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(FB, IG, Twitter, etc) how to delete accounts, search histories, etc

Hacker News

(FB, IG, Twitter, etc) how to delete accounts, search histories, etc

Hi all. I've been creating a spreadsheet ( https://docs.google.com/spreadsheets/d/14EGunLNCtPCIcDiT_l8M... ) with official links on how to delete accounts, search histories, tweets from Facebook, Instagram, Google, Whatsapp, Netflix, Twitter, etc. My motivation was because is not always an easy job to find out how to get rid of user accounts depending on the website. Feel free to include any websites you want, change the spreadsheet layout. Happy to hear your thoughts

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4points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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
61%61% 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: google, user · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
40%40% 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
31%31% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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