I mapped every US golf course

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I got tired of Googling basic course info, so I made a free directory of every US course Google filters for golf course results are terrible so I built a better way to browse courses using OSM as the backbone.... https://golfcoursebrowser.com/ It's a work in progress and mostly US for now, but I want to expand to the rest of North America, the EU, and the rest of the world ASAP. It's free, no ads, no login, no bs. If you spot anything wrong (bad info, a missing course, wrong scorecard), you can flag it right on the course page. I actually read those and fix them. Still a lot of missing info, but I'm filling out more and more each day. The goal is the most complete, and current golf course directory in the world, verified and maintained by actual golfers. I think is attainable in the next few months. The base layer is OpenStreetMap, cross-checked against course websites and enriched with scorecards, USGA rating/slope, and public/private status. User corrections from golfers who know their home course have beaten every commercial data source we check against.

Visit product View on Hacker NewsJul 26, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% 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.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos

48%48% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

23%23% 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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