Practical insights and engineering guides on local LLMs, edge computing, secure agent runtimes, and scalable ML infrastructure.
Privacy-first forecasting sounds nice in theory. In practice, running statistical packages like ARIMA and exponential smoothing on raw CSVs at the edge meant compiling Rust to WebAssembly. Here is how I built it.
Building an autonomous agent that handles the entire ML lifecycle: from exploratory data analysis in a Code Interpreter sandbox to Bayesian hyperparameter tuning and model deployment on AWS SageMaker.
It started as a thought experiment: multi-turn benchmarks expose a surprising weakness in frontier models. How close could a fine-tuned local model get?
Everyone's declaring RAG dead. They're right about the term and wrong about what it means. I built a code search system to figure out what comes after it, and the answer surprised me: retrieval was the easy part.