The Pragmatic Engineer - Mitchell Hashimoto’s new way of writing code

Episode Date: February 25, 2026

Brought to You By:• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—How has the day-to-day workflow of Mitchell Hashimoto changed, thanks to AI tools?Mitchell Hashimoto is one of the most influential infrastructure engineers of our time, and is one of the most pragmatic builders I’ve met. He is the co-founder of HashiCorp and creator of Ghostty. In this episode, we talk about how he got into software engineering, the history of HashiCorp, and the challenges of turning widely used open-source tools into a durable business. We also go into what it’s really like to work with AWS, Azure and GCP as a startup.Mitchell shares how he uses AI these days, and how agents have completely changed how he works. We touch on Ghostty, open source, and what’s changing for software engineers and founders in an AI-native era.—Timestamps(00:00) Intro(02:03) Mitchell’s path into software engineering(07:19) The origins of HashiCorp(15:52) Early cloud computing(18:22) The 2010s startup scene in SF(23:11) Funding HashiCorp(25:23) The Hashi stack(32:33) Why HashiCorp’s business lagged behind its technology(35:28) An early failure in commercialization(38:28) The open-core pivot and path to enterprise profitability(48:08) Taking HashiCorp public(51:58) The near VMware acquisition(59:10) Mitchell’s take on all the cloud providers(1:06:02) AI’s impact on open source(1:07:00) Why Mitchell built Ghostty(1:09:11) Why Mitchell used Zig(1:10:38) How terminals work and Ghostty’s approach(1:17:31) AI’s impact on terminals and libghostty(1:19:13) How Mitchell uses AI(1:22:02) Ghostty’s evolving AI use policy(1:28:36) Why open source must change(1:31:46) The problem of Git in monorepos(1:36:22) What needs to change to work effectively with AI(1:39:57) Mitchell’s hiring practices(1:47:52) Mitchell’s AI adoption journey(1:50:41) Advice to would-be founders(1:52:21) Mitchell’s advising work(1:53:20) What’s changing for software engineers(1:55:03) How Mitchell recharges(1:55:50) Book recommendation—The Pragmatic Engineer deepdives relevant for this episode:• AI Engineering in the real world• The AI Engineering stack• Pressure on commercial open source to make more money – and HashiCorp changing its license• How Linux is built with Greg Kroah-Hartman—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [e-mail]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

If AI agents can write code open-poor requests and ship features, do we even need open source contributors anymore. Michelle Hashimoto, the co-founder of Hashecorp, has been thinking deeply about this, the future of open source, and how to efficiently integrate AI into its data-day workflow. Mitchell built the tools that power modern cloud infrastructure, Terraform, and the Hashy stack. He also created a popular terminal, Ghostie, and I consider him to be one of the most thoughtful voices in the industry on how AI is changing the craft of software engineering. In today's episode we cover, the origin story of Hachicorp, a failed university research project, a notebook of non-sold problems, and an email from his future co-founder that he answered in two minutes. His honest, unfiltered take on working with AWS Azure and Google Cloud as partners, both the arrogance, and also the brilliant engineers who never thought about the business.

How he's adapted to AI coding tools, why he always keeps an agent running in the background, and his practical advice for engineers who have not yet warmed up to AI agents. And many more. If you're interested to hear from one of the most hands-on builders in the industry and want to know where AI tools are useful versus not, then this episode is for you. This episode is presented by Statsig, the Unified Platform for Flags, analytics experiments, and more. Check out the show notes to learn more about them and our other season sponsors, Sonar and WorkOS. Michelle, welcome to the podcast. It's awesome to be here in person. Yeah, it's cool to meet you in person after so many years of following you.

you've had such a massive impact on on the tech industry on software engineers, but how did this start? I think the high level is the same story as a lot of people. Self taught around 12, 13, early teens motivated by video games, same,

like, same as a lot of people. Although I really quickly realized that I liked web. You know, web was new. Google wasn't out yet. I think web was new.

It's like, I kind of like really quick, I never became a video game programmer. I really quickly just became a web programmer, PHP, um, Pearl, that sort of stuff.

And because I was so young, the only way I could learn was through whatever code was published online. And so that's how I got acquainted with open source. I didn't know that's what it was called then, but a kid with no job, no money, parents didn't want to buy, you know, professional books were like, I don't know what they are now, but they were like 50 bucks then, right? And so they were like, no way, right? And also, they didn't believe I was going to read it. And so there was no way they're going to buy that. So, yeah, anything I find online was my... my inn into coding. I'd walk to school every day with a group of friends. There's a period of time

where I printed out the first or second chapter of the Ph.P Manual. I remember it was about 30 to 40 pages of paper, and I never programmed. So all this stuff, and I'm 12, is very confusing. So I read the whole 40 pages every walk to school. And I don't remember how long it took me, but I did that a long time before, you know, I remember this one moment where I was walking to school where suddenly I understood what these dollar sign things were. Like, it like, for whatever reason, it just came in. Those are variables, right? Variables. Yeah, yeah.

And I really understood, you know, I never heard that word before. Like, you don't hear the word variable as a 12 year old out in any context. And finally, at one point, it like hit me that they store things and things could change. And I remember just like weeks of reading this thing and not understanding it, getting to school so excited. It triggered. And then after that, I remember stuff happened really quickly. What kind of stuff did you build? Websites. Yeah, websites. It was gaming related websites. It was like a lot of like game sheet stuff, forum software. Yeah, I mean, I had a lot of fun cloning websites, you know, and it poorly, but like PayPal was out.

And then I really wondered like, how does money get transferred over the internet? How does that work? So I tried to build, like, copies of cloning websites. I did, like, masquerade as an 18-year-old on, like, freelance websites. And so I got, you know, $100 here, $50 here to do, like, image, like, upload stuff. I decided to study computer science in college. Went to University of Washington. I mean, I guess that's when you would call it serious. But I was, like, really, I mean, I was coding every day as much as I could through high school.

Oh, okay. Yeah. that's impressive. Were you alone with this in your friend group? Were there other people doing it? Or was it kind of lonely? It was lonely. It was very lonely.

It was lonely in the real world. And then I quickly found online friends through like MSN Messenger and AOL and Messenger and Sennessinger and forums. I found online friends, which many I have meant now and I still keep in touch, which was cool. But no, I mean, like back then, I mean, being a programmer, one, no one knew that word. But being into computers was like, a social death kiss.

And so even my closest friends didn't know, my best friends and stuff. Like, I hid it from all of them and I didn't talk about it to the school and stuff like that. So it was just a secret until I went to college. And college is when I decided to like let it all out. The big like break that I got was I blogged. And my freshman year, late freshman year heading in this summer after it of college, someone just emailed me out of the blue. And I kind of thought it was a scam.

It was just like, do you want to, you know, it is, do you want to? you know, it is, do you want to be a Ruby on Rails programmer? And I didn't know Ruby. I was a PhD programmer. I had never done Ruby. I'd never done Rails. But I got this email and I'd never been like head hunted before. Like I didn't know what this was. I was also 18. So I didn't really know what to think about it. I probably would have not responded except that the person contacted me was in LA. And so I did respond and we set up a meeting like a real physical meeting and I met them and met the company and realized this is real and they're serious and genuine and I took that job and yeah I mean that was I learned a lot on the job there so that was a

huge change was it a startup a small company something like that no it was a consultancy so it's kind of like one of those standard like it's like 2007 ruby on rails was had blown up it was already very popular and uh there's all these consultancies that that appeared on nowhere that was basically like we'll build your minimum viable product and yeah and we're one of those shops so great job for a college didn't because we'd see a client for like two months and I would build a YouTube style website and then I would build like a philanthropy website and then I'd build an e-commerce website and like it was just like I got to learn all these different technologies and different scale challenges and different like there wasn't all of scale because we're

building MVP's but different like thinking of scale problems um yeah it was it was great how did eventually hashy corp start so what happened between like getting getting this ruby job to a few years later it kind of starts with this Ruby job? There was one guy that worked at the company and he's pretty into his privacy, so I won't share his name, but he was my boss. And there was no Heroku, there was no engineer. So you had to, like, self-host. And Ruby on Rails hosting then was kind of, like, difficult. So he was the guy who got all these projects hosted on dedicated servers. And I didn't know anything about that. And he ran Linux, and he had long black hair, and he, like, didn't use a mouse. And all these

things that were so weird to me. And I was just intrigued. He sat in the corner. He didn't want to talk to anybody. And I just wanted to know more about what that world was. And luckily, despite appearances, he's very nice. And so, yeah, I think as soon as I showed a genuine interest, started asking a lot of questions, he started just giving me challenges. Like, well, the first challenge I remember he did is he unplugged my mouse. It's funny because I don't think there's an era of time where if you did that, it probably would have been some kind of harassment or something. But he literally said, he would unplug my mouse and said, you're never going to work with a mouse again.

So figure it out. I'm not going to tell you how. Just unplug my mouse, restart the computer. Your problem now. And took the mouse away. It took me about a week and I got really good with the keyboard. Harsh lesson. Harsh lesson.

And once I got good with the keyboard, he said, okay, here's, he installed screen on my, early T-Mux. He installed screen in my terminal and said, figure this out. You're going to use this now. You know, there's no questions. Like, you will use this. And, and he just slowly instilled on it, um, on me. And as we got there, then it became, you know, here's SSH.

Here's a package manager. He's like, slowly taught me more and more. And that got me just in, I loved him. Like immediately it was like, this is super cool, super fun. So that long-winded process got me into infrastructure. And then it simultaneously or very shortly afterwards, I joined a research project at the University of Washington called the Seattle project, which is a terrible name. you can't Google it.

But it's called the Seattle Project. It was, I'm sure it doesn't exist anymore. And it was, again, another popular thing during this time was kind of like folding at home. They were trying to generalize folding at home, which is can a bunch of people compute of different, you know, it could be your home machine, it could be an unused rack, it could be in your basement, it could be around the world, but can you donate all this heterogeneous hardware? And then can you generalize a scheduler on top of it so that,

academic institutions across the world could just run workloads. And not just like, not just research. Like the job I got was basically to very vaguely to create, not the scheduler component, but like create the ability to spin up all these nodes and and a bunch of other stuff. It's very vague, but, but it was this infrastructure problem.

And I completely failed at it. Like I tried for a quarter, but from a technical side, I just failed. And I wrote down on his notebook, like what I thought the pieces were missing that I couldn't solve this problem in a quarter in a 10-week period, like why? Well, we need this, we need this, we need this. It's interesting to see how structured Michelle was in his approach

in defining components that would later become parts of the hashy stack. And this leads us nicely to our season's sponsor, WorkOS. One thing I've learned from studying great engineers, Michelle included, is that they're very deliberate about what they choose to build. Great engineers don't just ship fast. They think in systems. They understand leverage, and they're careful about what becomes part of their long-term surface area. If you're building SaaS, especially in AI product, authentication and enterprise identity can quietly turn into a long-term investment.

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there that I never ended up building, but a subset of that was ultimately what Hashikrop would end up building. And I shared this with my undergraduate, like boss, who is Armon, who was my co-founder. Yep. He was my- Later became your co-founder. Yes. He was my boss on the undergrad side. And I shared it with them as kind of an exit interview. Like, this is what it is. And then some period of time passed, not much weeks passed. And he emailed me out of the blue and was like, do you want to do a startup together? You know, you're a teenager and you have no idea what this commitment is. You're like 21 or something at this point. Probably not even

Probably 19 or 20 Yeah And he emailed me out of the blue Just like do you want to do a startup Like person you never met Or you barely met Never met personally Like all this stuff

And it's so funny And he emailed me that at like 1130 Near college I emailed him back in two minutes And said sure And he remembers thinking Wow yours thought it so fast And he's just in

He's ready to go That was sort of the start of our friendship And then And again like There's overlapping pieces here But I was also at the time Working on something called Vagrant and vagrant was, you know, came out of the consultancy,

less the, less the research project. It was solving the problem in this consultancy where we had new clients every two months and we had different teams. How do we create reproducible dev environments so I could go help somebody without a lot of billable hours? So this is a development environment that you could spin up quickly, right? Yeah, yeah, yeah.

