Timber

While everyone's losing their minds over LLMs, someone finally remembered that classical ML—XGBoost, LightGBM, scikit-learn, CatBoost—still powers most of production AI. The problem? Running these in Python is slow as hell. Timber is here to fix that. It's an AOT compiler that takes your existing models and turns them into native C99 inference code. One command to load, one command to serve. 336x faster than Python. That's not a typo—it's 336 times.

Timber supports XGBoost, LightGBM, scikit-learn, CatBoost, and ONNX models, so you can take whatever you've already trained and compile it down to blazing-fast C code. No rewrites, no framework migrations—just better performance. Think of it as Ollama for the traditional ML world that still actually runs most real-world systems. It's still early (just showed up on Hacker News 19 days ago and already has 207 points and 33 comments), but the idea is simple: why suffer through Python inference latency when you can compile your way to speed?

This is the kind of infrastructure tool that doesn't sound sexy but saves companies millions in compute costs. The ML world is overdue for a compiler that treats classical models like first-class citizens.