I'm tired of BANKS telling me how much I can afford

Hacker News

https://www.homevisor.io/ As a finance professional and homeowner, I'm astonished that there aren't any better tools or ways to help people distinguish and better plan how much home they should be purchasing. Banks will always discuss the maximum they're able to lend, which can often differ greatly from what may be realistic (especially considering future expenses). To that effect, I created this website as a fun project. Open to any/all feedback, including open-sourcing and making a community-driven project. Right now it's just Squarespace + JS code. Hopefully you find it helpful :)

Visit product View on Hacker NewsAug 8, 2020

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84%84% 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: ide, io, including · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

12%12% 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

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

Incorrect prediction on native model

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