Chapter 1: What is the main topic discussed in this episode?
My friends, welcome back. This is The Change Log. We feature the hackers, the leaders, and those living in this crazy world we're in. Can you believe it? Yeah, Damien Tanner's back on the show after 17 years. Wow. Okay, some backstory. Damien Tanner, founder of Pusher, now building LayerCode. He returned to the podcast, technically, officially for the first time, but he sponsored the show.
He was one of our very first sponsors of this podcast ever. 17 years ago, almost. I want to say I'm estimating, but it's pretty close to that. I mean, that's so cool. So he's back officially talking about the seismic shift happening right now in software development. I know you're feeling it. I'm feeling it. Everyone's feeling it. So from first time sponsor of the podcast to a frontline builder,
In the AI agent era, Damien shares raw insights on why SaaS is dying, why code review is becoming a bottleneck, maybe non-existent, and how small teams can build giant things. A massive thank you to our friends, our partners, our sponsor. Yes, talking about fly.io, the home of changelog.com. Love fly, and you should too.
Launch a sprite, launch a fly machine, launch an app, launch whatever on fly. We do. You should too. Learn more at fly.io. Okay, let's do this. Well, friends, I'm here again with a good friend of mine, Kyle Galbraith, co-founder and CEO of depot.dev. Slow builds suck. Depot knows it. Kyle, tell me, how do you go about making builds faster? What's the secret?
When it comes to optimizing build times to drive build times to zero, you really have to take a step back and think about the core components that make up a build. You have your CPUs, you have your networks, you have your disks, all of that comes into play when you're talking about reducing build time.
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Chapter 2: What insights does Damien Tanner share about the shift in software development?
And so some of the things that we do at Depot, we're always running on the latest generation for ARM CPUs and AMD CPUs from Amazon, those in general are anywhere between 30 and 40% faster than GitHub's own hosted runners. And then we do a lot of cache tricks, both for way back in the early days, when we first started Depot, we focused on container image
builds but now we're doing the same types of cache tricks inside of github actions where we essentially multiplex uploads and downloads of github actions cache inside of our runners so that we're going directly to blob storage with as high of throughput as humanly possible we do other things inside of a github actions runner like we cordon off portions of memory to act as disk so that any kind of integration tests that you're doing inside of ci that's doing a lot of operations to disk think like you're testing database migrations in ci
By using RAM disks instead inside of the runner, it's not going to a physical drive. It's going to memory. And that's orders of magnitude faster. The other part of build performance is the stuff that's not the tech side of it. It's the observability side of it. You can't actually make a build faster if you don't know where it should be faster.
And we look for patterns and commonalities across customers. And that's what drives our product roadmap. This is the next thing we'll start optimizing for.
Chapter 3: Why is SaaS considered to be dying according to Damien?
Okay, so when you build with Depot, you're getting this. You're getting the essential goodness of relentless pursuit of very, very fast builds, near zero speed builds. And that's cool. Kyle and his team are relentless on this pursuit. You should use them, depot.dev. Free to start, check it out. One-liner change in your GitHub actions, depot.dev.
Well, friends, I'm here with a longtime friend, first time sponsor of this podcast, Damian Tanner. Damian, it's been a journey, man. Like this is the 18th year of producing the changelog. As you know, when Netherland and I started this show back in 2009, we I corrected myself recently.
Chapter 4: How are small teams able to build giant things in today's tech landscape?
I thought it was November 19th. It was actually November 9th was the very first, the birthday of the changelog. November 9th, 2009. And back then you ran Pusher, Pusher app. And that's kind of when sponsoring a podcast was kind of like almost charity. You didn't get a ton of value because there wasn't a huge audience, but you want to support the makers of the podcast.
And we were learning and obviously open source was moving fast and we were trying to keep up and GitHub was one year old. I mean, this is a different world. But I do want to start off by saying you are our first sponsor of this podcast. I appreciate that, man.
It's very kind of you. You know, reflecting on Pusher, we kind of just ended up creating a lot of great community, especially around London and also around the world with Pusher. And I really love everything we did. We started an event series recently.
And in fact, another kind of like coming back around, Alex Booker, who works at Mastra, he's coming to speak at the AI Engineer London Meetup branch that I run. And he started and ran the Pusher Sessions, which became a really well-known talk series in London. Okay.
Were you at the most recent AIE conference? I was in SF, yeah. Okay. What was that like? We're kind of jumping the shark a little bit, because I want to juxtapose pusher then, timeframe developer to now, which is drastically different. So let's not go too far there, but how was AIE and SF recently?
