CMS-free in-place editable websites with Svelte (v2)

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Almost 3 years ago I posted v1 here. This is v2, a complete rewrite built on Svedit, an open source rich text editor I wrote specifically for structured in-place editing. The idea is simple: The website itself becomes the CMS. There’s no admin panel. You define your content model in JavaScript and write Svelte components to render it. Once deployed the client can login and change content live on the page, create new pages, and link them up to form a website. The data is stored in SQLite. It’s fast and doesn’t require a rebuild when content changes. My goal is to make small to medium websites editable, without depending on a 3rd party system. This is likely not a good fit if you need multi-author workflows, or heavy editorial tooling. Curios to hear what you think.

Visit product View on Hacker NewsFeb 25, 2026

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

89%89% 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.

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

76%76% 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 HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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

32%32% 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

19%19% 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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