Dan Shiebler

Generative large language models (LLMs) like ChatGPT are like wild beasts. They are powerful but difficult to tame. One formidable challenge lies in the cost and latency associated with these models. Compared to traditional software solutions, LLMs can operate at a slower pace, incurring higher costs along the way. Instead... [Read More]

Generative large language models (LLMs) like ChatGPT will revolutionize the way we approach complex problems. Enormous amounts of custom labeled data are no longer required to train many specialized AI systems. LLMs make generalized problem-solving capabilities vastly more accessible. This has profound implications for software development, data science, and machine... [Read More]

ML models are collaboration bottlenecks. Suppose your team owns a binary classification model, and you have decided that increasing this model’s recall without reducing precision is very important for the business. You’ve put 5-10 engineers on the job. The Problem What should these engineers do? There are many ways to... [Read More]

One of the hallmarks of a great engineer is the ability to execute. An engineer who can execute gets things done. They crush tickets, pump out designs, spin up features, answer critical data questions, improve models, and prototype new ideas quicker than their peers. How to Execute Engineering projects are... [Read More]

A common problem in machine learning is “use this function defined over this small set to generate predictions over that larger set.” Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In a recent... [Read More]