection on their end. I left it running overnight on a spare laptop, and my follower count went from 16K to 8K. So in theory, it'll hit zero tomorrow if I leave it running. No doubt it might break at some point, but it works for now. YMMV

Visit product View on Hacker NewsJan 23, 2025

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2points

Did not reach leaderboard

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

83%83% 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: io · Missing: https docs, excited, just released

76%76% 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 · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

26%26% 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

25%25% 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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