The metaphor I always had was, I didn't use Windows then, but the metaphor I always used was how could I double click and open a demo? Yeah, that was a metaphor I used because... It's a good one. Yeah, what the problem we're having was any hour waste in a consultancy that you can't bill is just a waste. And so it was basically like if somebody else is behind schedule, how can I jump in, help implement a feature and jump

out? And we were in that era just setting up the dev environment for a project might take you half a day. And you can build that for the client, right? The client will only pay for the work. Yeah, you can build that for the client. So it would be like four hours of work wasted. And it would probably mess up your dev environment for your actual client because you would be a different Ruby version, a different Rails version. And so you would kind of destroy both ends. And so vagrant came out of that, which was, I just needed to go over there and what ended up became vagran up, sweet, you know, a few minutes. Let's help you for the next two hours. And then, how did you build it back then? Was it some kind of virtual machine or? Yeah, as with virtual box,

Oracle. Well, it wasn't the sun then, but virtual box. And that's, that's another cool constraint, which is that I was a college student, so I had no money. This was expensive back then, right? Virtualization was expensive, a virtual box was free and open source. I don't care about the open source side for that. I was never going to read it,

but yeah, it was free. That was why I did it. And that's why I did that and not like EC2, which did come out by then. But I didn't do EC2 because I don't have money to pay for these instances. So, yeah, that was the constraints.

And I like bringing that up because I think so much of software engineering is understanding, constraints and working with these constraints. In your prior podcast, they were, you know, called forces, like static and dynamic forces. It's that. And I think that helps create better software when you have constraints. And that was my constraints.

So, yeah. So that was, we have vagrant. We have this failed infrastructure project. We have sort of the, my boss of the consultancy getting me in infrastructure and all of the. And then, I mean, externally, we had the cloud being introduced, AWS. I went to school, University of Washington. So, oh. I was right there.

Right in the epicenter. Amazon was next door, right? Amazon was very next door. They donated a bunch of credits right away. I knew about the launch. Most of the CS students at UW interned at Amazon, not necessarily at AWS, but also including AWS,

but all over Armand interned at AWS. And so, like, I was in the bubble of, like, cloud, cloud, cloud, AWS, AWS, when people were pronouncing S3, like S cubed. Like, people didn't know how to pronounce. it, right? That's how new it was. And so yeah, all this stuff came together and kind of led me on the path to build tooling to better manage it. At that moment in time, when you saw Cloud, you know, you saw it was being big, did you know or have a conviction that it would be big or as big as Cloud

had become? Because this was, I'm just trying to put yourself back. Like, this was very, very new back then, right? Totally. Yeah. And I think, you know, like if I imagine, I assume more people would have been skeptics or a thing that is just a fat or whatever. What was it like? Can you bring us back a little bit there? Compared to today, to today, it was very unpolished. I guess as I would describe it. You know, like EC2, I mean, AWS in general is very unreliable. S3 was the only ever reliable piece.

Everything else was was totally unreliable. And there was only a few services. Like EBS didn't even exist when we started. So there was no durable storage besides S3 when I first started with it. It just felt very raw. And I don't, I never really. viewed it as this is going to be big. Eventually, I thought it was going to be big.

What I viewed it as is this is the better way to do it. This feels like the better way to do it. Just, yeah, at a base level. Like, whether this wins or loses in the realm of markets and social, like, popularity, I don't know, but this felt

good. And so that's what kind of pushed me towards it is, and I say this I remember I'm really motivated by like what's the most fun and what, like, feels right. and that it just felt right to me. I think where I started making the bet, me and Armand both started making some kind of bet

was not just when we started Hoshkorp, but we started Hoshgorp on the basis of like multi-cloud. And I really like to like contextualize that at the time we were starting this, which was like 2001 and 2012, which is that AWS was huge. Azure didn't really exist and Google Cloud didn't really exist. There was Google App Engine, right?

It wasn't even cloud. Correct. I used to use that when it was App Engine. Yeah, yeah. And so in that context, as we were pitching these cloud agnostic tools, I mean, we got a lot of raised eyebrows being like, this is a waste of time because AWS is the only player in town. And our conviction was at that point, cloud is going to be huge. And anything that's economically huge, other people want piece of that pie. And so you're not going to just have AWS.

It'll be huge, but you're going to have these others pop up. and Microsoft is not going to sleep on it and Google's not going to sleep on it and who knows who else and who knows and that was our conviction, that was our bet and it mostly played out that way. So when you decided to start Hashid Corp, you had Vagrant,

was the idea to like, you know, invest in commercialized Vagrant and did you go out to raise money or did you start doing it with, you know, bootstrap? How did that go? It wasn't to commercialize Vagrant. So what we had done is Armand and I both worked at this mobile ads company startup. There's like less than 30,

people. And we had built with Python and C these really rough prototypes of these ideas that I had in this notebook of like service discovery and like an early version of Terraform. We called Launchy. We had DNS-based service discovery by

connecting an off-the-shelf DNS server with Postgres and we did all these like hacky things. But they felt good. And again we get like get back to this like how things feel to me to motivate me. Like it felt right directionally right. I graduated. The environment in Seattle was not very startup heavy at the time. Basically, everyone was like, are you going to work for Amazon or are you going to work for Microsoft? Yeah, that was like kind of the end. And like, to a certain extent, Facebook was starting to show up there. But that was it. I knew I wanted to work for startup. So I moved to San Francisco.

So I moved to San Francisco, found a startup that would hire me, which was a mobile ads thing, and just wanted to learn. So that's the short step there. So I ended up in San Francisco. And I convinced Armand was actually going to do a PhD at Berkeley. And he was accepted an inn. And he was just a huge deal. Huge deal. I mean, an incredible program. And so he was going to go there and he would have done amazing things there.

But I convinced him to join this mobile ad startup. He actually took a year to ferment on the PhD. He's like, I'll give it a year. Yeah. I'll join this mobile ad. And I'll go back for Berkeley for sure. If it doesn't work, I'm going to go back. And what ended up happening in that year is now where we get to,

which is that we had this, these, these. this hodgepodge of prototype tools that felt right. And we were going to all these little startup mingling parties. You know, it's like things like GitHub drinkups, but also just like are, this is such a San Francisco thing. And that's why I think it's, even though I don't want to live there again, it was so magical at the time.

Was like across the street was this company that was called Zimrite at the time, ultimately came Lyft. And they invited us over to get drinks and have pizza to demo this new, app with a mustache that like didn't have a name. And so, yeah. Yeah. So like stuff like that. You were there when I was born. Yeah, yeah, yeah. And like that happens all the time, like all the time in San Francisco. And

it's not unique to me at all. Yeah, there's there's a bunch of stories there that I think aren't worth going into. It's just like, it's funds. But I went to all these things and people just talk. They're all a bunch of tech guys. Right. And and you'd be like, what, what are you working on? And there's two things I realized. One is all these companies are cloud first. They're all just adopting AWS first. There was no,

there was no dedicated. This was like in 2011, 2012 or so like they just like went and paid for, pay for cloud, which was brand new, right?

The previous generation just had on-prem. I remember people, but server rooms and, and server admins. They had roles for those, all that jazz. That was just gone.

Gone. Like, that must have been a massive shift. I literally can't think of one social event I went to where there was somebody that had dedicated servers. The only one is very Twitter.

Yeah, yeah, but I think, you know, we probably have, I have to emphasize that this was a massive shift in the industry, right? And it probably was only happening in Silicon Valley or like in. Probably.

Yeah, probably. Well, we'll have it of everyone else. At a scale that was larger than anywhere else. It was probably in Silicon Valley. The joke used to be, because Aduos was so unreliable. The joke used to be that when Aduos went down,

all these startups finally became more cash flow neutral. And they would lose less money. So there would be like a huge, you know, U.S. East outage. And, and everyone would be like, are you going to migrate, region's like no we're saving money right now but yeah getting back to it everyone was cloud first cloudborn cloud native whatever you want to call it and the other thing was they were hitting all the same challenges that we were hitting and they didn't use our tools because they were just like internal prototype tools but but i knew that our tools felt good so i had these two things came together where

i had some ego some hubris where i'm like i'm pretty sure we're building the right thing along with I think the industry is moving in that direction. And like we could come together. And so that led to let's start a company based around that. The fact that I had vagrant was more of like a industry respect. I mean, vagrant wasn't that big then. So that's not saying much. But it was it.

I just had some foundation publicly with to give some credibility to head in this direction. That was about it. And we, we started Hosh Corp. And then what, when you just. decided you incorporated, you know, got the thing. Did you decide to raise money? Because again, back then, I guess it wasn't as common wisdom, you know, why Combinator was probably starting around that time. So like, start of, we're start a big thing or was it a given that, okay, if you start a startup, you're going to raise money? In my social bubble, it was pretty much a given. And, and not just that. So we incorporated, I self-funded, I transferred $20,000 from my savings account into this corporate account, initial funding. And I worked off of that. I paid myself $0 for the first six months. So the $20,000 was purely towards whatever things that company needed.

That was the first six months. And then Armand joined after six months. And we decided to raise. And the motivation there really is there weren't many other options. There were basically three options, as I saw it then, which was bootstrapping, right? Just like build something, make money. and as it becomes affordable, continue to grow, reinvest and grow bootstrapping. VC on the other side.

And then in the middle was like what I called patronage, which was not like, not, not, not Patreon style stuff today. Like that infrastructure didn't exist. There was no subscriber donate type infrastructure then. Patronage was more like you might be able to convince a company like VMware to pay your salary for you to work on some idea. And the best example is Reddit us at VMware. And yeah, and we kind of laid out.

this plan that we wanted to do, which at inception of the company included Terraform, console, it included everything about Vault. Vault came a little bit later. And we looked at that and said, if we bootstrap this, even if we hit it out of the park, this is going to take us like a decade just to like build the software. And that's in the best case scenario. This is just going to be slow. And the problem with slow is that things have a window. And cloud is going so fast that if we were that slow, someone else was going to do it their own way.

I mean, I guess that was the primary issue is we really just wanted to go fast. You knew you needed to. Yeah, we need to hire many engineers right away and start building right away. And so VC was the route, we chose. Can you talk us to the first several products and what they do? You know, we know vagrant, but just for those who are less aware of what became the hashy stack later, right? Yeah, let me see if I could still get these in order. I'm pretty sure I can't.

So as vagrant was predated it. The first product that came out of Hachshkorp itself was a product called Packer. Kind of understated publicly, but kind of underpins a lot of things in the industry to this day. That's an image building tool. So building Amazon images, VMware images, et cetera. I'm not even sure how much, like, publicly came out, but there are whole cloud, like, multi-billion-dollar cloud platforms that all of their official images are, like, the service images are built with Packer.

Everyone was trying to utilize this horizontal scaling, auto-scaling nature of AWS. That was the dream. And if you were, it's kind of like the, what did it, Cold Star problem with serverless today. If you were waiting tens of minutes for your server to be ready, you couldn't react. And so my idea was do that, snapshot the image. And then next time just spin up that image. And so that was Packer. That was Packer.

So Begrant Packer. the next one that came out was console. Console was solving the networking problem and not networking. It was more solving the service discovery problem, which was you have all these machines coming and going. Before, again, like to contextualize it was before you would have a static set of machines that had IPs. And you would probably use DNS or something, but the IPs didn't change that much. So you could be like, oh, my database is here and it's not moving.

But if you're in this world where web servers and load balancers and databases are just breathing, that's how I always describe breathing. Their creation destruction, creation destruction, like constantly, then things are happening at a scale where the service discovery needs to be much faster. And not just faster, but you want to be, have better guarantees that when you get a response that, oh, it's at this IP address, so that IP address is like ready. It's not just, you know, I think this is also kind of more mainstream with like Kubernetes readiness checks and health checks and things like that.

