PrevelteKit – Static-first Svelte/Rsbuild/jsdom framework

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I built PrevelteKit to solve the architecture trade-offs I ran into with modern web apps. SPAs have clean separation between frontend and backend, but users see a white loading page or spinner while JavaScript loads and renders the initial UI. Next.js/SvelteKit solve the loading problem but require running your JavaScript stack in your frontend infrastructure. While services like Vercel hide this operational complexity (and they do it quite well), you're still coupling your frontend deployment to server infrastructure and adding another moving part to your system. I wanted something in between, a clean separation like SPAs, but with something already loaded for users. PrevelteKit pre-renders and hydrates your Svelte components at build time using jsdom, then serves them as pure HTML/CSS/JS files. During loading, users immediately see the layout and static content, while dynamic content takes another round-trip. Key features: * Static-first - No JavaScript runtime needed in production for the frontend * Minimalistic - Less than 500 lines of code, just glue for existing tools * Fast builds - Uses Rsbuild for rapid bundling (~hundreds of milliseconds with Rsbuild) * Deploy anywhere - Pure static files, no server required, deploy on GitHub Pages, S3, any web server The concept is working well for my projects and I'm looking for feedback on the approach and whether others find this useful.

Visit product View on Hacker NewsSep 26, 2025

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

80%80% 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, user, using · Missing: mac, agents, macos

76%76% 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: exist, existing, ide · Missing: https docs, excited, just released

44%44% 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: users · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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

18%18% 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.

Correct prediction on native model

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