CrossFit Timers with Svelte, TailwindCSS and Firebase

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As a java backend developer decided to practice some front end work (Each backend developer dreams to become FrontEnd developer :-D ) I сhoosed Svelte+TailwindCSS to get more familiar with frameworks as I planning to use it on my next project(probably SvelteKit+TailwindCSS and SSR on Firebase function). But to get started, decided to begin with something simple, pure Svelte+TailwindCSS and Firebase hosting. And as my morning routine starts with some workout, decided to implement CrossFit timers. This simple project allow me to understand more deeply: - work with components and cross-component communication - work with local storage (custom store), so you can save yours timers to local storage - PWA capabilities, you can install it on your device and it can work in offline mode. - get familiar with utility classes and configuration of Tailwind - deploy it to Firebase hosting Few words about product's features: Four different timers available (Tabata, EMOM, AMRAP, RFT); possibility to save your timer; you can share your timers e.g. between your devices or with your friends or audience(for this purpose, custom URL parser/generator was implemented as Svelte doesn't support routing unlike SvelteKit/Sapper); PWA and Offline mode. Conclusion/Impression: It's pleasure to work with Svelte, it allows you write well structured code, all concepts are understandable and easy to apply, pre built animations and transition, flexibility With TailwindCSS you can quickly prototype UI components with any complexity with 99% of cases, otherwise extensions are available. Firebase allowed me to deploy the app in few clicks and, as it just hosting, free quotes are available.

Visit product View on Hacker NewsJul 25, 2022

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

83%83% 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: code · Missing: mac, agents, macos

51%51% 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: ide, io · 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

12%12% 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.

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