It was bringing that to more like physical server or cloud servers, virtual machines and things like that. And so that was console. Then after that, I think we did Terraform. Terraform spins up infrastructure's code, describe your infrastructure. In AWS parlance, it was things like all the attachments to your machine EBS volumes, gateways, VPCs, subnets, and, like, connecting them all together. Like, the idea was I wanted to have an empty AWS account or any cloud account,

and I wanted to have this text, and I wanted to say make this text reality. And that's what Terraform is. And you would wait, whatever. amount of time it took A to S and you would blink and you would have thousands of resources. And then with one command again, you could just tear it down to zero. That was terraform. So that came out in 2014. So that was the next thing.

And then was Vault. Vault is the easiest to describe this secrets management as core. Secrets management and encryption grew to do a lot more things. So it's like, well, we have like our on your local developer machine. You have you at like your environment variables and doing that at scale at a team level at a, company level at service services needs to access all these stuff securely yeah it was much more focused on the like the the production environment secrets um i had dreams and visions of really solving like developer secret problem but vault really never never did that well michel just

talked about secrets management which turned out to be a pretty important focus here for him in general security is both very valuable but also pretty hard to do well this leads us nicely to our season sponsor, Sonar. Looking at where we are today, we've now moved past tap completion into the era of Agentic AI. Autonomous agents are opening poor requests. One big question. How do we get the speed of AI without inheriting a mountain of risk? Sonar, the makers of Sonar Cube, has a really clear way of framing this, vibe, then verify. The vibe part is about innovation, giving your teams and your AI agents the freedom to build and iterate at high velocity. The verified part is the essential automated guardrail. As agents start contributing more of our code base, independent verification that

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and why the company decided to raise six months after founding. But yeah, it's just basically like we got wordy story secrets and the secrets were not just, I forgot the words I used to describe this, but secrets were not just like passwords, but it was also like PII. So, how do you protect emails and addresses and stuff for your customers? Or credit card numbers? Credit card numbers. So Vault was core to all of that and continues to be. That is a part to build.

Something like that. Yeah, we were really scared when we built that actually because we kind of hid the fact we never lied about it. But nobody on the team that Bill Vault had more than one quarter of security undergraduate security experience. There is no professional security engineers from industry. there was no professional security academics. And yeah, we built it. We got a lot of audits because of that. Like, we were scared.

So we did get a couple. For us, it was very expensive. As a startup, we paid a couple firms, tens of thousands of dollars for vault 0.1 to audit it. We paid two. We got, we shared the early beta with a lot of people who were security experts in order to review it, not publicly, just privately. We got a lot of good feedback. But, yeah, we didn't want that exposed in a sense.

I understand, but I mean, it kind of valid is that you can build good stuff with, I guess, people who might not have the experience, but I guess people were learning, right? Yeah, the security stuff ended up, we know, we really quickly hired professionals that helped the product in, and the security stuff was always pretty solid. But I think what it really showed was what the security industry needed was a shift in user experience, more than a shift in, like, what it did. Because what we were doing was not fundamentally different than existing multi-hundred million billion-dollar companies that already existed. But the experience, the way you interface with it, was dramatically different. And that was, I think, a good example of that, yeah. An apt for vault came. Nomad.

Nomad. Yeah. Nomad, which was our scheduler, which was a couple years late. To the market, yeah. Well, you say scheduler. Was it not an orchestrator? I always described it as scheduling. What did it do? Simple thing.

You have a pool of compute. It finally solved that problem that I had in undergraduate. You have a pool of compute. You have an app that has a certain set of requirements, and it needs to find a place to run it. Yeah, yeah, the underground we talked about. And as you're building out, like, you said, like, some of these took years. Like, how did the business, like, hash a corporate as a business work? Like, did you start to generate some?

There was no business. There was no. So, like, all right, tell me about this one. Yeah, I think we waited too long to develop a business, but for four years, there was, there was actually revenue from a couple of random sources, but there was no real reproducible growing business. So you were just building this vision of, you know, the founder's vision of like, right, we need all these things that would have taken like a decade boost trap. Let's build it. Build in five years and figured out. That was literally it. Yeah, that was literally it.

And, you know, it was, it was all open source. And I always had this mentality, which was, which was like, if the company fails, it doesn't matter. Because if they're good ideas, the open source community will just continue. And so I don't think I would ever tell that to my investors at that time. But, you know, I had this idea, which was like the technology was the most important thing to get out into the world. The business, I really sure hope we could figure it out. But it's not the most important thing. And for those engineers who are thinking of becoming founders or, you know, might be founders, how did this work?

with your investors. You know, when they put in money, like, did they get some boardcies? Did you have to manage expectations? Because I'm hearing, just putting a bit of my business hat on is like, you know, for four years, you're building these cool things. You don't exactly have a business plan. How did that work? Or they just believe that eventually you guys will figure it out. Or they sell some kind of traction with like open source. It's traction. And I don't think what we did was a typical for Silicon Valley. So the really broad hand wavy way I like to describe it is, you know, your seed is about building the product, you don't even know if there's product market fit, you're just guess. Not you're, you're making an educated guess, but you're building something, getting the A, you've sort

proven hints of product market fit, but you definitely don't have it yet. You've proven hints. And then when you get the B, you've proven product market fit, and now you haven't really proven, like, repeatable revenue. You've, you now have hints of revenue, but you know the product is useful. You know people like the product and want to use the product and maybe want to pay for the product, but you don't know exactly how to get everybody to pay for the product. And then CD and so on is just continued to build the repeatable revenue machine. And so with that framework combined, we were on the right track. It was basically like to build the product.

We had clear product market fit by the A in terms of the open source, right? We had millions of downloads, a lot of stars on GitHub, all sorts of signals that showed that this was resonating. We had zero revenue. And so, you know, it was raised money and slowly, slowly get. closer and closer to solving the business problem. And I think we're just a year or too late, or like later than the average startup. But the general keyframes were the same just on the slightly wrong timeline, I guess. And then when you decided to do a business, this was you already had the hashy stack and then you built managed offering, I remember. Yeah, our first foray into

commercialization was a total failure. It was this. Oh, really? Yeah, we had this product that some people, you would have to have been a die-hard, like, Hashigur product fan to know this. But we had this first product that was called Atlas. And the idea was commercially shipping the vision of running all the products. And so that, you know, there's a couple death knells there.

One of them was that you had to run all the products. And so if you were just like a vault user, you had a really impossible time buying or buying into our commercial product. And the second was just that it was just a huge problem to, like, attach on to. regardless of the adoption required, you're trying to solve the problem that multiple different buying organizations in a company were fighting over. So, like, even the people who had adopted all our tools, we ran into the problem of who pays for it. It wasn't the simplest engineering thing for it. Correct.

I think one of the lessons that I would have, you know, I would have for engineers that become founders that don't have a business background. And one of the tough lessons I had to learn is that companies want to pay for software, but they will fight over whose budget owns that. But just are important. Yes, so the budget has to exist. And if it looks like a networking problem, they're going to say, oh, networking should pay for that. So I have more budget to buy my other toys that I want. Or I can hire more people or hire the stuff. Yeah, it could get broken down into like vendor budgets.

It could already be earmarked for external purchase. But yeah, so we have this product that was like the security proof for it, does networking people for it? Does infrastructure pay for it? Like, does dev tooling pay for it? Like, where is this go? And it's just that Spider-Man mean where everyone's pointing at each other. Ultimately, you don't sell anything. And so that was a failure for that reason.

So I don't remember the total time we chased this down, but we had a board meeting for sure on a Friday. And board meetings were usually on Fridays. And we had this board meeting. We were based in the city of San Francisco. Board meetings were an hour south in real Silicon Valley. And it didn't go well. It wasn't.

There was no yelling. There was nobody saying, you guys are, messing up. There was nothing like that. It was just the way I describe it is when your parents aren't happy with you, but they don't have to say that they're not happy with you, but you know they're not happy with you. We had this boring. We drove home. Armand and I complete drive home was silent. And Friday, it's Friday night. So usually what we do is we go straight to, Armand lived in the city and I lived in L.A. already, but we'd go straight back to where Armand's place and just like have a glass of wine, debrief, talk.

through things and we didn't talk on this car right home. Armand drove straight to the office. I didn't question that. We went into the office, sat at a table not much larger than this. The only difference was there would be a whiteboard here. I think one of us at that point said, well, that didn't go well. We both knew it. We didn't feel good. And the sequence events here is now very fuzzy, but at a certain point, we decided, let's play this experiment where if there was no sunk costs, if we were starting from scratch, what would we do differently today? We whiteboarded all this stuff out. What we whiteboarded out was per product, enterprise products, and doing vault first,

and all this stuff. We wrote it out, spent some amount of time there. It's still Friday. It might be Saturday in terms of the time of day, but it's still Friday. I think it was Armand who looked at the board and goes, why don't we just do that? Like, why not? Like, and, and, and I was like, yeah, why not? So we decided over the course of that weekend,

just throw it all the way. Just throw everything we were doing before away. We had two paying customers. We're like, just breach, contract. I don't know, like, figure it out. Like, get out of it. We're done. And we convened an all-hands meeting on Monday,

probably only about 20, 30 people in the company that time, but we convened an all-hands meeting over Zoom. And we might not have used Zoom that, but whatever video chat. And we said, okay, we're switching directions. We are now enterprise as our customer, open core per product. We would have this open source and we would have a forked version internally that had close source features. Yeah.

It was a fork, but yeah. Open core business model. Armand and I thought people would quit. Like, we thought we would lose, like, we didn't have exact number. We thought it would shatter some level of confidence and like, wow, these guys have no idea what they're doing. we didn't have any idea what we're doing and, you know, open core even then had a bit of a

like, icky taste in people's mouth. And so like we thought people would just like philosophically quit being like, no, I came here to work on open source. I'm not going to do open core. Enterprise was kind of just like a suety, boring thing. There was like multiple facets of why people might quit. Nobody quit. The vibes in Slack were amazing, super positive.

Oh, what happened? You think like, why? People are in general? We asked about it in one-on-ones and follow-ups. We asked about it. And it was really like everyone was kind of just like buzzing that we had a clear direction and a conviction. And, you know, there was fear of the unknown. But before there was this feeling of like, we're just throwing darts at the wall and doing this thing.

And we don't know who exactly who our customer is. And there was all this uncertainty in a different way. And now it was like, we don't know if this will work, but at least we're just going to sprint towards this like, there's these clear things, which was like definitely enterprise, definitely open core, definitely vault. Like all these things were set in stone that gave us a different set of certainty that suddenly the company was like, let's go. So yeah, nobody quit. It went super well.

And we started, I don't know the time of year, but it was like in the fall. We built Vault Enterprise by the new year within like the first quarter of trying to do sales. We could just like tell that it was different. It wasn't like obviously successful yet. But just the caliber of conversation we're having. having the distance we're getting in the buying process and the speed we're doing it, it just felt different. And what was different of this approach?

Yeah, I mean, part of it just comes down to like the classic startup, like listen to your customer. And we should have listened from the beginning because our potential customers were screaming at us to do what we ended up doing, which is we would give these pitches about adopt all the products and buy this pie and sky thing. And there were so many meetings where someone would be like, okay, I'll think about that. But how do you replicate your secrets involved? You know, they were just like asked these questions where if you, if I was just listening,

I was so blinded, a lot of us were blinded, but I was so blinded. If I was just listening, I'd be like, wait, a lot of people are asking about secrets replication. And that's a at-scale problem. Maybe we could close source that, right? Like, that's what we ended up doing. That was our first feature with secrets replication, not even across data centers. The first feature was just like a cluster. of vault servers in a single region.

You would sell this more focused product, but now kind of the problem I talked about earlier, security was definitely the buyer. There was an obvious budget, an obvious person you were talking to. There was a feature that it resonated with that scale. And so we were just having much higher quality meetings in terms of getting this done.

