We

We made a Buy Button for real estate

Hacker News

We made a Buy Button for real estate

Hi HN! At Open Listings, we build technology to make buying a house simpler and more affordable. Ultimately, we want to make buying a house as simple as clicking a button. Our core technology is really our transaction process. While we also provide some shopping tools for house hunters, we recognize that people don’t just use 1 site when they’re searching for a home. In the spirit of supporting our clients even if they’er shopping on other sites, we made a “buy button” that we hope will work anywhere. When you click, it looks for address and MLS# on your current page and fills them in on our offer form. It’s the fastest way to make an offer on a piece of real estate. Also it’s open source, so if you find a real estate site that this doesn’t work on, feel free to send us a pull request: https://github.com/openlistings/open_listings_chrome Fun trivia: pg suggested we call it “Buyjacking” but we thought that sounded too much like we were stealing someone else's business. We’re just trying to build tools to make the buying process less painful and thought this was a fun idea in that spirit.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
39%39% 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
18%18% 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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