I made the game LexiCraft – Have a 2min break and Try it [SPOCK stack]

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I worked on nailing down a few technical aspects for the Pocketbase-centric stack I'm focusing on at the moment. And as a lot of you I like to use simple games as playground projects for my tests. That's how I made LexiCraft a word game with Sveltekit for the frontend & Pocketbase as backend. You can play it in browser on mobile, tablet and desktop or install as a PWA. Current rules are based on the game `Spelling Bee` but I added several extra features (see below) and will work on expanding in the future. The cross-device sync makes opening an account a requirement but everything there is free and without any ads. I'd love to hear your feedback and suggestions! Thanks MORE: Highlighted features: * Pocketbase<->Svelte stores data layer * Multi-game support (save and switch) * Cross-device syncing * Keyboard support on desktop STACK: [self-hosted SPOCK] * fe: Sveltekit * css: Tailwind + Shadcn-svelte * be: Pocketbase * host: Cloudflare Pages * cdn: Cloudflare * analytics: Plausible * grinder: Good ol' bash ROADMAP: * New languages * Realtime database events (overkill this project but want to learn more about Pocketbase's RT capabilities) * Package via capacitor to emulate native app

Visit product View on Hacker NewsOct 22, 2024

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

86%86% 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.

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% 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 · Missing: mobile apps, ios, personal

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

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

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

Acquire.comPre-revenue stage for this audience · 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 · 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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