Michelle just talked about how Hashikorp managed to build a product that enterprise customers cared about and wanted to buy because it resonated with their scale. This brings us nicely to our presenting partner for the season, Statsig. Statsig offers engineering teams a tooling for experimentation and feature flying that used to require years of internal work to build and is especially important at enterprise scale. Here's what looks in practice.

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That was about the extent of it. Otherwise, they were like, you know, we need support. We need proof of concept of proof it works. We need some white papers in terms of like other customers, scale, blah, blah, blah. And yeah, that's what we had to build up after that and get going. And then so you started selling it vault and then you did it for the other products as well, right? Yeah, we did Terraform and we did console. We had it for all the products, but, you know, all this data is public.

You could look at it and, well, for a period of time it was public. You could look at in like the public reports of when Hosh Group was a public company. you know, it really broke down to all the Terraform. One thing I, I remember is Terraform just became so, so, so popular across the industry. So, like, you know, like there's a hashy stack, but I only layer that all the other parts existed because, like, Terraform just seemed to be everywhere. Why do you think that sudden popularity was? It's so funny to hear that because I accept and know that now, and I feel the same way that you feel now that Terraform is this huge thing. but for the longest time,

like we were the vagrant company. Like all the other tools were, like no one knew the other tools. And not only that, like, terraform, one of the things that kind of frustrating, I haven't heard it recently, but for a period of time,

one of the things that frustrating me was like, oh, they only won because they were first to market. I hear that a lot, and we were like seventh to market. Okay, so like, to market in what category? In terms of that infrastructure's codes, so there were like other like players

who, So many, yeah, yeah. And no one was a clear winner. It was a warring market. But like that first year, 2014, when we came out to Airform, I, you know, at that time, one of my marketing strategies was I was at every conference. I went, I traveled an obscene amount. I was speaking wherever I could. But even if I couldn't speak, I was going just to talk to people. And there was actually a little anecdote here was when the COVID lockdowns happened in March 2020. My wife and I had nothing to do. And I, I, We didn't have kids yet. And we opened up our calendars and we realized that it was a, we had been dating since 2012. And the first time in almost 10 years of our relationship that I will have been in the same place longer than eight days. No.

For almost 10 years, nine years, for nine years straight, I had been somewhere different at least every eight days. That's how much you traveled. That's how much I traveled. Yeah. And I know there's consultants that travel a lot more and stuff. But like, I was traveling a lot. I was coding a lot.

I was like doing all these things. You must have coded it while you traveled as well. All time. Yeah, I had a whole system. When I started traveling, in flight Wi-Fi didn't exist. Yeah,

yeah, exactly. Even now it's kind of patchy. Yeah, so I wrote these scripts that ended up iterating on, but mostly used, where I downloaded all the GitHub issues, and I categorized them, and I would just break it down into tasks

that none took more than 10 to 15 minutes. And I just created this list, and when I was on the plane, I would just one-by-one, bust them, out. There's no internet. Just commit them locally. And then I would get back. And some people used to notice this because I would land and you'd get this push and people would get these email notifications where like 30 issues were closed all at once. But I found the key was pre-planning what

issues you were going to work on. I did that online on the ground. Yeah. And then breaking them down into 15-minute chunks because I found it was really hard to get into like multi-hour, even when I was traveling to like Japan or something. It's really hard to get into like multi-hour flow. on an airplane. So I was like, I'm only going to work on the stuff that isn't like heavy design work. None of that. It's just like bug fixes, right? Like just cleaning stuff up. And so that was my process. In 2021, HashiCorp went public. What is it like to go public both in terms of preparing for it? How did it feel? What changed after? On the prep side, I don't have the full answer because I also stepped down from the executive team about maybe six months before we went public. So I was part of some of the planning. And obviously, I was very aware that we were planning. to go public and but um like for example i wasn't part of the road show or any of that but yeah you know from my seat the the parts that i was part of the parts that have visibility onto i mean it's it takes over a year to do it so there's a lot of prep and and there's some funny things that you do like you do you start running like a public company at least two quarters before you're

public um i don't remember what the drop dead date is but there's a date where you could just like cancel going public. And it's pretty close. It's like very close to when you actually like have that day. So you you kind of run like a public company and to the point where you do mock earnings calls. Like you actually with a conference room table, your investors are the public investors that aren't in the room. They go somewhere else and they talk over the speakerphone and ask you the types of questions. your CFO or VP of finance gives the full report of the quarter. They try to frame the types of questions again.

You run it and you try to figure out whether it's running well, you know, I guess. And that's sort of what the prep feels like. And there's an obscene amount of secrecy because from a regulation standpoint, you can't talk about any of this. And so, I mean, you could look back at even the dumb stuff like Hacker News comments. Like, I just want radio. It's the clearest signal that a company is going to go public because I want radio silent on every topic. because everything became questionable.

I remember there was just a point because there was a hacker news comment I gave like eight months before we went public and our general counsel like in the middle of the night was like you have to believe that. After he talked to me, I was like I could see how that might affect things

but like I didn't realize this matter and I ended up deleting it. And is this because you're not supposed to give public information away or something like that? I don't remember the exact regulation to be honest. Yeah, but there's some regulations. about like not leaking

information. It's not really, I mean, it's all information, but it's more about like, you can't influence the market in any way. And so,

yeah, and you can't make promises because if you say a working go public and might cause even private funding to froth up. And it's a form of fraud. So, yeah,

basically like, I just stopped talking about everything. I don't know how seriously other people take it, but I took it. get to the point where I plan this trip to New York to go public and I invited my parents and I didn't tell my parents why we were going to New York. And I just told them, I want you to New York. It's really, really important. It has to do with HashiCorp. And they were like,

sure. And I can't tell you about it. And they said, sure. And I told them maybe a month in advance. We had a dog. We had to get our dog sat by my aunt. And I just told them we're going on a family vacation up to the point we left. So they like, I didn't tell, nobody except my parents knew basically. None of my friends, nothing, except the friends at work at the company. But yeah, that's what it's like leading up to it. Yeah, I was at Uber when we went public. And previously I read that well before going public, HashiCorp VMware made an offer

earlier. That was like in super early days. That was like two years into the company. We went public like 10 years in the company. Yeah. So when they tried to buy you, like, what was it like? Did you almost sell at some point? Was there any point where you were close to potentially selling? It felt close. And I got a lot of accounts afterwards that it was very close. It came down to like one vote on the VMware board. It was what I heard. About two years in the company, we were only three employees, including me and our month. So we had one employee, I guess.

So there's two founders and employees. Three of us. We got approached by VMware. I didn't know what this would be like. And it is not what it isn't. is they don't show up and say we would like to buy you. No? No. That would be too obvious. The way it happens is you get an email from some low-level business development person

that wants to just like talk vaguely. And the vague talk is they're not interested in mind you. One of the jobs of BD people at large companies is just to have an understanding of the ecosystem. So it's really just like let's have an understanding. They might have had an executive tell him or her to go talk to this company. There might already be an executive

kind of poking around. But yeah, so it kind of starts out that way. It turns into, would you like to come by our offices and meet in person? Oh, our VP of engineering swung by. Let's talk to him. Nice to meet you. A lot. Then I think this is our actual timeline.

And then I think there was a dinner where there was three VMware executives at the dinner. At that point, we thought they might be interested. But it was still so. Wow, so much dancing. Oh, this is not even, this is months before there was in an offer. It was still so social. Like, we drank, we talked about our hobbies and interests and very not about, I mean, very basic about, like, tech. It's really more vibes.

They'd go to dinner. And then it started to get more serious. We spent more time in Palo Alto, the VMware offices, where we started talking about partnerships, about how can VMware help our products more? And it starts about partnerships. and then it turns into like hypothetical if you had the resources of VMware, what would you do? You know, we're like six meetings in at this point.

There's no, no offer of anything. And then at a certain point, honestly, we were getting tired of it because nothing was happening. It sounds like you're a startup and you're going to all these meetings. Oh, and I don't even live in the Bay Area. So I was flying up all the time.

It was a waste of time. And to a lot of founders, that is the warning I give them is M&A becomes a waste of time. So I have another story. I'm an emerging acquisitions. Yeah, emerged acquisitions becomes a waste of time. So I'll tell you another anecdote after this.

But ultimately, we kind of politely had the like, okay, let's like shit or get off the pot kind of conversation. And they put an LOI in front of us, which is a letter of intent, letter of intent. The LOI was, it was one page. You know, it's basically like a non, like semi-binding promise that we're pursuing buying you. no number on there.

It's just like kind of they. Still no number. Yeah. Well, verbally. Yeah. They're not writing anything down. They're not putting anything in email.

None of that. It's just verbal. And so at that point, verbally, we had gotten a drop of $20 million, which doesn't sound that much. Well, yeah,

I'm 23 years old. Oh, yeah. The three of you 23 years old. I'm 23 years old. Me and Armand together own 70% of company. Okay. Yeah. Yeah.

It, you know, it sounds interesting to say the least. What I tell people is, you start, you start thinking about the things you will buy. Is you start, that's the, that's the, it's a dangerous path. That's the, that's what it happens. And we had advice from people who said it's like phenomenally too low, like wildly too low. So go ask much higher. And we asked, I don't remember anymore, but we asked for maybe like 40 or 50 or something. And they just said yes.

They said, okay. And then you know it's like way too low. And that was verbal too. So there was nothing binding about that. Yes. It was just it was, and it wasn't like yes. It was more like,

okay, we'll work on that. You know, but very positive. Yeah, a bit like in this indirect, it's an indirect business sense. Yeah.

And it turned into coming to the CEO of VMware. You know, like clearly they're interested because we're like climbing still. Armand and I, we kind of started getting cold feet because it's, it's, the way we described it. it's a dream killing amount of money.

It's like you would take the money, but you're too small to be important to the company like VMware. So they're going to just... Because even though it's like so much money. Personally, it's so much money. But you know that at the VMware level, I guess you see the revenue,

there, all that. You realize that for them, it's not a big deal. It's meaningless to them. Yeah, it's meaningless. That's crazy. That messes with your mind, you know? Yeah, yeah. So it becomes this thing where it's like, personally, your life could change.

But this thing that we both were truly passionate about, Like the thing I wanted to work on more than anything else would end in a sense because, you know, I would probably get thrown into like working on ESX or something, you know. And you would get a manager of a VM where not even the CEO. The executives make it sound like they're going to do all this stuff with your products. But like that's just one executive and a cog of corporate machinery. So we started getting cold fee being like if they're interested, maybe we're on to something. If we're on to something, we want to sell out early and sell out in a way where our dream dies. that's why I was like a dream killer. Armand very maturely, and he's two years younger than me, so he's 21.

