There are some big AI-related controversies swirling, and it’s time we talk about them. A lawsuit has been filed against GitHub, Microsoft, and OpenAI related to Copilot code suggestions, and many people have been disturbed by the output of Meta AI’s Galactica model. Does Copilot violate open source licenses? Does Galactica output dangerous science-related content? In this episode, we dive into the controversies and risks, and we discuss the benefits of these technologies.

Kent and our panelists dive deep on the hottest new React framework: Remix. What it does today, what makes it special, how it lured Kent away from a lucrative independent teaching career, and what’s coming up next.

We are at GenAI saturation, so let’s talk about scikit-learn, a long time favorite for data scientists building classifiers, time series analyzers, dimensionality reducers, and more! Scikit-learn is deployed across industry and driving a significant portion of the “AI” that is actually in production. :probabl is a new kind of company that is stewarding this project along with a variety of other open source projects. Yann Lechelle and Guillaume Lemaitre share some of the vision behind the company and talk about the future of scikit-learn!

Benn Stancil’s weekly Substack on data and technology provides a fascinating perspective on the modern data stack & the industry building it. On this episode, Benn joins Jerod to dissect a few of his essays, discuss opportunities he sees during this slowdown & explain why he thinks maybe we should disband the analytics team.

We’re back with another spicy YepNope debate! This time, Amelia and KBall are arguing that there’s real value to (continue) using React in 2022, while Amal and special guest (and author of the post which stemmed the whole debate) Josh Collinsworth argue that React’s time leading innovation has passed. Of course, the stance each panelist is taking is assigned ahead of time. Is that how they really feel? Tune in and find out!

Federated learning is increasingly practical for machine learning developers because of the challenges we face with model and data privacy. In this fully connected episode, Chris and Daniel dive into the topic and dissect the ideas behind federated learning, practicalities of implementing decentralized training, and current uses of the technique.

We did an episode on functional programming in Go with Aaron Schlesinger back in 2019… But that was before generics were a thing. Let’s revisit the topic and discuss the pros and cons now that we have generics. What’s changed? What hasn’t?

Natalie & Ian welcome Liran Haimovitch & Tiago Queiroz to the show for a discussion focused on debugging Go programs. They cover good & bad debugging practices, the difficulty of debugging in the cloud, the value of errors logs & metrics, the practice of debugging in production (or not) & much more!

Bryan Cantrill, Co-founder and CTO of Oxide Computer Company, joins Adam to share his journey from Sun to Oxide – from Sun and Fishworks, to DTrace, to ZFS, to Joyent and Node.js, and now working to build on-prem cloud servers as they should be at Oxide.

It’s been a while since we’ve touched on quantum computing. It’s time for an update! This week we talk with Yonatan from Quantum Machines about real progress being made in the practical construction of hybrid computing centers with a mix of classical processors, GPUs, and quantum processors. Quantum Machines is building both hardware and software to help control, program, and integrate quantum processors within a hybrid computing environment.

What is a Product Manager, and do Engineers need them? In this episode, we will be discussing what a Product Manager does, what makes a good Product Manager, and debating if engineering teams truly need them, with some tech companies going without them. We are joined by Gaëlle Sharma, Senior Technical Product Manager, at the New York Times, leading the Identity group.

KBall, Ali & Nick explore a new type of segment: “WTFJS” talking about wild and wooly “it’s not a bug it’s a feature” examples in the JavaScript language. They also dive into code maintainability, and end by discussing the whiplash shift in the tech industry from “hottest market for engineers in history” to “oh noes everything is stopping!”

Hugging Face is increasingly becomes the “hub” of AI innovation. In this episode, Merve Noyan joins us to dive into this hub in more detail. We discuss automation around model cards, reproducibility, and the new community features. If you are wanting to engage with the wider AI community, this is the show for you!

When is the last time you had a eureka moment? Chris had a chat with Nicholas Mohnacky, CEO and Cofounder of bundleIQ, where they use natural language processing algorithms like GPT-3 to connect your Google GSuite with other personal data sources to find deeper connections, go beyond the obvious, and create eureka moments.

