In this episode we welcome back our good friend Demetrios from the MLOps Community to discuss fine-tuning vs. retrieval augmented generation. Along the way, we also chat about OpenAI Enterprise, results from the MLOps Community LLM survey, and the orchestration and evaluation of generative AI workloads.

Nick celebrates a decade of writing everyone’s favorite language with guest Josh Goldberg, who contributes to TypeScript, maintains typescript-eslint, and is an all-around great person! Jerod is also here to join the celebration, but let’s keep that a secret from him!

Leslie Lamport is a computer scientist & mathematician who won ACM’s Turing Award in 2013 for his fundamental contributions to the theory and practice of distributed and concurrent systems. He also created LaTeX and TLA+, a high-level language for “writing down the ideas that go into the program before you do any coding.”

Ok Homelabbers, it’s time to unite! Join Adam and his new friend Techno Tim for 1.5 hours of homelab goodness. From networking and WiFi, virtualizing Ubuntu running Docker containers, to Home Assistant and automation, building a Kubernetes cluster, to gutting a perfectly good machine just to build exactly what you need to run the ultimate Plex server — that’s what homelab is about. Let’s do this.

This week it’s storytime with Steve Yegge! Steve came out of retirement to join Sourcegraph as Head of Engineering. Their next frontier is Cody, their AI coding assistant that answers code questions and writes code for you by reading your entire codebase and the code graph. But, we really spent a lot of time talking with Steve about his time at Amazon, Google, and Grab. Ok, it’s storytime!

The Go ecosystem has a hoard of tools and editors for Gophers to choose from and it can be difficult to find ones that are a good fit for each individual. In this episode, we discuss what tools and editors we’re using, the ones we wish existed, how we go about finding new ones, and why we sometimes choose to write our own tools.

As a technologist, coder, and lawyer, few people are better equipped to discuss the legal and practical consequences of generative AI than Damien Riehl. He demonstrated this a couple years ago by generating, writing to disk, and then releasing every possible musical melody. Damien joins us to answer our many questions about generated content, copyright, dataset licensing/usage, and the future of knowledge work.

On Monday, Kelsey Hightower announced his retirement from Google. On Tuesday, he sat down with us to discuss why, how & what’s next.

Along the way, Kelsey teaches us how not to suck at work, analyzes his magical demos, fights off the haters (again) & opines on System Initiative, Dagger & 37Signals moving off the cloud.

Chris and Daniel take a step back to look at how generative AI fits into the wider landscape of ML/AI and data science. They talk through the differences in how one approaches “traditional” supervised learning and how practitioners are approaching generative AI based solutions (such as those using Midjourney or GPT family models). Finally, they talk through the risk and compliance implications of generative AI, which was in the news this week in the EU.

Mat Ryer is back and he’s brought with him 10 tips to be a 10x developer (like he is). After that, we try a new segment we’re calling “Tool Time” (and try out a few jingles for it along the way). Finally, it’s time to review our previous unpopular opinions and put some new ones into the world for your (dis)agreeing pleasure. Join us for an automagical time!

This week we’re talking about Passkeys with Anna Pobletts, Head of Passwordless, at 1Password. Will Passkeys enable a passwordless future? Time will tell. Anna shares the what, the why, how, and the when on Passkeys.

Homebrew project leader Mike McQuaid joins us to weigh in on Apple’s big Vision Pro announcement. We also hit on our favorite (and least favorite) non-AR things from the WWDC 2023 keynote.

Return guests Ben Johnson & Chris James join Mat & Kris to talk about the files and folders of your Go projects, big and small. Does the holy grail exist, of the perfect structure to rule them all? Or are we doomed to be figuring this out for the rest of our lives?

You can’t build robust systems with inconsistent, unstructured text output from LLMs. Moreover, LLM integrations scare corporate lawyers, finance departments, and security professionals due to hallucinations, cost, lack of compliance (e.g., HIPAA), leaked IP/PII, and “injection” vulnerabilities.

In this episode, Chris interviews Daniel about his new company called Prediction Guard, which addresses these issues. They discuss some practical methodologies for getting consistent, structured output from compliant AI systems. These systems, driven by open access models and various kinds of LLM wrappers, can help you delight customers AND navigate the increasing restrictions on “GPT” models.

