Hopscotch – Find your ideal NYC neighborhood

Hacker News

If you’re moving to NYC (or thinking about it), Hopscotch helps you discover suitable neighborhoods that best align with your needs or wants. We built Hopscotch to reduce the hassle of mining through various sources to find your optimal living location. Some features: - Hopscotch incorporates many filters including proximity to specific locations, budget, safety, nearby stores and gyms, subway lines and citi bikes, and more. - Hopscotch allows you to customize your priorities and receive personalized recommendations for top neighborhoods and communities. - You can instantly see your priorities reflected on an interactive map and visualize the best neighborhood locations. While Hopscotch currently serves NYC, we plan to expand to other cities in the future. Give it a try and let us know what you think!

Visit product View on Hacker NewsSep 12, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: personal, visualize, way · Missing: mobile apps, ios, entrepreneurs

52%52% predicted probability of success on TrustMRR, 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.

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

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

14%14% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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