C++

Background: Why BPE Training Speed Matters In practice, BPE vocabulary training is slow enough that most practitioners sub-sample their data before learning a vocabulary. A common recipe is to randomly sample a few million sentences, train BPE on that, and apply the learned vocabulary to the full dataset. For dataset with less diversity or a single language, this is usually fine — a million sentences captures most frequent patterns. But for massively multilingual models, sub-sampling is a serious compromise. In 2020, I was building a many-to-English translation system covering 500+ languages (see our ACL 2021 paper write-up). The full training corpus had roughly half a billion sentences across hundreds of languages. Many of these languages had only a few thousand sentences each. Sub-sampling a few million sentences meant that low-resource languages would be drastically underrepresented in the vocabulary — their unique character sequences and morphological patterns would be treated as rare noise rather than learned as proper subword units. ...

Modern LLMs are chat models that use Jinja2 templates to format conversations. But Jinja is Python-centric, making deployment hard in C and edge environments. Here's how to build a Jinja2 template engine from scratch in C — covering lexing, parsing, and evaluation — so you can render chat templates natively without Python.

I gave Claude Opus 4.6 and GPT 5.4 the same task: rewrite pigz (a parallel gzip tool) in modern C++23. After 70 minutes, one delivered a clean-room rewrite, the other a wrapper around the old code. Then I pushed the winner to beat pigz — and it did.