Mitchell Hashimoto’s new way of writing code

24,931 words · about 100 min to read

0:00 What was your experience back then of AWS? Your honest view.

0:04 AWS was really arrogant. Felt like they were doing us a favor. Subtle vibe of we will spin up a product and kill your company.

0:11 Terraform just seemed to be everywhere. Why do you think that sudden popularity was?

0:15 One of the things that frustrated me was like, oh, they only won cuz they were first to market. We were like seventh to market.

0:20 It feels like most of open source will have to change because of AI. AI makes it trivial to create plausible looking but incorrect and lowquality contributions. Open source has always been a system of trust. Now it's just default deny and you must get trust.

0:35 Do you think Git will be around in a few years?

0:37 What's interesting is this is the first time in like 12 to 15 years that anyone is even asking that question without laughing. If AI agents can write code, open pull requests and ship features, do we even need open source contributors anymore? Michelle Hashimoto, the co-founder of Hashi Cororp, has been thinking deeply about this, the future of open source and how to efficiently integrate AI into his day-to-day workflow. Michelle built the tools that power modern cloud infrastructure, Terraform, and the Hashi stack. He also created a popular terminal, Ghosty, 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 original story of Hash Corp, a failed university research project, a notebook of unsolved 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 adopted to AI coding tools, why he

1:38 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 Work OS. Michelle, welcome to the podcast. It's awesome to be here in person.

2:08 Yeah, it's it's cool to meet you in person after so many years of following you. you've had such a massive impact on on

Self-taught through online code and manuals

2:15 the tech industry on software engineers, but how did it start?

2:18 I think the high level is the same story as a lot of people. I self-taught uh around 12, 13, early teens, motivated by video games. Same like same as a lot of people. Um although I really quickly realized that I liked web, you know, web was new. Google wasn't out yet. I think web was new. And so I I kind of like really quick I I never became a video game programmer. I really quickly just became a web programmer, PHP, um Pearl, that sort of stuff. And uh 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. Um parents didn't want to buy, you know, uh professional books were like I don't know what they are now, but they were like 50 bucks then, right? And and so they were like, "No way, right? This and also they didn't believe I was going to read it." And so there was no way they're gonna buy that. So, um, yeah, just anything I find online was my my inn into coding. I'd walk to school every day with a group of friends.

3:14 There's a period of time where I printed out the first or second chapter of the PHP manual. I remember it was about 30 to 40 pages of of paper and I never programmed. So, all the stuff and I'm 12 is it's 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. I like it like for whatever reason it just came in.

3:46 Those are variables, right?

3:47 Variables. Yeah. Yeah. And I I really understood 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 being like it it triggered and then after that I remember stuff happened really quickly.

4:12 What what kind of stuff did you build? Websites.

4:14 Yeah, websites. It was gaming related websites. It was like a lot of like gamech stuff, forum software. Yeah, I mean I had a lot of fun cloning websites, you know, in a poorly, but like uh PayPal was out and then and 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 a 18-year-old on um uh like freelance websites. And so I got, you know, 100 bucks here, 50 bucks here to do like image like upload stuff. I

Landing a rails consultancy job early

4:46 decided to study computer science in college. Um went to University of Washington. I mean, I guess that's when you would call it serious, but I was I was like really I mean, I was coding every day as much as I could through high school.

4:58 Oh, okay.

4:59 Yeah, that's impressive. Were you alone with this when your friend group there? Were there other people doing it or was it kind of lonely?

5:07 It was lonely. It was very lonely. It was It was lonely in the real world and then I quickly found online friends through like MSN Messenger and ALS Messenger and forums. I found online friends which many I I have met now and I still keep in touch which was cool but no I mean like back then I mean being a being a programmer when no one knew that word but but being into computers was like a social death kiss and so uh even my closest friends didn't though my best friends and stuff like I hid it from all of them and I didn't talk about it at 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 uh my freshman year, late freshman year heading into summer um 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 was do you want to be a Ruby on Rails programmer? And I didn't know Ruby. I was a PhD programmer. Um I had never done Ruby. I'd never done Rails. But I got this email and I'd never been like

6:08 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 him and met the company and realized this is real and they're serious and genuine and I took that job and uh yeah I mean that was I learned a lot on the job there. So that was a huge change. Um

6:35 was it a startup or small company something like that?

6:38 No, it was a consultancy. So, it's kind of like one of those standard like this like 2007 Ruby on Rails was had blown up. It was already very popular and uh there was all these consultancies that that appeared out of 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 student cuz we'd see a client for like 2 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 you there wasn't a lot of scale because we're building MVPs but different like thinking of scale problems. Um yeah it

Immersion in Linux and keyboard workflows

7:16 was it was great. How did eventually Hashi Corp start or what happened between like getting getting this this Ruby job to a few years later?