No, he sounds like the older one. Yeah, yeah, yeah, yeah. He's very mature. And Armand very maturely came up with the, I forgot where it comes from, but the risk minimization, not risk, the regret, minimization framework. He was like, what, personally on your own, go think, and I'll do the same. And let's come up with a number that if we walked in the next day and they said, we're killing everything, you're going to go work on ESX for the next four years because we were going to have a walk-up, no matter of it. You're going to work next four years that we would be like,

cool, this was worth it. Like, what's the minimum, no regret, or minimum regret? We came back, and I don't remember exactly where our numbers were, but they were pretty close, and we had to have it been 100. And so we're like, it felt so wrong, like, how could we possibly ask for 100? But we're like, we said this is what we're going to do,

and we stuck to it. So we went back, we asked, for 100. And it wasn't a no. And they, they wasn't a yes. This one had a lot more hesitance. It was a lot more like, we'll get back to you. Right. Like, I don't know. But it wasn't a no. And basically, they came back to us and said, this requires board approval. So we're convening a board meeting next week. Like, unplanned. That's not when they're board meeting. Like, we're convening the VMware board. We're going to vote on this. And then we heard that, uh, the vote didn't pass. That was that. It was that. It's just, crazy how so such small things could you know like influence if that was an extra yes who knows what your story yeah what would have been you you might have you know like it's hard but you know in the where you might have been clogging away on like this yeah yeah yeah i mean we didn't build terraform man so terraform might have not probably probably never would have existed high confidence i know who the vote was i know why they voted that way like i know a lot more

details but it's like i it's it worked out obviously my favorite but yeah so So you've left HashiCorp and you're independent. And one thing about cool about being independent is you're just very honest about stuff. And there was this really interesting thread where on Twitter you wrote about, you said, like, asked me anything about the big cloud providers because at Hachacchikorp, you've worked with all of them. What was your experience back then of, you know, like Azure, AWS, Google Cloud? Like, like you're kind of honest view of how they work back then and possibly like how

has your views changed on them? The precursor of that is while I was at Hoshkorp, I always said to be very careful about what I said about any of the cloud fighters because we're partners with all of them. We're partners. And I didn't want to insult anyone. And so I was just very professional about all their relationships. And then like... We like all of them. Or just say nothing. Or just say nothing nice to say. Don't say anything at all. And then I left and I was still... I kept that up because it was too close. I was still flying too close to the sun, as they say. And they didn't have time pass where I was like, yeah. Like, in my opinion doesn't really matter. And yeah, so my, to answer your question, my broad view of all of them was that AWS was really arrogant, annoyingly arrogant was how I'd describe it. And then when you say arrogant, like, can you help us understand, like, how you work with them or what part of them or like, is it just general? Yeah, I'll start disclaiming this, though, that, you know,

we worked with so many people there, that there were individuals, and all of them who were awesome and nice and kind. So I'm not trying to make like individual judgments here. It was just more of like how all of it came together and how it felt as a whole. So by arrogant, I mean, it always felt like they were doing us a favor at every turn in terms of partnerships, in terms of just getting a meeting with them. It always felt like you should be thankful that we're spending time talking to you. And not just that, but also like there was always this subtle vibe of like, we will just spin up a product and kill your company. You know, it felt that.

No one ever said that. well it kind of got to a point where it was sort of like if we don't come to terms we're going to build this service it did kind of come to that but you know we did see that later on with elastic and oh that had already happened that oh it happened already yeah just not with us but with open search yeah and they always publicly spun it as like oh it's so great and builds the builds the ecosystem larger and we're doing it by the letter of the license and you know all has truth elements to it but it's still not a nice thing no I I think

I don't think people paying an digital open source appreciated what Amazon did with with a lot. It really hurt Elastic's business and it showed how open source can weaponize against a company that spends,

you know, their blood, sweat and tears. And I guess, you know, Hashocorp, you had the same thing, right?

Because you were publishing permissive. Well, I mean, open source needs to be permissive. So it was MIT or MPL license. Yeah. So like Amazon could have spun up any, anything they wanted.

Yeah. There was like a two-year period where I think for the entire two years, the entire leadership team was terrified that at any moment there would be like a vault service or something would pop up. And so yeah, that's sort of my characterization of AWS. It really took like, for example, teeth ringing to get them to help with the AWS Terraform provider. We had, I don't remember the exact number, but we had something like five full-time engineers employed working on only the AWS provider for Terraform, which, you know,

maths out full benefits and everything to like a million dollars a year. And all of that was pure open source, pure integration with a commercial entity. And they were not helping us at all. And they were the last of any of the cloud providers to provide any sort of help there. And it came down to some drama where we went to a meeting and basically said, so we're going to publicly say that the ADBF provider is deprecated. and we're done. Like the community could pick it up or whatever,

but we're not, we're gonna. Yeah, because you didn't get any help from that one. Yeah, and it's taking up to its work and there's too many bugs and you're shipping. Honestly, AWS is shipping features too fast and like, it's just like not worth it.

And that freaked them out. And finally they start helping, you know, they might recount their side of things differently, but that's pretty much, it felt like no movement for years. And we said that and movement started happening really fast. So yeah,

there was that. Microsoft, I have the most positive view on Microsoft. They had a really hairy technical product, is how I describe it. It was very difficult to use. Azure. And a lot of nouns,

like principles. And I still to this day, and I've integrated with the service, don't fully understand the IAM hierarchy of Azure. I just kind of bolted it and got it working with a team. And that was that. But technically kind of, but from the business side,

super competent, professionals and team players. It was like how I describe it. We went into every meeting with them and a lot of our meetings the first question was, how do we both win? That was like the first question.

And yeah, very pleasant, awesome. They were the first people to jump on board supporting Terraform. Sure, that's some kind of bias, but like they were consistent throughout the years. So positive on Microsoft. And Google Cloud, you know, my, yeah, Google Cloud in general,

it was always like the best technology, the most incredible technology and architectural thinking. And I swear none of them, it felt like none of them cared or thought about the business at all. It was like every partnership meeting, we'd spend hours talking about the coolest edge cases and scalability and how this is going to work. And like, I think the best public example

that you could just see in history was they were the only company that when they, They partnered with us to write the provider. They spent a lot of time building this very good. I think they called it magic something. They fully automated the whole thing. So when they shipped a new Google Cloud thing, it had a Terraform provider resource right away.

And not just like, it didn't feel automated. It felt very ergonomic and like it was good. It was really good. And so they had that. But whenever we would get into, how do we do co-sale? How do we like attribute your sales engineers, to selling like infrastructure that's spun up by terraform like how do like how do we do this so like the business side of things rickets like impossible to get anyone not just impossible it's like even if you

got someone they would say something for 20 minutes and be like okay cool we have two more hours let's figure this other thing out and yeah it that's what it felt like so and the other disclaimer i give is all this knowledge was circa i don't know 2019 something like that so maybe in the past seven years things have dramatically changed, but that's what it felt like. Yeah. Going to open source, you're actively involved in open source and open source today. And it seems open source is changing a lot, especially with AI and you're seeing stuff at ghosty. Like can you tell us like how open source has changed with ghosty, with the AI contributions? And what are you seeing with open source maintainers?

Seems like there's a bit of a, you know, like drama or worrying stuff happening. Well, I would say more broadly, the issue facing open source today is, I mean, there's multiple, but the one that I feel is most prevalent across industries right now is AI contributions. And specifically the ratio, the signal to noise ratio being incredibly low. In other words, just being super noisy with low quality contributions. It's just stressing the system quite considerably. And so after you left Hashikorp, you started ghosty. How many years ago was that?

Was that like two years or so? Well, I left Hashigorp over two years ago. I had like poked around with prototypes of Ghosty like maybe three years ago. But after I left Haschikorp, I started just like kind of working on it like 20 hours, like much more just because it was the thing that I had. What drew you to, Gossi? What was your kind of vision and why you started working on it? It's a better.

terminal, right? It's a terminal. Better as subjective. Well, I insult it because I like it better, but yes, a terminal and opinionated terminal, right? Opinionated, very modern in terms of like supporting as many of the newer specs as possible that enable functionality like displaying images or, you know, clicking on your prompt to move the cursor and, but like dozens more examples like that. The original thing that drew me to it is the exact. opposite of good advice that people usually give to people, which is that you find the problem

and you build a solution. And what I did, and you picked the best technology that then solved that. What I did was I found a set of technologies and I was like, what could I build with these technologies? I went the opposite direction. And I had spent over 10 years, 12 years at Hosh Corp, incorporated, and three years prior to that, doing infrastructure open source. So 15 years in total, just thinking almost all the time about infrastructure and cloud services and things like that. And so I had felt that I was rusty. I had sort of like my skills have had weakened on desktop software systems programming to a certain extent because I was so constrained by networking challenges distributed systems. So like low level systems programming had had had atrophied.

I had never really worked with GPUs and GPUs, I guess crypto was happening, but I kind of ignored that whole trend. But this is pre-AI. So, but GPUs were obviously in use. And I just felt like I had no idea how they work. So I wanted to go to desktop. So I picked all these like different technologies and I said okay Zig because it looked cool to me. I just wanted to try it. Can't for those of us I'm not into Zig? I heard good things about it. Can you explain why Zig is so interesting, innovative and why does it grab so many devs attention? I don't know why it grabs other people's attention but for me it was it just felt like the the best better see that I saw out there and I am someone that's coming from the position where I actually

enjoyed writing C. So a better C sounds great to me. To me, it's not very annoying in terms of like, if I want to blow my own foot off, please let me blow my own foot off. You know, a bunch of qualities came together where I thought on the surface it looked cool, but it's very hard to judge a program language on the surface. So I wanted to build something with it. And so yeah, I pick Zig, GPUs, desktop software. What could I build? For all my time at Hachshikorpe, I built CLEs. And I was like, well, I live in a terminal. Like, what does it take? I don't, I live in a terminal and yet I understand very little about a terminal.

So why don't I just, like, build a toy project that's a terminal. And that's how it started. And as with auto stuff, I find that once you dig beneath the layer of taking something for granted, you realize that everything is way more nuanced and complicated than you imagined it to be. And terminals were the same way as once I dug beneath the surface, I realize how much they were doing, how brittle some things were. how much better certain things could be. And I got sucked in to being like, I want to do this better.

Okay. For like someone who's a dev, you know, I use terminals as well. I'm going to ask the stupid question. How hard could it be? What does a terminal actually do? And then can you maybe tell us like how ghosty is structured or like what are the things that it needs to do? Just give a little empathy of like all the work that you're doing.

Yeah, yeah, yeah. I actually get that a lot. I get that question a lot. So it's definitely not a dumb question. It's really like. It gets asked less now, but a lot of people were like, I thought they were done. It's usually the most feedback, I guess. Like, what is there to do in a terminal?

So at a basic level, they don't do a lot. The problem is that the functionality is grown significantly of what terminal developers want to do. But let me just give what they do. It's kind of like an application development platform, right? It's like a, it's not an operating system. You're not dealing with like hardware level problems, but it is like an application sandbox on top of that. And that other applications run within it and need to rent. render text. They need to render colors and images and widgets and mouse events and all this

stuff. The best description is it's like it's like a browser but for text content. And so all of the complexities that a browser has, a terminal has similar ones, a smaller scale, but similar ones. And if you try to extend what a terminal is capable of, then it gets, you know, you start bringing more and more problems. Like as soon as you brought images into a terminal, you've introduced like a whole new ecosystem of problems. But the tongue and cheek answer I like to give to GOSI's complexity is that it's 30% of terminal and 70% of font renderer. And yeah, that's what it feels like. It's really like a problem of, you know, that terminal screen you see, whether it's GP or CPU rendered, that terminal screen you see is like you're drawing on a canvas. So you are building a renderer

for text in there. Everything kind of bubbles from there. So from a rough architecture standpoint of ghosty. I like bringing it down in terms of threads because if G0xie is multi-threaded, most terminals are not, but I'm not saying that as a positive point. It's just a good way to describe the architecture. We have a central UI thread, which just draws the windows and stuff. That's pretty standard for desktop software. And then we have an I.O. thread, which runs the actual shell that you're seeing. So any bytes that we send or it sends back to us, it's processed by the I.O. thread. And then we have a renderer thread, which is actually drawing it. So it's, it's the best way to think of it is it's on a V-Cense.

clock through 30, 60, 120 frames per second is it's just sampling what the terminal state is and then drawing it. And the render itself uses a font subsystem on the same thread. But we have to take the fact that this grid has this character at these sets of characters and map them into fonts and do that all on our own. A lot of people think, oh, doesn't the operating system solve that for you? But they don't, unless you're much higher level. Like, you know, you can't just draw easily, you know, monospace text in that way. you have to really put pieces together. That's the big picture.

It's quite simple at that level, and then just extend all the functionality the terminals have into that. So you're kind of like building a 2D graphics engine a little bit that has like very focused on fonts. Yeah, yeah. It's from a renderer side, it's very simple. The render is actually not that complicated

and I won't work complicated. The hardest part is actually maintaining the terminal state. So the way terminals work is they're a grid of monospace cell. So you'll have like 80 by 24, 80 columns, 24 rows. And there's commands that the program could send to move the cursor or say, if I like to say, think of it like a paintbrush. I could say, make the paintbrush red and bold. And everything after that is red and bold.