Don’t all AI methods need a bunch of data to work? How could AI help document and revitalize endangered languages with “human-in-the-loop” or “active learning” methods? Sarah Moeller from the University of Florida joins us to discuss those and other related questions. She also shares many of her personal experiences working with languages in low resource settings.

Recently Chris and Daniel briefly discussed the Open RAIL-M licensing and model releases on Hugging Face. In this episode, Daniel follows up on this topic based on some recent practical experience. Also included is a discussion about graph neural networks, message passing, and tweaking synthesized voices!

Mark Nadal joined the show to talk about his hacker story and his venture backed open source datastore project called GunDB — a realtime, decentralized, offline-first, graph database engine. We talked about the details behind this database, how Mark secured funding, why yet another datastore, who’s using the database, how Mark plans to sustain this project through products and services, his thoughts on the RethinkDB postmortem and more.

Ashley Willis and Ela Krief join Natalie to discuss the ins and outs of management. They discuss what makes a good manager, common mistakes managers make, how to communicate effectively, dealing with conflict, and much more.

Kent C. Dodds joined the show to talk about guiding and supporting first time contributors to open source. We talked about the many ways to be first-timer friendly, how to contribute to open source, the burden and balance of a maintainer, and a few of the projects Kent maintains, including his latest project at PayPal called Glamourous.

AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment, and is accelerating research in nearly every field of biology. Daniel and Chris delve into protein folding, and explore the implications of this revolutionary and hugely impactful application of AI.

Here’s a bonus episode this week from our friends behind Me, Myself, and AI — a podcast on artificial intelligence and business, and produced by MIT Sloan Management Review and Boston Consulting Group. We partnered with them to help promote their awesome podcast.

We hand picked this full-length episode to share with you because of its focus on using technology as a force for good, something we’re very passionate about. This episode features, Paula Goldman, Chief Ethical and Humane Use Officer at Salesforce, and the conversation touches on some interesting topics around the role tech companies play in society at large.

Ever wonder how new features get added to the go command? Or where tools like gopls come from? Well, there’s an open team that handles just those things.

Just like the programming language itself, many of the tools that Go engineers use everyday are discussed and developed in the open. In this episode we’ll talk about this team, how it started, where it’s going, and how you can get involved.

In this “fully connected” episode of the podcast, we catch up on some recent developments in the AI world, including a new model from DeepMind called Gato. This generalist model can play video games, caption images, respond to chat messages, control robot arms, and much more. We also discuss the use of AI in the entertainment industry (e.g., in new Top Gun movie).

There are a lot of people trying to innovate in the area of specialized AI hardware, but most of them are doing it with traditional transistors. Lightmatter is doing something totally different. They’re building photonic computers that are more power efficient and faster for AI inference. Nick Harris joins us in this episode to bring us up to speed on all the details.

Mat Ryer returns with his guitar, an unpopular opinion & his favorite internet virus.

Any AI play that lacks an underlying data strategy is doomed to fail, and a big part of any data strategy is labeling. Michael, from Label Studio, joins us in this episode to discuss how the industry’s perception of data labeling is shifting. We cover open source tooling, validating labels, and integrating ML/AI models in the labeling loop.

We’re celebrating our 200th episode with a crazy game of Gophers Say! Mat Ryer hosts two epic teams including Go Time OGs Carlisia, Erik, and Brian!

Nick and Chris welcome back Mik and Bret to discuss logging and error handling in Node and JavaScript and the subtleties and intricacies that extend far beyond console.log!

Ahmad Awais joins Amal, Amelia, and Jerod to discuss scripting, automation, and building CLIs with Node! We hear Ahmad’s back story, learn the ABC’s of mastering Node automation tooling, and share automation wins from all of our lives (and Twitter too).

Chris sits down with Ankur Goyal to talk about DocQuery, Impira’s new open source ML model. DocQuery lets you ask questions about semi-structured data (like invoices) and unstructured documents (like contracts) using Large Language Models (LLMs). Ankur illustrates many of the ways DocQuery can help people tame documents, and references Chris’s real life tasks as a non-profit director to demonstrate that DocQuery is indeed practical AI.