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Divisualise! An interactive divide-and-conquer algorithm visualiser
Hacker NewsDivisualise! An interactive divide-and-conquer algorithm visualiser
Hello all! I created Divisualise for my undergraduate project. If you play with it, I would be very appreciative if you complete the user survey which can be found here: https://forms.gle/bthpuGJ6CdeES7q9A
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting80%80% 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 · Missing: mobile apps, ios, personal55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released48%48% 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: user, visual · Missing: mac, agents, macos44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue13%13% 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, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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