And now change it. And you're just maintaining the state and drawing around. And then there's all the scroll back, right? People are used to in terminals going back. And that's where the challenge is doing that in a fast performance way. And that's what I try to do with go see. I mean, I show this. There's so many benchmarks.

run. But one of the most obvious ones that shows the speed, which also gets a lot of criticism, is just catting, reading a large file. If you just, like, dump a bunch of text, how fast can get through it? And you'll see a stark difference between modern terminals. I'm not just going to say ghosty here. Like, if you take ghosty kitty, um, alacrity, um, any of these newer terminals, they're all going to do great compared to terminal a app on macOS, um, or traditional, like, Linux terminals. The criticism is why does that matter? And you know, the easy answer is when you

accidentally cat a file, like a lot of people will force close. The creator of Redis posted a great comment for me, a great comment on Hacker News about why he loves ghosty, which is that he previously used to tail production Redis logs and, you know, just spews logs out.

And he used to have to send them to an intermediary file and then read them out later. So he could render it. So he could render it and actually work with it and he doesn't have to do that anymore. Because Ghosties fast enough that he could just let it dump while he's going through it, parsing it, like mentally parsing it, things like that. And that just saves him time. And yeah, so. There's something to be said at some point we should probably talk more about

the fact that a lot of software these days does not care about performance. I think it's refreshing to actually have examples. And I hope we will at some point maybe get back to it. You know, we'll talk about AI, it might not help. But there's a level of craftmanship, right? just like not wasting resources or being efficient or I think we all like I see in my day to day life like we have more powerful resources laptops phones and they're not getting any faster and it's just frustrating at times it's kind of like the love of the game I mean a lot of a lot of ghosty is just the love of the game like like I like to say like our render because because like a disclaimed before like it's not complicated I'm not I'm not ever going to say that ghosty is like a 2D game

because a 2D game from a rendering standpoint is much more complicated. But I do care a lot about the render, and we got our renderer down to, for a full screen on my Mac, set of grids, each frame updates in roughly, I don't know, it's like, it's something like nine microseconds or something.

That doesn't include the draw time. That's just like taking the state and submitting work to the GPU. It's about nine microseconds, and the GPU takes some time. 120 hertz, 120 frame per second, frame is 8,33 microseconds. So if you have nine, you know, again, we don't have the number of how long the GPU takes,

but it's super, it doesn't take much time at all. You're leaving a lot of options and work for. What I'm saying is like we could have made it 2,000 microseconds, and it wouldn't have mattered. Like, you would, you would still get that performance, but that's not fun. Like, I want to make it sub 10. I like it the fun. Yeah.

So we spent a lot of time just like, it was a big, I blogged about it. It was this thing where we got it down from, it used to be about 800 microseconds and got it down to like nine. And I thought that was awesome, even though for end users, it doesn't make a difference. But as you say, the craft and the level of the game.

So when you started out building, go see that. That was around the time where I think Chad GPT was out. There were some tools. How did your tool set change in terms of how you're developing day-to-day? There's two sides to that. So one, AI gave a huge boost of terminals,

which is a funny thing. Like, like... Oh, how so? The number, because of Claude Code and all these things, the amount of time spent in a terminal has gone up, which if you told me in 2023, terminal usage would go up. I would say, no, it's not going to go up.

I had no disillusions that I was going to, like, save terminals. And I didn't, right? Like, AI came out and came out all these CLI tools. And even when you're seeing, like, Codex apps and Clod apps, like, is leaving the terminal, they're still executing so many things in a pseudo terminal. The number of terminals out there is massively larger than there was in 2023, which is hilarious. Oh, wow.

Yeah. So random. Super random. And so that's part of why one of the things I'm doing with Ghostie is extracting. It's actually extracted already what I've called Lib Ghostie, which is everyone reinvents this very small surface area of a terminal. And because they do it, it breaks, like all sorts of things break. Like if you run a Docker build or push to a platform like Heroku and you do enough, weird things in the terminal that aren't actually that weird, just like draw progress bar.

It renders it like chaos. All over the place. All over the place. Yeah. And it's just because they've poorly implemented a tiny subset of a terminal because they're more complicated than people think. And so LibGo C is this minimal zero dependency library that people can embed terminals anywhere. Oh, cool.

And yeah, yeah, MIT license and just it's really like I'm tired of seeing broken terminals everywhere. So please use this. So, okay, that's the one angle, really funny. But the other angle is actually AI usage. It's hard to say I'm a big fan. but within the right categories of things. Like I think that it's a revolutionary tool and I get a lot of joy using it.

Yeah, I use it every day. I use tools like ClaudeCode and AMP and Codex and the chat tools like every day for some aspect of my life. And it's really allowed me to choose what I want to actually think about. Right. I think that's the most important thing is that I always felt limited in terms of, oh, I'm going to have to spend the next two hours I don't know, doing this boilerplate, annoying stuff, and that I don't want to learn about.

But now I don't have to learn about it, which is, yeah, I'm not, I'm not like getting skill formation in that category. But I could now spend those two hours doing something else. And that's the best to me. In your workflow, do you just use a single agent? Do you use multiple agents? Have you experimented with them? I've tried a bit of everything. I would say my standard workflow.

What I try to do is I try, I endeavor. to always have an agent doing something at all times. Maybe not when I sleep. I don't go that far. A lot of people do go that far. I don't go that far. But while I'm working, I basically say I want an agent.

If I'm coding, I want an agent planning. If they're coding, I want to be reviewing or, you know, that there should always be an agent doing something. So you have a separate tab? Yeah, separate tab. And sometimes it's multiple.

I don't, there's a lot of work that I do around cleaning up what agents do and I don't run like Gastown-esque like things and so I'm the mayor so to speak and so I don't want to run too many I don't find it that fun to clean their stuff up but periodically I'll run two

in competition with each other because it's a harder task and I don't have a high confidence they're going to just like crush it they'll just run Claude versus Codex or something like that or I'll have one coding I'll have one doing like some sort of research task I absolutely love them for research. That's awesome.

And then I'll be doing something else. But no more than two, I would say. The code that they generate, do you always review it or have you kind of got a bit more loose and some people swear on having a closing the loop, having validation for it? Or are you like still like, I want to see the exact code and I'll review if it's correct and what I expected. Matters what I'm working on. And if it's ghosty, I'm reviewing everything that's going into it. If it's like I set up a personal wedding website from one of my family members, I don't care at all what the code looks like.

Did it render right in the three browsers that I tried? Yes. Did it render right on my phone? Yes. Don't care what they could. Like, doesn't make any network work because no, has no secrets access. I don't care. Like, ship it.

It's the only going to be online for two months, so ship it. Yeah. And then how did the AI policy at ghosty change? I remember that maybe a year ago or so you asked for disclosures if someone is using it. And just very recently, you kind of crammed, crack down and said, like, all right, no more. Yeah, we're going to change again. Well, I'm not going to change. We're going to iterate.

So, yeah, a year ago, started asking for disclosure. And people, you know, the very fair question there is, what does it matter how the code is produced? And the reason to me it always mattered was because it dictates how much effort I go into fixing it. because if you produce the code of AI and you did it really quickly, then I'm not going to spend hours fixing up your code. You spend your time. Yeah, because you know that that person that puts much time, it is not much human time.

You're trying to mirror it, right? It's ever for effort. If you put in hours, I'm going to put in hours back, and I'm going to help you. But if you put in a few minutes and never read anything and threw it over the wall, then I should be able to read it in a few minutes say, no, thank you, and close it. it's fair and I need to better understand what that is. And, you know, it's not about bad code because open source has always gotten bad

code contributions. But the difference before is usually those bad code contributions came from people that were genuinely trying their best and put in a lot of effort just to get to that bad code point. And so I, I, people behave differently. I would always try to reciprocate by being like, this is someone very junior or this is someone just new to the project and I would try to educate them. be like, okay, we should do this better and give these careful reviews. But if it's bad code that there was low effort, I'm not going to give a careful review.

So again, like, I wanted to know these things. And the disclosure worked decently well. The issue wasn't the disclosure. The issue was that the quantity of low quality AI PRs that we were getting reached a point where it was too high. Like, do you know why that might have happened? more people instructed agents to contribute a PR or to fix an issue they had? Do you have theories or actually like seen evidence of why this happened? I have theories and I've seen some evidence.

But yeah, I mean, I think obviously there's the rise of just AI usage in general. But the real trend at a step change that I saw at a certain point, and I don't know when it happened because I don't use agents in this way, but at a certain point, they started opening PRs. You know, before it was like you generate code and maybe they'd commit and stuff, but you would still like, push it to a branch and then open the pull request. At a certain point, they started opening PRs. And there was a dead giveaway at AI because at least to this day, to the point we're recording us, the way Claude opens a PR is it opens a draft with no body and then it edits a body later and then reopens it for review.

Which is not how human would do it. Oh, like one human a year would do that. And now it's happening three times a day. And so even if they're not disclosing AI or they're hiding it, it's like, oh, and it happened to the speed that's unrealistic. It opened. The body came in less than a minute later and it opened less than a minute later. Like, pure AI. I just tweeted out of this a couple days ago, which is just like I wish that these agenetic tools would put a pause on opening PRs for a second because I think that's the point where it's really causing a lot of friction.

How did you change the policy? Are you considering closing down PRs? You mentioned that recently, that you've, the thought crossed your mind. I would say I was crashing out in that moment. But I, but kind of. So we shipped this policy update where PR's written by AI are no longer allowed anymore unless they're associated with an accepted feature request. So you can't just drive by and be like, I did this thing that I've never talked to about. Here you go.

And we, we get about two or three of those a day. And so we just close those thing. I don't even, I literally don't even read the content. I could see it to AI. I could see there's no fixes issue number. I just close it. No idea if the code is good. Don't care.

It's just policy. Don't have time for that. That's pretty much where we landed on currently. And we're recording this in the middle of another transition, which I already have the PR open, where we're going to switch to a explicit vouching system for the community. So you're no longer able to open a PR at all. AI or not,

anymore, which is I think the people who criticize where it came from doesn't matter. It doesn't matter anymore. Now, all that matters is that another community member has vouched for you. And if they've vouched for you, you're added to a list where forever or indefinitely, you could open a PR.

If you behave badly, then you, the person who invited you and the entire tree of people they ever invited are blocked forever for the repo. This reminds you a little bit of, do you know, the social lobsters? Lopsters. Yes, that's what's based off of. So the ideas that you're putting your own reputation on the line by vouching for somebody else.

I'm a reasonable person. If this happens, then I are one of our maintainers of community made a mistake. If you just like hop in the Discord or email and seem like a reasonable apologetic person, like I'm not going to spend a lot of time like there's not going to be like a, I don't know, a mock like court type session. I'm just going to be like, okay, I'll give you no chance. So yeah, we're sort of moving to that system. I think one thing that's a little bit different is, I should say that this is one inspired by lobsters, but specifically in the AI space

is inspired by this project called Pi. They do this. Well, they do, they... Quality is built on pie. It's a self-improving... Build your own agent toolkit. So, you know,

kind of ironically, it's an AI tool, but they care a lot about code quality and anti-slop and things like that. So they have a similar mechanism, a little bit less of the tree and some of it. But a similar, you can't open a PR unless you're vouched for.

And the other difference here that we're going with is, in addition to vouching where you could positively mark someone, you could actually denounce users. So if there's a bad actor, you could actually ban them. Not just like you can't even attempt to contribute again. And that's just a, yeah, we had one yesterday where don't open PR, we closed it because it violated. It had no associated issue. And it was AI. And then they just reopened it. Like not the same one.

