Practice for Your YC Interviews with Betafi

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Hi folks, Betafi is a product feedback platform build around moderated user interviews and usability testing sessions. To help folks applying for the W23 batch with interviews coming up this week, we just launched a project templates feature and catered onboarding for fellow founders to practice and conduct mock YC interviews with each other. You can use Betafi's interview script feature, "instant tags," and timestamped notes to take turns annotating rough spots and practicing rapid-fire responses to questions with your cofounder(s), to try and make the most out of those 10 minutes during your real YC interview. A big part of the joy of building Betafi is getting to support other early-stage founders who wind up using our product in interesting and creative ways. This project came out of several teams applying to the W23 batch, who organically started using Betafi to help prepare for their interviews, so we thought we might as well build "first class" support for it! Do let us know what you think, keen to hear your feedback, especially given this is a slightly different use-case from what we initially designed the product for!

Visit product View on Hacker NewsNov 10, 2022

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

89%89% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, notes · Missing: mac, agents, macos

81%81% 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

44%44% predicted probability of success on TrustMRR, 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

38%38% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

30%30% 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

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.

Incorrect prediction on native model

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