Yes. We open sourced an SSG based off SvelteKit about a year ago and were starting building AI tooling and product workflows around it, https://statue.dev but have since mothballed it.

For most of 2024-2025 frontier models really struggled with Svelte 4 vs 5 compatibility issues. Some of the main contributors, to their credit, really put in a lot of work to create benchmarks/MCP/other tools to make AI better at using Svelte. Personally I am just not a fan of MCP and other tools like that at all, and decided I'd rather just use vanilla css/js for new projects.

Look at this; it's literally multiple times more expensive to have a model sifting through all this stuff in every context use them with a tool: https://svelte.dev/docs/ai/prompts/llms.txt All of these are input tokens and turns to gather context.

The "too niche to be a major training priority" dilemma is actually a really big problem that almost all new or emerging dev tools have now. Incumbents and category leaders are priorities and show up in major benchmarks, so models develop excellent tacit knowledge and capabilities with them and expose it everywhere all the time for "free" in their weights. Everything else is at a major disadvantage because models don't know about them, how to use them, how they work, what they're for/why, and every time you use them you pay a big capability and token hit because models only learn about them in-context.

I don't blame the Svelte team for this at all. There should be a better way for devtool projects to contribute to frontier labs training pipelines or properly posttrain their own agent coding models.