They resubmitted a new branch and reopened it. like less than 10 minutes later. I was like, oh my gosh. So stuff like that is just, the problem is it's just wasting time. It feels like most of open source will have to change because of AI, right? Like it's, you probably know,

more maintainers, but I hear this, your story is not the only one, you know, like the project closed down PRs. GitHub is, I think, just shipping a feature that projects can automatically close or reject PRs. Yeah, I think open source will have to change

in a lot of ways. I mean, I think, I forgot who wrote this, but, you know, one of the logical extremes is if agents are so good, you don't need open source anymore because you can just build it. Right. That's the extreme. I don't describe that extreme, but that's one of the extremes. The issue is there used to just be this natural back pressure in terms of effort required to submit a change. And that was enough. And now that that has been eliminated by AI.

It's, I like the wording that Pai uses, which is that AI makes it trivial to create plausible, looking but incorrect and low quality contributions. And that's the, that's the fundamental issue. You know, open source to a certain extent has always been a system of reputation, right? Like, you, you earn some trust and you get more access that, you know, and that's how it's supposed to work. But yeah, it's been that reputation system has been taken advantage of in a certain sense with AI, or the default allow PRs has, you know, has been taken advantage of. And so I think like this vouching system that we're proposing for my project. I think it's like very true to what open source is, which is that open source has always been a system of trust before we've had a default trust,

and now it's just a default deny and you must get trust by somebody. Do you think we might see a lot more forking happening though? I hope so. I hope so. Because until now, forking used to be a, you know, like a fork off a little bit because it was a lot of effort. It wasn't to keep up. Like it it never seemed viable to fork a proper project, right? Yeah, and I, okay, I am separate from AI and everything. I have always been a huge proponent, or I guess in the past few years, I've been a huge public proponent of, there should be a lot more forks, like a lot more forks, because open source, I think,

one of the reasons maintainers have been taken advantage of to some extent is that contributors have some sort of entitlement, you know, whether it's toxic entitlement or not, but there's some sort of entitlement, which is I've made a valuable change. So you should, and it's clean, and it works great, so you should accept it.

But you really don't have to. Like, you absolutely don't have to. And then I've seen this time and time again where you have a high-quality PR, like perfect PR, but you say no. And there's anger in the community. But the thing is,

I've said this since 10 years ago on the Hoshkrip days, hitting the merge button is the easiest step. Getting to and hitting the merge button is the easiest step. like undergraduates should be able to do that. It's after that, it's the years of maintaining whatever you just merged within the context of your roadmap, the bugs, customer needs, all that stuff.

Like, that's the hard part. Like, you're signing up to keeping this forever. It's very hard to remove features. So anything, remove anything. So the core privilege you get with open source, like OSI open source is forking. And you should take that that's, that's the right you got. You should fork it and maintain your own software. Yeah.

One interesting impact of AI, as someone tweeted about how there's a rumor that big tech is looking into rearchitecting their mono repos because of agentic tooling, AI tooling just a lot more code being turned out. What's actually happening? What's the problem with Git? The problem with Git, I mean, I think there's a lot of problems with Git. But the mono repo problem with Git is that Git is relatively bad at very large repositories because you pretty much have to clone the entire repository. There's some extensions to like fix up, but like official mainline Git can't really do. that, right? And so for very large changes, the very large repositories, it's sort of annoying to maintain. And then if you have a lot of churn in it, it's very hard to get changes into whatever your

trunk is, your main, your master branch, right? You constantly have to rebase. Merge Q solves that to a certain extent. I think merge Q's works for humans at a certain scale, but the merge Q's could get quite deep. But then if you sort of 10x that, like conservatively, I think 10x that. And then if you, buy into like hype cycles and you 100 or 1000 X that I think it gets completely untenable in terms of how are you ever getting any semblance of

cohesiveness onto the main branch quickly and and so yeah I think there's a confluence of problems there which is the merge queue problem the disk space problem the like branching review type problem oh I also

treat the other another time where like Git has this you branch and you push up your branches, but the branches are only the positive. Like, when you, when you close a PR and you don't accept it, like you pretty much are the branch. GitHub, you could reaccess close PRs, but a lot of people don't even get to the PR stage.

They experiment, they're like, oh, this isn't the right way, and they never push the branch. And that's, like, relatively important information, relatively important. It's not as important as the positive, but I think there should be a lot more branches and get a lot more information that we just never throw.

away. Like we're at, to me, we're sort of at the like Gmail moment for email for version control where like you used to really have to like curate, delete all this email and then Gmail came out, gave a gig away for free to everybody. We never has to think about it. Their tagline or something was like never deleted email. I remember seeing that in some set of marketing. It's like archive it, right? Never delete it. And that's where I feel like we should be out with code, which is like just this huge repos, a lot of context. We need better tooling in order to find relevant context. in that Git repo or version controlled repo.

I would say that the real, you ask for like real examples, I do advise a company that's currently stealth but working in this space. And the real examples is driven by the highly agenda of companies. The companies are like going really all in and drinking the Kool-Aid. And they're struggling in terms of the amount of churn that these agents is causing is so much greater than humans. And it's not an AI review problem or anything. it's really just like a release problem, like managing the merge cues,

humans getting access to the right set of data in the repository and things like that. So are our performance problems, mainly with Git or just like even the workflow of? Yeah, yeah, all of it. Performance for sure, but workflow, yeah. I mean, like every time you pull, you're, you can't push because every time you pull,

there's another chain. Like, every time you push, it's rejected. Yeah, there's a lot of parallel work happening as well. Do you think Git will be around with the judge in the few years? Who knows, but what's interesting is this is the first time in like 12 to 15 years that anyone is even asking that question without laughing. We're not laughing. Right.

Like, if you, five years ago, you said, we'll get be around in five years, you'd be like, yeah, of course it'll be around. Like, that's crazy to think, right? But now people could ask that question. And of course, some people laugh. But like, there are people that critically think that it might not be around in five years. Well, I think you do want to save the prompt history because often reading the prompt is actually, if it's a bunch of a code generated, the poor request is meaningless.

Changes will have, like Git and GitHub, forges in their current form, do not work with agenetic infrastructure today. And it's nascent today. So, yeah, change will happen.

And I'm not exactly sure. And that's not something I'm trying to change myself. But it, you know, I'm on the receiving end in terms of an agent user and a maintainer where I'm like, this is working. What other engineering practices that,

you know, we have been relatively stable for like 10, 20 or a bit more years, you think have to change or are looking to change, thinking things like CICD, testing, code review, other, you know, ways of Yeah. You know, AMP has the saying, which is, is, it's kind of clickbaity, but it's so true as everything is changing. And this is the first time, really where it feels like, it is the first time in my, you know, relatively short to other people but still like 20 year professional career that so much is on the table for change at one time and I'm an optimist so it's really exciting to me it's a lot of fun but it's we've never seen so much editor mobility. Editors used to be one of those things that once someone picks an editor it's very hard to get them off that editor they're like stuck the level of editor mobility in the past few years between like vS code and cursor and just jumping around is is unreal so there's a bunch of mobility there. in terms of, I mean, Cursor itself is a great example of a company that reached an insane valuation

that you could never have gotten pre-A-I on an editor product. So editor forges, CICD for sure. And I think that testing in general, because to make an agent better, it needs to be able to validate its work. And so tests go from, even the best test case scenarios don't have like, I mean, the best, I guess, have full coverage. But that's a very extreme. The very good test case scenarios, just test, like, one of the, you know, one of the best cases. the edge cases and one of the happy cases and, you know, bad case, and they just kind of go through and if it passes, it's probably good paired with a human who's thought about the problem. But

AI is more goal oriented in terms of I want this feature to work this way that if it doesn't see a spec somewhere or a test somewhere that other things should work in a different way, it'll just break it on its path to its own goal. And so I've heard this call a lot of things. I mean, the one I like the most is like kind of like harness engineering, which is like, harness engineering. Yeah, it's like, and I've been, one of my like goals for this calendar year has been to spend more time doing that, which is that anytime you see AI do a bad thing, try to build tooling that could have called out to to have prevented that bad thing or course corrected that bad thing. And so it's sort of like moving from the product to working on the harness for the product

or product development. And so, yeah, there's, there's a lot of that where I think testing has to change to be far more expansive, but CICD is not set up just resource performance-wise to be able to do stuff like that. So yeah, I'm not sure how it changed, but that's going to change too. So everything is on the table. It's really interesting. Yeah. And it's a lot of tools to be built. One other thing, observability. Yeah. And then, and I guess on that same topic, I mean, of the volume and scale and observability, it's also like the sandbox. Like, I didn't think, even being in infrastructure and being heavily into infrastructure,

containers blew up the amount of minimal compute units we had, like, floating around everywhere. I didn't think that was going to go up. I mean, it'd go up, like, predictably up, but I didn't think it was going to, like, slope change up. And it has, like, slope change up already just due to the sandbox environments that agents need.

And, yeah, I mean, that's super interesting to me because that stresses a whole lot of new systems. I think, you know, the things that I work on like all the products I worked on, but also things in the ecosystem like Docker, but like Kubernetes, they're going to be stressed significantly because they're engineer for some level of scale, but this is a different type of particularly non-production workload scale that you have to support. So, yeah, it's fun, fun problems. Going back to hiring, you've hired a lot of engineers and you previously talked about something really interesting. This was I think in the

context of maybe HashyCorp, how some of the best engineers you've hired, how some of the best engineers you've hired had really boring backgrounds. Can you talk about that? Like, who were the best engineers you hired? And like, how... Yeah, it's a better way to frame it. Yeah, I stand by this. The best engineers I can remember from my time at Hoshkorp,

but also just in every job that I've had are notoriously private, and not because they want to be private, because they just don't care to be public, I guess. It would be the better way to put it. I don't want to, like, carefully describe anyone without giving them away, but, you know, they're just, they don't have social media profiles. Very often, they honestly, are nine to five engineers.

They go back and they don't code at night. They just spend time with their family. But because they don't do anything else during their working time, they're like locked in and they're really good. And it's not about putting the hours. It's also just skill-wise, super strong. So, yeah, I always found, like, when I was reviewing resumes and stuff, when you find the person that has a resume where they, like,

they don't have any GitHub, even a GitHub account. Like, some people are like, oh, you have to public contributions to stand out. Like, that is a way to stand out. But also if you have zero public contributions and you've just worked at companies that also I've never heard of before, it kind of is interesting to me, which is like, okay, you might know something like deep. So, yeah, I think that, you know, the problem is, and the funny, the ironic thing is I spend a lot of time on social media and these engineers are better than me. But the funny thing is every moment you spend on social media, time is zero sum. So every moment you spend on social media is taking away from something else. And the issue is it's not one for one because as every engineer knows,

the time it takes to really get your mind into flow to get going with something is, it varies, but it takes time. And so when you context switch to social media, if something's compiling and you tab over and you spend time, you've given something up in terms of thinking, I think one of the best things I do spend a lot of time on social media, but maybe unhealthy amount of time on social media, but also an unhealthy amount of time at night.

I don't have insomnia, but it takes me a long time to fall asleep. And it's because I just sit there in the dark. And I love, some people do this in the shower, but it's not long enough for me. I love to just sit in bed, lights off, my wife's sleeping, and I just think through, like, I'm writing code in my head, I'm thinking through products, I'm thinking through website copy,

I'm thinking through, I'm running CILIs in my head of how it's going to feel. And sometimes, last night, I went to bed at 9.30, because I'm a dad, so I go to bed early. And you have to wake up, and you don't know when you have to wake up. Yeah, yeah. And I didn't even feel like I was up that long. It's like, I got to go to the bathroom. I should really actually go to sleep.

And I looked at it was 1230. And all I was thinking about was, it's so dumb, but all I was thinking about was is vouching system of how vouching might work. It might not work. And I've always had this thing where I'm willing to, I like competing. I think competition is fun. but I always feel fair game to compete with anyone in product building space because I think I'll spend more time thinking about it than they will.

