I made an offline-ready hiking trail companion app

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Hi HN! This is a trail companion web app (think AllTrails) I hacked together in a couple weeks time. I was inspired to create this project while training for an extended backpacking trip. My motivation was to create a UI tailored exactly to my liking, and to be able to track my progress along the trail without draining my battery. I also wanted to experiment with PWA technologies. I successfully used it on my five day adventure along the Knobstone Trail in southern Indiana, and even though it's web-based it hardly consumed any battery life on my old first-gen Pixel XL. It's set up currently to support a single trail, where the trail and trail markers are deployed with the rest of the app. So it's single-use in that way. For future trips, I can simply swap out the GPX files and deploy. I am releasing it with an open source license in case anyone wants to use it as a boilerplate to create their own.

Visit product View on Hacker NewsNov 1, 2022

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

45%45% 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 · Strong signals: way · Missing: mobile apps, ios, personal

45%45% 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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · 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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