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I made a route planner app (update from 2018)

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I made a route planner app (update from 2018)

Hello HN, I made the web app that helps plan your routes/road trips. It has so many options that I don’t know how to explain in simple words :), so I’ll try like this. What is cool about it is that your routes are shareable and hackable. The routes come in the format ie. https://camptarget.com/London,Uk/Vienna,AT/Istanbul,TR/5h:6d, meaning we travel from London via Vienna to Istanbul, driving 5 hours daily while our total time budget would be 6 days. At given breakpoints (after 5 hours) we can explore the nearby locality in radius of some 30-40 miles, for lodgings/hotels, stores, camps etc. (the list is long, but currently limited to few). We can add the time we are about to spend on a given destination, and it will be automatically deducted from our time budget, so the limit will be flagged on the route. As you could assume, I use Google Maps Javascript API, which gives me geocoding, plain routes, nearby search, and other services than the app combines them to create in colorful itineraries. (colors are for distinction) https://camptarget.com P.S. Due to me being only developer, designer etc. it might not work well on all of the platforms, but it should work in the latest 2 version of ff and gc.

Visit productView on Hacker NewsAug 9, 2020
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
37%37% 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, coding, plain · Missing: mac, agents, macos
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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.

Incorrect prediction on native model

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