AI to help you online date better

Hacker News

SmallTalks was born from my personal experience dating in NYC, where I struggled with the apps for quite a while before eventually figuring things out. Initially, this app is focused on helping people build high performing Hinge and Bumble profiles by combining AI and human expertise, along with empirical data from content experiments. Most online daters are not strategic about designing their Hinge or Bumble profiles but it's actually immensely important to be intentional about that process. Online dating is a power law distribution as far as matching goes (a small percentage of profiles attract an outsized amount of attention), so it's critical that you optimize your profile to fit your relationship goals. If you sign up and go through the quick onboarding process, we'll review your existing dating profile for free using the same kind of general thought process as displayed here: https://www.loom.com/share/4619b64b935d4357818a307d5c176b20?... ).

Visit product View on Hacker NewsAug 21, 2023

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

68%68% 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: exist, existing, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% 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

19%19% 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.

Correct prediction on native model

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