Interactive and lightweight map of NYC's Restaurant Week

Hacker News

I found the official website https://www.nyctourism.com/restaurant-week slow and tedious to navigate, so I hacked together a fast, filterable map to showcase the participating NYC restaurants during Restaurant Week. It's in pure HTML, CSS, and JS, and it uses Leaflet for mapping and a utility JS file for the searchable multi-select input. If you're interested, check out the unminified source code by right-clicking and selecting "view source". The full source is available here: https://github.com/Blue9/restaurant-week .

Visit product View on Hacker NewsJul 26, 2023

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5points

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

66%66% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

23%23% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

18%18% 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

13%13% 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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