Streamlit recently burst onto the scene with their intuitive, open source solution for building custom ML/AI tools. It allows data scientists and ML engineers to rapidly build internal or external UIs without spending time on frontend development. In this episode, Adrien Treuille joins us to discuss ML/AI app development in general and Streamlit. We talk about the practicalities of working with Streamlit along with its seemingly instant adoption by AI2, Stripe, Stitch Fix, Uber, and Twitter.

Johnny is joined by Marty Schoch, creator of the full-text search and indexing engine Bleve, to talk about the art and science of building capable search tools in Go. You get a mix of deep technical considerations as well as some of the challenges around running a popular open source project.

Adam adds a twist to our YepNope format this week. Instead of 2v2, it’s 1v1v1 with Mikeal reppin’ team Yep, Divya on team Nope, and Feross sitting in the middle on team It Depends. You don’t want to miss this excellent debate/discussion all about JS tooling complexity.

Many packages
New frameworks built all the time
Config hell. Webpack

We’re talking about the tools we use every day help us to be productive! This show will be a great introduction for those new to Go tooling, with some discussion around what we think of them after using some of them for many years.

Adam caught up with Francesc Campoy at KubeCon + CloudNativeCon 2018 in Seattle, WA to talk about the work he’s doing at Source{d} to apply Machine Learning to source code, and turn that codebase into actionable insights. It’s a movement they’re driving called Machine Learning on Code. They talked through their open source products, how they work, what types of insights can be gained, and they also talked through the code analysis Francesc did on the Kubernetes code base. This is as close as you get to the bleeding edge and we’re very interested to see where this goes.