Chapter 1: What is the main topic discussed in this episode?
Dax Rod is the co-founder of OpenCode, the most popular open source coding harness. He's also a very down-to-earth when it comes to AI, which can be a bit surprising when you consider that he built such a widely used AI tool. Today we talk about the rapid growth of OpenCode to closer to 10 million active users in less than a year.
The memo Dax sent to his team admitting they were shipping too many features, taking on too many hacks, and AI usage not helping them move faster. why inference is one of the most profitable businesses in tech right now, and why even open code is bottlenecked by GPU supply, and many more.
If you'd like to ignore the hype on social media or on AI, and instead talk about where it helps or hinders productive engineering teams, this episode is for you. This episode is presented by Antisys.
Chapter 2: What led Dax Raad to co-found OpenCode?
Verify your system's correctness without human review or traditional integration tests, and avoid bugs or outages. I'd also like to mention our season sponsor, TurboBuffer. You've probably heard someone say rag is dead these days, but it's not. It just doesn't mean what it meant in 2023. Back then, retrieval was pretty straightforward.
The human asks a question, your code embeds it, you hit a vector database, top K results come back, those get stuffed into context, and the LLM answers. Now look at what's actually happening inside something like OpenCode or any serious agent product in 2026. A human sends one prompt to an orchestrator agent. That agent fans out to sub-agents. Each sub-agent is hitting separate systems,
a vector index, a full-text index, grabbing the file system, running CLI commands and SQL against OLTP and OLAP stores, reading and writing a memory system, re-ranking results, looping, and calling more tools. One human prompt turns into dozens or even hundreds of searches across totally different shapes of data.
In practice, this means a lot more complexity, a lot more cost, and a lot more potential performance issues, especially when you need to scale up the system. This is exactly what TurboPuffer is built for. It's a ridiculously scalable, fast, and cheap search engine. It's built on top of object storage for reliability and scale, with smart caching on NVMe SSDs, so it's very fast.
Chapter 3: How did OpenCode respond to Anthropic's blocking of integration?
It's priced so you can let agents loose with proper rag in 2026 without seeing your bill explode. Check it out at turbobuffer.com slash pragmatic. Dax, welcome to the podcast.
Yeah, thanks for having me and thanks for coming all the way to Miami.
I wanted to jump in to something really interesting about you. You're building one of the most popular AI engineering harnesses, OpenCode, which is speeding up how people write code and just like turn out software. And yet you're claiming...
that this itself is not enough like this itself will not get us to better software i've always said the easiest products are ones that you can use yourself and obviously we built open code so that our team can use it and it's like we are the customers of the product uh so we use it aggressively as much as anyone else can and of course it is we build it so we think it's useful and we use it every day and it's a critical part of our workflows but
All the old problems that I've always struggled with are still there. I'm working as hard as I ever have. I'm struggling as hard as much as I ever have. So a lot of the job has become easier. But yeah, it's a weird feeling because objectively stuff has become easier. But then why am I like thinking as hard as I ever have? You know, it's a weird feeling to have both those things be true.
And it's interesting because a lot of the people who are decision makers, CEOs, often hands-on people, like hands-on CEOs, CTOs, founders of companies, they kind of think, oh, look, we've got these tools.
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Chapter 4: What challenges arise from the rapid growth of AI coding tools?
Coding used to be the hard part, right?
Chapter 5: Why is GPU demand becoming a bottleneck for AI tools?
Like objectively, it took us so much time. It still takes to get into the zone if you're going back to coding by hand. So if that's faster, because that's where we used to spend most of our time, it should be faster. Like everything should be faster. but why do you think this is not the case?
Chapter 6: What are the implications of AI hype on engineering productivity?
Or like, what is getting in the way of like, just like shipping high quality software faster, better, right? Yeah.
I mean, there's different, there's different life cycles, different companies. There's like pre-product market fit. There's achieved product market fit, which is kind of where we are. And there's companies that are like, have had probably market fit for like a decade. And I imagine that things look very different across these three.
For us, pre-private market fit, to me, it doesn't really help that much because you're trying to figure out what you should be doing. And yeah, like maybe it helps you swing a lot, but I've always thought it's better to think a lot instead of swinging a lot.
Chapter 7: How does Dax Raad view the future of engineering and work?
I think you can eliminate a lot of ideas or directions just by, you know, spending a lot of time in your head and with your team talking. Obviously, AI doesn't speed that part up. We're at the phase where we've achieved product-market fit.
Chapter 8: What role does engineering culture play at OpenCode?
Now our task is to kind of hit the potential that we have. And the issue for us is there's a million different directions we can go in. There's all the obvious stuff we can do. There's all the stuff that our users are telling us that we have to do. There's stuff that our competitors are doing. And it's very easy to just one-to-one do each one of those things because
we have a problem prompt the agent competitor has a feature prompt the agent user has a problem prompt the agent if you add that up you think oh we shipped a thousand features now that adds it to a good product it actually adds it to a horrible product yeah nothing's cohesive you look in there you're like we shouldn't have shipped this the moment you ship something you're stuck supporting it forever and by supporting it means any future feature you build is gonna like interact with it so
You still have to be very conservative with what you put out there. It's hard to undo anything. Just because we can ship 10 times more doesn't mean we have 10 times as many good ideas to ship out there. So in a lot of ways, my struggle has now been, how do I slow everyone down? And like understanding that, yes, our process can look very different, but should it look very different?
Like we, you know, we we've done in the past six months, we've kind of operated very differently than we ever have. A lot of stuff went wrong because of that. So now we're pulling back and figuring out, okay, what from the old world still makes sense. So yeah, we're like figuring out what we should be doing. And I definitely don't feel like, oh yeah, we're like killing all our competitors.
We're using AI so much better than everyone else. Um, And by the way, none of our competitors are crushing us either. Like no one out there is using AI so well that they just like we can't even compete, right? And we're in the coding agent space. All our competitors are super into AI. So you would think in our space there would be like a huge gap, but there just isn't.
So I want to rewind back to the very beginning of how, you know, before AI, before a lot of these things, how did you get into tech and software engineering?
yeah so i uh i grew up kind of cliche story i grew up programming as a kid uh my dad was a software engineer so a little bit easier for me to get into than for other people then just started working out of high school uh founded a company thought it was cool though i knew what i was doing looking back in hindsight like wow i didn't know what i was doing at all um that eventually got aqua hired as a small acquire uh and i ended up in like the real tech industry
Bounced around as a consultant, founded a few companies, and then ended up doing open source pretty much the past six years full time.
But going back to the very beginning, I found or saw some references to you working on Minecraft, Minecraft servers. Yeah, yeah. In the beginning, can you take us back a little bit?
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