This week Sarah Drasner joins us to talk about her book Engineering Management for the Rest of Us and her experience leading engineering at Zillow, Microsoft, Netlify, and now Google.

There are a ton of problems around building LLM apps in production and the last mile of that problem. Travis Fischer, builder of open AI projects like @ChatGPTBot, joins us to talk through these problems (and how to overcome them). He helps us understand the hierarchy of complexity from simple prompting to augmentation, agents, and fine-tuning. Along the way we discuss the frontend developer community that is rapidly adopting AI technology via Typescript (not Python).

Large Language Model (LLM) capabilities have reached new heights and are nothing short of mind-blowing! However, with so many advancements happening at once, it can be overwhelming to keep up with all the latest developments. To help us navigate through this complex terrain, we’ve invited Raj - one of the most adept at explaining State-of-the-Art (SOTA) AI in practical terms - to join us on the podcast.

Raj discusses several intriguing topics such as in-context learning, reasoning, LLM options, and related tooling. But that’s not all! We also hear from Raj about the rapidly growing data science and AI community on TikTok.

Matthew Boyle, the author of Domain-Driven Design with Golang, sits down with Jon & Mat to talk about (you guessed it!) DDD with Go.

This week we’re talking about LLMs with Simon Willison. We can not avoid this topic. Last time it was Stable Diffusion breaking the internet. This time it’s LLMs breaking the internet. Large Language Models, ChatGPT, Bard, Claude, Bing, GitHub Copilot X, Cody…we cover it all.

Jerod & the gang catch you up on what’s new and poppin’ in the web development world. We go deep on GitHub Copilot X and the latest AI advancements, take a bathroom break while Nick talks about TypeScript 5 & continue the debate about the future of React.

Dan Abramov & Joe Savona from the React Team join Jerod & Nick for a wide-ranging discussion about React’s place in the frontend ecosystem. We cover everything from React competing with React, their responses to SPA fatigue and recent criticisms, to Server Components and the future of the framework.

This is our 9th Kaizen with Adam & Jerod. We start today’s conversation with the most important thing: embracing change. For Gerhard, this means putting Ship It on hold after this episode. It also means making more time to experiment, maybe try a few of those small bets that we recently talked about with Daniel. Kaizen will continue, we are thinking on the Changelog. Stick around to hear the rest.

With the recent proliferation of generative AI models (from OpenAI, co:here, Anthropic, etc.), practitioners are racing to come up with best practices around prompting, grounding, and control of outputs.

Chris and Daniel take a deep dive into the kinds of behavior we are seeing with this latest wave of models (both good and bad) and what leads to that behavior. They also dig into some prompting and integration tips.

Una & Adam from The CSS Podcast defend their Frontend Feud title against challengers David & Shaw from the keyframers. Let’s get it on!

This week we’re talking to Rachel Potvin, former VP of Engineering at GitHub about what it takes to scale engineering. Rachel says it’s a game-changer when engineering scales beyond 100 people. So we asked to her to share everything she has learned in her career of leading and scaling engineering.

A quick look at the history of building web apps, followed by a discussion of htmx and how it compares to both modern and traditional ways of building.

This week we’re talking about mainframes with Cameron Seay, Adjunct Professor at East Carolina University and a member of the Governing Board of the Open Mainframe Project. If you’ve been curious about mainframes, this show will be a great guide.

Cameron explains exactly what a mainframe is and how it’s different from the cloud. We talk COBOL and the state of education and opportunities around that language. We cover the state-of-the-art in mainframe land, System Z, Linux on mainframes, and more.

Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.

Daniel and Chris do a deep dive into OpenAI’s ChatGPT, which is the first LLM to enjoy direct mass adoption by folks outside the AI world. They discuss how it works, its effect on the world, ramifications of its adoption, and what we may expect in the future as these types of models continue to evolve.

Our 5th annual year-end wrap-up episode! Sit back, relax, pour a glass of your favorite beverage and join us for listener voice mails, our favorite episodes, some must-listens, and of course the top 5 most listened to episodes of the year. Thanks for listening! 💚