What it does:

- Parses Svelte files with a custom Rust parser - Transforms them to TSX in parallel using Rayon - Runs type-checking via Microsoft's tsgo (the native Go port of TypeScript) - Maps errors back to original .svelte locations via source maps

Why it's fast:

The official svelte-check uses TypeScript's Language Service API optimized for IDEs with persistent connections. Great for autocomplete but slow for batch CLI checks.

svelte-check-rs writes real TSX files to disk and runs tsgo as a standalone compiler. This enables incremental builds with persistent .tsbuildinfo, so subsequent runs only re-check changed files.

Benchmarks on a 650-file SvelteKit monorepo (M4 Max):

  Cold: 17.5s vs 39.6s (2.3x faster)
  Warm: 1.3s vs 39.4s (30x faster)
  Iterative: 2.5s vs 39.8s (16x faster)

I built this in ~2 days using Claude Code (Opus 4.5) and Codex CLI (GPT-5.2 xhigh). The entire Svelte parser, TSX transformer, diagnostics engine, and CLI were written entirely by AI. I focused on architecture decisions and testing against real codebases while the models handled the implementation.

My motivation was actually to make AI coding agents more effective. When agents write code, they need to verify it works, and waiting 40 seconds for type-checking kills the feedback loop. With 1-2 second checks, agents can iterate much faster and catch their own mistakes immediately on our large and growing production SvelteKit codebase.

Website: https://svelte-check-rs.vercel.app/

Comments URL: https://news.ycombinator.com/item?id=46478789

Points: 6

# Comments: 0

The developers might feel more productive because they're engaging with their code at a higher level of abstraction, even if it takes longer. This would be consistent with why they maintained positive perceptions despite the slowdown.

Highly agree with the sentiments expressed in this post, I wrote about something similar in my blog post on "Artificial General Software": https://www.markfayngersh.com/posts/artificial-general-softw...

anthropic figured out something about real world coding that openai is still trying to catch up to, o3-mini-high notwithstanding.

Comments URL: https://news.ycombinator.com/item?id=42480725

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42119106

Points: 14

# Comments: 0

The paper actually makes a stronger case for using LLMs to enhance rather than replace human strategists - imagine a military commander with instant access to an aide that has deeply analyzed every military campaign in history and can spot relevant patterns. The question isn't about putting LLMs "in charge," but whether we're fully leveraging their unique capabilities for strategic innovation while maintaining human oversight.

- Examine how the human-AI relationship evolved as the AI system improved during the study period

- Theorize more explicitly about which aspects of human judgment might be more vs less persistent

- Consider how their findings might change with more capable AI systems

- Reduced creativity and ideation work (dropping from 39% to 16% of time)

- Increased focus on evaluating AI suggestions (rising to 40% of time)

- Feelings of skill underutilization

[^1]: https://github.com/pheuter/hnpush

Comments URL: https://news.ycombinator.com/item?id=42041470

Points: 4

# Comments: 2

Comments URL: https://news.ycombinator.com/item?id=41722742

Points: 236

# Comments: 424

I also believe increasingly capable and sophisticated AI systems will play a formative role in transforming education, not as the current chatbots that are disrupting education as mentioned in the article, but as active participants in the reimagined classrooms of the future. The transition will probably be rough, but it has the potential to bring about a better future and more fruitful learning and writing.