It was a good experience. Always a good injection of energy going to SF. I live just outside London. But you know what? The venue was quite big, and it didn't have that like...
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Chapter 5: What challenges do developers face with code reviews in the AI era?
together feel as much as some conferences. But it was the first time, though, I sat in a huge conference hall and I think it was like windsurf or something chatting. I was like, this is really like we're all miners at a conference about mining automation. And we're like, we're engineers, so we're super excited about it. But it's kind of weird, like it's going to change all of our jobs.
Right. It's like I'm working right now to change everything I'm doing tomorrow. Right. I mean, that's kind of how I viewed it. I was watching a lot of the playback. I wasn't there personally this time around, but I do want to make it the next time around. But, you know, just the... Sean Swickswang, the content coming out of there, everybody speaking.
I know a lot of great people are there, obviously pushing the boundaries of what's next for us, the frontier, so to speak. But a lot of the content, I mean, almost all the content was like top, top notch. And I feel like I was just watching the tip of humanity. Right.
Like just experiencing what's to come, because in tech, you know, this as being a veteran in tech, we shape we're shaping the future of humanity in a lot of cases because technology drives that technology is a major driver of everything. And here we are at the precipice of the next the next next thing. And it's just wild to see what people are doing with it, how it's changing everything we know.
Everything I feel like is like a flip. It's a complete, not even a one, it's like a 720. You know what I mean? Like it's three spins or four spins. It's not just one spin around to change things. I feel like it's a dramatic, forever, don't even know how it's going to change things, changing things thing.
And, you know, bringing it back to the pusher days, it's the vibe we had then. You know, there was this... period around just before pusher and and the first half of pusher i felt like where we were going through this maybe maybe it's called like the web 2 but there was a lot of great software being built and a lot of you know the the community and i think the
the craft that went into especially the Rails community. And we were able to build incredible web-based software. And then we've gone through the commercialization, industrialization of SaaS. And what gets me really excited is now when we're, you know, we run this AI engineer London branch and incredible communities come together and it's got that energy again.
And I guess the energy is very exciting. There's new stuff. Everyone can play a part in it. And we're also just all completely working it out. And it's like, you've got the, you know, folks on the main stage of the conference, and then you've got, we'll chat about it later, maybe like Jeffrey Huntley posting his meme Ralph Wiggum blog post.
It's like the crazy ideas and innovation is kind of coming from anywhere, which is brilliant.
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Chapter 6: How does LayerCode enhance voice AI agent capabilities?
For those who are uninitiated, and I kind of am to some degree because it's been a long time, remind me and our listeners, what exactly was Pusher? And I suppose the tail end of that, how are things different today than they were then?
Pusher was basically a WebSockets push API. So you could push anything to your web app in real time. So just things like notifications into your application. We ended up having a bunch of customers, maybe in finance or crypto or any kind of area where you needed live updating pricing.
In the early days, at one point, Uber was using Pusher to update the cars in real time before they built their own infra. Um, and it was, it was funny. I remember the standup cause we ran a consultancy where we were chatting about the web sockets in browsers and we're like, Oh, this, this is cool. How can we use this? And the problem is, you know, we, we were all building rails apps.
So like, okay, we need, we need like a separate thing, which manages all the web socket connections to the client. Um, And then we can just post an API request and say, push this message to all the clients. It was a simple idea, and we took it seriously and built it into a pretty formidable dev tool used by millions of developers and still use a lot today.
And we eventually exited the company to MessageBird, who are a kind of European Twilio competitor. Actually, at one point, we nearly sold the company to Twilio. That would have been a very different timeline
According to my notes, you raised $9.2 million, which is a lot of money back then. I mean, it's a lot of money now, but that was tremendous. That was probably 2010, right? 2011, maybe?
The bulk of that we raised later on from Bolton. The first round was maybe half a million. And it started out the agency, so we built the first version in the agency.
Just for fun, I suppose, and maybe some tears on your part, juxtapose the timelines, right? You got an acquisition ultimately, but you mentioned Twilio was an opportunity. How would have that been different if you can branch the timeline?
It would have been a great experience to work. with the team at Twilio. There's incredible people who've worked at Twilio and moved through Twilio. I don't know. I haven't calculated it, but we didn't sell because the offer wasn't good enough in our minds. It was a bit of a low ball and it was all stock. And in hindsight, the stock hasn't gone very well.
