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Weather the Trip’ Built on Kotlin Multiplatform Mobile

Hacker News

Weather the Trip’ Built on Kotlin Multiplatform Mobile

Hi there Jason Marks here~ I’ve been browsing hacker news for years and am happy to share with the community my latest project Weather the Trip. Weather the Trip provides you with forecasts and recommended departure times for your road trips within the U.S. I did the whole legal zoom LLC angle trying to bootstrap this idea to something. So far this is a difficult process but I find it fun and rewarding. Download link > www.weatherthetrip.com/download If you are unacquainted, Kotlin Multiplatform Mobile (KMM) helps you create a core business logic library that includes all the tools and convenience to compile directly to the platforms you would like to build for. You get to keep all the benefits that native applications inherently have without having to port the business logic amongst the platforms! The native portions of the Android and iOS app for Weather the Trip are pretty much just view related code. I think KMM is a great platform but I feel obligated to a few critiques. Right now, there are not a lot of third-party dependencies that are interesting which also compile to the swath of targets available. Worse yet some that are interesting get out of date fast since KMM is still not beta (the caveat is SQLDelight for ur tables which is [in fact] delightful). The first party core dependencies do have you covered for all of the essentials: Net, Serialization (no XML), Datetime (no formatters at the moment), Concurrency. Concurrency has additional complexity to get right. The first party coroutines work great but not all targets know what a coroutine is (looking at you iOS). Out of the box you are not able to use coroutines inline to something like a Reactive pattern for all targets. I ended up deciding on flinging out results through interfaces and catching and redirecting using native abstractions. It isn’t elegant but works without getting too clever. This path does require a contract with the core library where calls must return data or an error to the target. I will end on an acknowledgement that other multiapp platforms exist. I am a believer that native apps always outperform alternatives in UX. I won’t bother ranting against other multiapp platforms. If your skillset fits a certain platform then do that. If you are willing to pickup Kotlin, Swift, etc. then KMM is worth your time. Still reading? I’m also on twitter @jmarks213 Still still reading? How about some inflammation???? Do you think the Kremlin is in the Kotlin? side ways eye emoji

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
90%90% 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: apps, new, using · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, hacker news, ide · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, interface, calls · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · 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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