Browsing: Machine Learning

Picture an agent that answers questions about customer invoices. It has one tool, a lookup against an internal API, and a task that needs four lookups. The first two go through fine. Then the credential behind that API expires, and every call from the third one onward comes back as a 401.

A specific kind of quiet follows a good validation run. Numbers on the screen, AUC at 0.91, and everyone nodding along in the morning meeting because, for once, nobody had a reason to ask a follow-up question.

In 2022, a small team of researchers ran an experiment that should have been unremarkable. They trained a tiny neural network, far smaller than anything that gets called “AI” today, on one of the simplest tasks imaginable: modular addition, basically clock math. What is 8 plus 7 if the clock only goes up to 12?…

Benjamin Nweke described a failure that took him three weeks to notice. He had turned on Structured Outputs for a pipeline that parsed payment confirmation messages into transaction records, and his reconciliation job started flagging a small, steady stream of mismatches — a couple of percent of a week’s volume. Not crashes. Not malformed rows.…

We use the word AI to describe all sorts of different things, from talking to ChatGPT in a browser to running entire businesses where operations would simply cease to exist in their current form without language models. Where is the line between using AI automation and AI being at the core of a business? At…

Every major inference framework shipped prefill-decode disaggregation this year. NVIDIA built it into Dynamo. SGLang made it the default for large-scale deployments. vLLM added a KV connector API to support it natively. The consensus is forming fast: split your prefill and decode onto separate GPU pools, and throughput improves.