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Chapter 7: What is the significance of time to first token in voice AI interactions?
And then we're kind of in there and we're swimming, trying to swim the way we normally swim and the way we want to go. And suddenly I've just gone... Just relax and just let the river take you. Just let it go, man. Just let it go. It's new. It's scary. It feels kind of terrifying. And I don't have the answers to how we do code review, but...
If you look at a lot of teams talking about using AI coding agents in their existing project, everyone's big problem now, code reviews, right? Because everyone using coding agents is producing so many PRs. It's piling up in this review process that has to be done. The new teams that don't have that process in place, they are going multiple times faster right now.
This is the year we almost break the database. Let me explain. Where do agents actually store their stuff? They've got vectors, relational data, conversational history, embeddings, and they're hammering the database at speeds that humans just never have done before. And most teams are duct taping together a Postgres instance, a vector database, maybe Elasticsearch for search. It's a mess.
Well, our friends at Tiger Data looked at this and said, what if the database just understood agents? That's Agentic Postgres. It's Postgres built specifically for AI agents, and it combines three things that usually require three separate systems. Native Model Context Protocol Servers, MCP, Hybrid Search, and Zero Copy Forks. The MCP integration is the clever bit.
Your agents can actually talk directly to the database. They can query data, introspect schemas, execute SQL without you writing fragile glue code. The database essentially becomes a tool your agent can wield safely. Then there's hybrid search. Tiger Data merges vector similarity search with good old keyword search into a SQL query.
No separate vector database, no elastic search cluster, semantic and keyword search in one transaction. One engine. OK. My favorite feature, the forks agents can spawn sub second zero copy database clones for isolated testing. This is not a database they can destroy. It's a fork. It's a copy off of your main production database, if you so choose. We're talking a one terabyte database fort.
In under one second, your agent can run destructive experiments in a sandbox without touching production, and you only pay for the data that actually changes. That's how CopyOnWrite works. All your agent data, vectors, relational tables, time series metrics, conversational history lives in one queryable engine.
It's the elegant simplification that makes you wonder why we've been doing it the hard way for so long. So if you're building with AI agents and you're tired of managing a zoo of data systems, check out our friends at TigerData at TigerData.com. They've got a free trial and a CLI with an MCP server you can download to start experimenting right now. Again, TigerData.com.
What is replacing code review if there's no code review? Is it just nothing?
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Chapter 8: How can developers leverage coding agents for efficiency?
Let's just go. Just trust the model, man. Just trust the model.
And it can surprise you. And I think that still gives me that like dopamine hit that I would have coding. Right. When, when I was coding manually, you know, you'd get a function, right. And you'd be like, ah, it works. Right. And now it's like, you've got like the whole application. Right. And you're like, ah, I just did a prompt for the whole thing works. That's right. Yeah.
It's really exciting. And, and, and, Yeah, it's fun right now. I mean, it's going to keep changing. This is just a bit of a temporary phase we're in now. But I think for many of us building software, we love the craft of it, which you can still do. But also the making a thing is also one of the exciting bits of it. And the world is full of software still.
Like, you think about so many interactions you have with, like, government services or whatever. Not saying that they're going to adopt coding agents particularly quickly, but there is a lot of bad software in the world. And software has been expensive to build. And that's because it's been in high demand. And so I don't think we're going to run out of stuff to build.
I think even if we get 10 times faster or 100 times faster... There's so much useful software and products and things and jobs to be done.
Closest loop for me, then you said SaaS is dead. Or dying. I'm paraphrasing because you didn't say or dying. I'm just going to say or dying. I'll parenthesis. That's my parenthesis. I'll add it to your thing. How is it going to change then? So if we're making software, there's still tons of software to write, but SaaS is dead. What exactly are we making then if it's not SaaS?
I know that not all software is SaaS, but you do build something, a platform, and people buy SaaS. the platform? Is that SaaS? What changes? You mentioned interfaces. Where do you see it going?
I think we're moving. And so this is the next level. The next kind of revelation I had was I started using the CRM. I was like, this is cool. This is super fast. This is better than the other CRM. And I can change it. Cool. I'm doing some important sales work. I'm enriching leads. And then I kind of woke up a few days later and was like, Why am I doing the work? What's going on here?
I create an interface for me to use, right? Why can't Claude Code just do the work that I need to do for me? I know it's not going to be with the same taste that I have, and I know it's going to make mistakes, but I can have 10 of them do it at the same time. And it's not a particularly fun idea, fully automated sales and what that means for the world in general.
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