I made a free site to discover your Instagram Unfollowers

Hacker News

After 1 month of coding (probably more), and beyond my 8-6 work hours I developed this website to discover who doesn't follow you back on Instagram and in a very secure way. Finally, I did it, because the website did not need logins or registrations. You can discover your Instagram Unfollowers and Instagram Fans in just 3 easy steps: 1-Request your ZIP file from Instagram with your "followers and following" data 2-Upload to Unfollowers Tracker 3-Discover who doesn't follow you back on Instagram It is the most secure tool because you don't need to log in with Instagram and even you don't need to register on the website. Is free.

Visit product View on Hacker NewsJan 18, 2024

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2points

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Did not reach leaderboard

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

79%79% 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.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

52%52% 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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: coding · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, 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

43%43% 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

14%14% 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.

Correct prediction on native model

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