a PMF accountability buddy – sends daily reminders to focus on PMF

Hacker News

Hey HN, I'm one of those founders who has always been too product-obsessed - I never give 'market fit' the attention it deserves. Recently looking back I was embarrassed to see how much time I was wasting: building the wrong thing, taking too long to pivot, rarely refining the niche… So I built a PMF accountability buddy to send me a daily reminder to focus on PMF. I hope you find this as helpful as I do. Would love your feedback pls Cam

Visit product View on Hacker NewsJan 31, 2024

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% predicted probability of success on AppSumo, 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

21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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