Coding Java Swing in Kotlin-idiomatic way

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It's a pet project from the time I got amazed by Kotlin's ability to express DSLs. I also needed to quickly prototype a remote control UI for my art robots using RxJava-based event stream processing. Recently I had some time to kick out RxJava from these projects in favor of coroutines and Kotlin-native Flows, publish artifacts to maven central and to release the official v1.0. I hope this more declarative, more reactive and less verbose way of using Swing might be useful also for someone else.

Visit product View on Hacker NewsAug 26, 2024

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

46%46% 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

40%40% 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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

14%14% 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.

Correct prediction on native model

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