Listable – A website to organize your apartment search

Hacker News

Hey HN, I’ve been working with my friend and roommate Jacob Strieb ( https://github.com/jstrieb/ ) on a website to make apartment searching less chaotic. When we were looking for an apartment in New York City, we made a Google Sheets spreadsheet and entered all the listings we were considering as rows. Jacob wrote some fancy formulas using IMPORTXML that created columns by pulling useful bits of information from the listing page such as square footage, number of beds/baths, days on market, and even the commute times to our places of work! Listable turns that spreadsheet into an application anyone can use. It works by scanning the pages of any URLs you paste in to extract attributes about your listing, and organizes everything into a table. (Currently only a limited number of listing websites are supported.) Listable can calculate the commute from each listing to nearby businesses or custom waypoints. You can add fields for things like “commute time to the nearest Trader Joe’s” and it will update for each listing you are looking at. There are many things we’d love to add, but thought it would be good to get some initial feedback on our prototype. Feel free to make your own list from the homepage (no account required!), or check out our demo list ( https://getlistable.app/d/f795c5d4-a523-4c14-904f-e07d668e52... ). Thank you for taking a look!

Visit product View on Hacker NewsJan 25, 2025

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

79%79% 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 HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% 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

37%37% 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

33%33% 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

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

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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