Articles about the training-data pipeline, model training, tokenization, and other non-model components.
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The v0.5.0-a1 tokenizer had a dirty secret: it was trained exclusively on US and French addresses. When it encountered Chinese, Japanese, Korean, Thai, or Arabic text, it fell back to encoding individual bytes — 50-75% of tokens for CJK scripts. Every byte-fallback token is a lost opportunity for the model to learn meaningful subword patterns.
Today we fixed that.
We shipped a browser-based address parser that runs a 29 MB ONNX model entirely client-side. The Playwright tests showed perfect results. Chrome desktop looked great. Then someone opened it on an iPhone.
The second night shift ran from roughly 2am to 2pm UTC on May 25th, 2026. It started with a GPU that wouldn't stop crashing and ended with a trained model, an ONNX export, and a full evaluation report. This is the story of how infrastructure choices turned a hardware problem into a non-issue.