Bike route planner that follows almost only official bike trails

Hacker News

Hey guys, I built a route planner that is mostly focused on bike touring and using existing bike infrastructure. For each request you're shown what bike tracks/trails your route uses and can further explore them by showing them on map or going to the official trail route. The main idea for the app is to have a friendly and easy to use planner that would make heavy use of official bike trails data (mainly from OpenStreetMap) and make it easy to plan a longer trip using the best possible bike routes out there. Currently the app only works for the Euro region but I'm planning to add North America very soon and then rest of the world. Technical overview: Route finding - Graphhopper sitting in a docker container on a Hetzner server somewhere in Germany. It has 38 GB of graph data(Europe) loaded into RAM for a fast graph traversal. Web App - Next.js 14 with Typescript, backend on the newest version of .NET Map tiles - right now I'm using MapTiler their free tier but planning to switch to my own home server soon and host the maps on it.

Visit product View on Hacker NewsNov 20, 2024

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

89%89% 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.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host, friendly, soon · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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