I think people turn it off and I try not to turn it off. So, yeah, I mean, I think the point of all that is the best engineers are the ones that context switch the least probably. Having used AI, AI agents, do you think that's my change? Because, you know, like these agents can go on and think or do work for you. Like, how would you hire in this new world where we're using AI as kind of a given? Most devs will prompt and fewer and fewer write, even though best devs clearly know how to write code as well. I would definitely require competency with AI tools.

You don't need to use them for everything. That's not important to me, but it's an important tool to understand the edges of. It's like any other tool where sometimes it's useful and sometimes not useful. but if you ignore it completely, you're going to do something something suboptimal in a time. I mean, the best example, to me, is proof of concepts.

Like, constantly in real product organizations, you have an idea, and you need to, like, demo it out to figure out if it works. I would much rather someone just, like, throw a slop at a wall that you're never going to ship and spend a day doing that,

you know, less than a day doing that, rather than spend a week doing it organically as a human. like because you're going to throw it away anyway and you don't even you might throw it away because it's a bad idea but i'd rather prove it out and so just slop it up and so this is why it's so nuanced i'm so like i'm so get so worked up about sloppy prs to open source but it's because there's a time and place for them and that's not the time and place for them but there is and so i would hire in that way and i think the other thing that i don't know if is the right thing to do but i would strive that that goal that i have i would strive for everyone

to have an agent running it all the time. Again, like, it doesn't need to be coding, but to be doing something extra for you, I would strive for that because I do a driving. That's my biggest one. On the drive here, I had some deep research going, and it's like I will always spend 30 minutes on the boundaries when I wake up and before I stop working

and before I leave the house something, I spend 30 minutes stop working. What can my agent be doing next? That's slow. What's a slow thing my agent could do for the next time? and I know I was going to drive here for an hour. It finished far faster than hour. But, you know, it was just like, oh, I need to do some library research.

Okay, find all the libraries that have these properties that are licensed in this way. And I was looking up some like H-D-3 stuff, quick stuff. And so build that ecosystem graph for me. Right before I left, I was working on something to do with this vouching system. And I didn't quite understand the edge cases of what I was doing. And I will think about that manually. But why not just start, just start an agent to like, look at this repo and I use AMP to consult the Oracle,

like think deeply about what the edge cases might be. What am I missing? If I had another two hours to work, I wouldn't need an agent to do that. I would have done it myself, but I don't. So why not have it do it? So it's just part of my goal to always have one going.

And I unfortunately don't have one going because they finished it all right now. Interesting. And so this agent running there is kind of, do I feel correctly that it's now so natural that it doesn't get in the way of your own thinking. Like you do your own thinking and you do your work. but every now and then you glance and you ping it or you start it or it's now so it's not distracting right because I think that's yes I actually turn off all the agent tools do this and I turn off the desktop notifications yeah I think the desktop notifications are for the most part of mistake so yeah I turn those off I choose when I interrupt the agent not it doesn't get to interrupt me so for sure and there's another aspect where I think my engineering has changed where I try to identify the tasks that don't require thinking

and the tasks that do require thinking and just delegate like delegate the work to an agent like sometimes it just feels productive do the non-thinking tasks and you're like yeah I did a lot today you got this but

a lot of times I just try to just delegate that out there's a lot of people that you know say like you think less and I think if you use the tools wrong you do think less because you just like launch an agent and I don't know go watch YouTube or scroll social media or something, but if you instead view it as a way to choose what you think about,

then I think that you don't need to sacrifice that thinking. But I think the problem is the majority of the population probably won't do that. Yeah, but it's still, I think it's good food for thought. And it goes to hear from you on how you're using, and it's working for you. When did you start to have this second agent running, welcome to switch, was it the model's getting better?

Yeah, I don't remember which model it was, but there was a certain... I tried cloud code right when it came out, It was just like March or May last year. It was March, the beta. Yeah, and the May public release. Okay. I don't think I used the beta.

So it was probably May. It wasn't super impressed, honestly. And then, I mean, really quickly by like the summer, at some point during the summer, oh, I remember, I remember. I saw so many positive remarks about it, that then I started to get scared that I would be behind on how to use a tool. And so I actually started forcing myself to,

I still didn't believe in it so I would do everything manually but I was forcing myself to figure out how to prompt the agent to produce the same quality result I was working much slower because I was doubling the work and it was more than double because

they're slow and we're going back and forth and I already had the work done and all the stuff but I was forcing myself to do it and you find stuff that I couldn't figure it out it just wasn't there yet but then I found other stuff where it's like oh I naturally got to the same point that thousands of other people got to, which is like, oh, if I do a separate planning step,

it does so much better, and everyone got there. And then I figured out, oh, if I have a better test harness for it to execute, it does a lot better. And then, you know, I think everyone starts with like no agents. that MD or Claude.md.md. or anything. Same thing. I realize, oh, if it makes a mistake and I add that just to agents. at MD, it never makes that mistake again. Like, oh, and like, these are just like incremental things that I recognize when I see people

that are new or I've watched a couple of live streams, like lurked on live streams where, like, kind of anti-AI people, like try AI. And it's one of those things where I'm like, they're just swinging the hammer way, way off, right? Like, it's because you haven't, it's, the thing is like, it's as if someone tried to, like, adopt Git and they used it for an hour and decided they weren't more productive with. it like it takes much longer than an hour to get proficient with get but you put in the effort and then you reap the rewards later and it's sort of the same thing to me with AI tools what would your first advice be for someone who's like not my first advice would be reproducing your

work with an agent and if you really really don't want an agent to code reproduce the research part of your work with an agent um like there there's a lot of people it's like i don't want it to write code for me for whatever reasons like um but yeah just to kind of delegate some of the other research part. There's so many places it could be helpful. So it doesn't need to take, you know, you don't need to pick up on the, it must replace you as a person kind of propaganda. You could just find the

corners of where you work and replace those parts. One thing that you give people is you give advice on for potential founders because you're a successful founder. You've had an exit. You built up this awesome company. You get a bunch of emails from people asking, hey, I want to be a founder. What was your advice? And you wrote about this

you shared the email, but can you tell us what advice you typically give people and how is it received? Well, I usually ask something more specific because, yeah, if someone's like, what could I do to be successful? I always disclaim that you're consulting someone of survivorship bias. So you need to take that into account. But I'm willing to share my experience as a survivor, but just understand that there's survivorship bias. But usually I ask for like, what's something more specific? Like, what are you trying to do? And so we usually get to like, should I open?

or should I be remote or not or should I do enterprise or I don't know. But my my, the most general advice I usually give people is startups are much longer than you think. You're going to probably work on it for I say imagine 10 years. A lot of people say five years, but I say imagine 10 years. Like is this really something you want to work on for 10 years? And is it something that like you need to have a certain amount of hubris in order to say I'm going to work on this for 10 years and I've truly believe I'm going to do it better than anyone else. There's nothing behind that, no substance ban that, other than hubris. So you need to have a certain amount of ego and hubris in your head to make that,

but not too much where you'll be blind to change coming in. So that's usually like the first advice I give because a lot of people have cool ideas, but they're going to burn out relatively quickly. So that's where I start. So currently you're advising some companies. What are you seeing with them? Like what are our service doing these days? What are doing differently than?

you know, like earlier, how's that landscape? Again, it's really contextual in terms of like if you're an AI startup, it's very, very different. How are AI startups working differently? They are, there's a lot of pressure to go faster than I've ever seen any startup.

I think the industry is moving so fast that I don't advise any AI startups, but I've talked to some of them. And it's, even as an advisor, I feel like it's too much pressure because they are just being pushed to prove themselves quickly, whether it's through traction or revenue or something, it's sort of like, there's this mentality within that ecosystem where AI should allow you to go crazy fast.

And in addition to that, there are a lot of companies moving crazy fast. So the change is happening. I think that's the one thing. Outside of that, I mean, like I said, it's just a ton of opportunity in every space. Otherwise, it's a lot of the same stuff. I mean, it's remote versus non-remote open source versus not open source. Do you see the role of software insurance changing now? especially at the A&A of the companies where engineers like yourself,

they're actually being way more productive. They can produce a lot more code, a lot more output. Are they being pushed into being like wearing more hats, talking to the business, of being a bit more like a mini founder, if you will? I hesitate to say more productive. I view that there's an expectation that they could do more. I don't think that's necessarily more productive,

but it's more like you should be able to, for example, build a full demo design everything for your... You don't need a team to do that anymore, right? Like, you should be able to do that, at least from a demo perspective. There's no reason not to, because again, you could ship slot for that. That's fine. I mean, this is still the same, but you should be able to research effectively and in a sense to handle more vague tasks.

I'm saying that a lot more. It's like just... The capacity to experiment is so much higher, I would say. But then when it turns into productionizing something, it feels similar to what it's always been. I think that there's a lot of companies that are eating the dog food of the AI companies, of shipping, whatever. And I think that's a little scary.

Yeah, they look at it in traffic and they're like, oh, they build cloth coal work in 10 days and they'll be building a company. They're freaking out of why they're not doing that. I think a big change is from like a pre-seat perspective or, yeah, a pre-seat perspective where you would be like, I need to raise a seed in order to build a prototype. that's like, like show me the prototype because yeah, you should build, build that really quickly

for most things. There's still hard tech out there that you can't do that. So you do a bunch of coding, you do a bunch of thinking about coding as well, even as you're trying to fall asleep. What refills your bucket outside, outside of coding, outside of tech? Obviously, like the stereotypical things,

like just taking breaks and being with my family and things like that. But I mean, I think the biggest thing is, you know, I am introverted. So just quiet, solo time, refills the most energy for me. I live pretty close to the beach and just, if I'm in a bad mentality,

things aren't working and feeling unproductive or something's going on, like just closing my laptop and taking a walk outside, like stuff like that helps a lot. I have a lot of hobbies and stuff, but it's, I think like just as a general recharge, it's that more than anything.

I know there's a lot of people that's like going out friends or something like that, then I like that, but that's not the full recharge for me. And what's a book that would you recommend and why? So I only, I pretty much only read fiction outside of news. Great. Great.

Okay. The most recent book of fiction I read is an older book, and it is an easy read. So I hope people are like, not like, oh, he's an idiot for reading this. But it was, what is it called? The Something Life of Adi LaRue. It's just like kind of a romantic type of fiction novel. But, yeah, it's just about, it's about. It's about, I think it's like 10 years old.

It's older now. But it's just about a woman who kind of sells her soul to live forever. But the cost was no one remembers her once they walk out the room. And yeah, it's just going through her whole life of losing all human connection. But she gets to live forever what that is like. And I like reading fiction. I like reading fiction at night. I don't know.

I don't know if it's escapism or just like. You just like, you know, you get to live in different roles. It's still so, so different to the coding or anything. It may maybe just helps me turns off the thing. I personally, I probably read way more fiction than I do professional nonfiction, honestly. Yeah, yeah, I'm the same way. It's my version of TV too. TV to me is more of social activity.

Like if my wife wants to watch something together, like we'll watch a show. But if I'm alone, I'm not going to watch a show. I'm going to read probably. Awesome. Well, thanks so much for going through all of these details. it was just not great to hear from how you're working, the history of HashyCorp. This was all just really interesting and motivating.

Yeah, thank you. Thank you. I hope you enjoyed this long and interesting conversation with Mitchell. One thing that really stuck with me from this conversation is Michelle's own rule for himself. Always have an agent that does something. Not necessarily coding, just doing something. For example, while he was driving to this podcast recording,

he had deep research running. Before he leaves the house, he asked himself, what's a slow task that my agent could do while I'm gone? An important part to all of this, he turns off all notifications. The agent does not get to interrupt him. He interrupts the agent when he's ready. Mitchell is in charge and he has a buddy who does the work that he has delegated while he focuses on the problem that he is solving.

This is a nice challenge for anyone listening. Next time you step away from your desk, before you close the laptop, ask yourself, what slow task could an agent be doing while you're gone? If you enjoyed this episode, share with a colleague who's thinking about where software where insurance could be heading. And if you've not subscribed yet, now is a good time. We have more conversations like this one coming. Thanks and see you in the next one.