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Casabrix: a purpose-built tool to manage your apartment/home search

Hacker News

Casabrix: a purpose-built tool to manage your apartment/home search

Hi everyone! I’m Kevin and I’m a developer who is obsessed with creating things that are actually useful for people. My most recent apartment search in Chicago was awful, and I’ve spent the past couple months trying to find ways to make the process less painful ( https://chromewebstore.google.com/detail/apartmentscom-red-f... , https://chromewebstore.google.com/detail/fast-filter-chicago... , https://github.com/kloovin112358/ApartmentsThatDontSuck ). But one of the most interesting problems with apartment hunting was the lack of technology to help me keep track of the different apartment options I was considering. Previously I just copy/pasted information from Apartments.com into a spreadsheet, which resulted in me constantly flipping over to Google Maps. But the real kicker came during apartment tours, and I would totally forget the details on the unit I was there to see (and Google Sheets on mobile isn’t particularly user-friendly). Then I would forget what questions I wanted to ask, and leave the tour without complete information. That’s why I made Casabrix. A spreadsheet interface with maps integration on web, a mobile-optimized view for iOS and Android, and a Chrome extension to scrape Apartments.com or Zillow listings. I would love your feedback on what features you’d need to see in Casabrix to consider using it.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
81%81% 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: google, user, using · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, interface · Missing: plus, platform, intuitive
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, month, google · Missing: mobile apps, personal, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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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