I built a 30x faster svelte-check in 2 days with AI
I built a Rust drop-in replacement for svelte-check that's 10-30x faster for Svelte 5 projects. 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…
What it does
In the maker’s words, at launch
I built a Rust drop-in replacement for svelte-check that's 10-30x faster for Svelte 5 projects. 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) The AI part: 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/
Does the same job
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Serialization framework with some interesting numbers: 10-20x faster on nested objects than json/protobuf. Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs. Unique features that other fast serializers don't have: - Cross-language without IDL files (Rust ↔ Python/Java/Go) - Trait object serialization (Box) - Automatic circular reference handling - Schema evolution without coordination Happy to discuss design trade-offs. Benchmarks:…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
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Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
AI · Jan 2026 · cannoneyed.com
Automatic AI-powered code reviews the moment you open a PR
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