7:25 It kind of starts with this Ruby job. Um there was one guy that worked at the the company and and he's 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 engineard 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 on dedicated servers and I didn't know anything about that and I 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. I just he sat in the corner. He didn't want to talk to anybody. Um, and I just wanted to know more about what that world was. And luckily, despite appearances, he's very nice. And so, um,

1 Highlight · Forced keyboard-only workflows as a learning catalyst

8:11 yeah, I 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. And it's funny cuz I 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 he literally said unplug he 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.

8:38 And took the mouse away.

8:39 Mhm. Um took me about a week and I got really good with the keyboard.

8:42 Harsh lesson.

8:43 Harsh lesson. And once I got good with the keyboard, um he said, "Okay, here's he he installed screen on my, you know, early team. 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 it slowly taught me more and more and that got me

9:09 just in I loved in like immediately it was like this is super cool super fun so that long-winded process got me into infrastructure and then simultaneously or very shortly afterwards I joined a research project at the University of Washington called the Seattle project which is a terrible name cuz you can't Google it, but it's called the Seattle project. It was I'm sure it doesn't

Notebook of unsolved infrastructure problems

9:28 exist anymore. And it was again another popular thing during this time was uh kind of like um folding at home. It was this idealized folding at home which is can a bunch of people compute of different you know it could be your home machine it could be a unused rack it could be in your basement it could be around the world but can you comp you donate all this heterogeneous hardware and then can you generalize auler 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 um and and and a bunch of other stuff.

10:13 It's very vague but but it was this infrastructury problem and I completely failed at it. like I I tried for a quarter but um from a technical side I just failed and 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 10e 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 sponsor work OS one thing I've learned from studying great engineers Michelle included is 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 service area. If you're building SAS, especially an AI product, authentication, and enterprise identity can quietly turn into a long-term investment. SAML edge cases, directory sync, audit logs, and all the things enterprise customers expect. Works provides these building blocks as infrastructure so your team can stay

11:15 focused on what actually differentiates your product. Great engineers know what not to build. If identity is one of those things for you, visit workwise.com. And with this, let's get back to Michelle's notebook with all the components he would end up building at Hashior Corp. And I still have this notebook um at my house here, but the problems are really like, you know, I have no way to declaratively manage the different resources that are out there. I have no way to network these together in a private network. Um, you know, I wrote these things down and there was a lot of stuff there that I never ended up building, but a subset of that was ultimately what Hashorp would end up building. And I shared this with my undergraduate like boss who was Arman who was my co-founder.

11:55 Y later became your co-founder.

11:57 Yes. He was the my my boss on the undergrad side. And I shared it with him 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?" That, you know, you're a teenager and you have no idea what this commitment is.

12:16 Well, you're like 21 or something at this point.

12:18 Uh, probably not even. Probably probably 19 or 20. Yeah. And he emailed me out of the blue like, "Do you want to do a startup?" Like person you never met or you barely met, never met personally, like all this stuff. It's so funny. And he emailed me that at like 11:30. You're in college. I emailed him back in 2 minutes and said, "Sure." and he remembers thinking, "Wow, yours

Vagrant born from consultancy time constraints

12:36 thought it so fast that he's just in. He's ready to go." That was sort of the start of our friendship. And then uh and uh 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, so this is a development environment that you could spin up quickly, right?

13:06 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 doubleclick and open a dev?

13:12 Yep.

13:13 That was a metaphor I used because

13:14 it's a good one.

13:15 Yeah. What what what the problem we're having was

13:17 any hour waste in a consultancy that you can't build 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.

13:35 And you couldn't build that for the client, right? The client would only pay for the work.

13:38 Yeah. You couldn't build that for the client. So it' be like 4 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 need to go over there and what ended up becoming vagrant up sweet you know few minutes let's help you for the next two hours and then

14:02 and how how did you build it back then was it some kind of virtual machine or

14:05 yeah I was with virtual box virt oracle well it wasn't it was sun then but um virtual box and and that's that's another cool constraint which is that I

2 Highlight · Embracing strict constraints to build better software

14:13 was a college student so I had no money so

14:15 this was expensive back then right

14:17 uh virtualization was expensive virtual box was free and open source I don't care about the open source side um for that. I was I was never going to read it. But yeah, it was free. That was why I did it and and that's why I did that and not like EC2, which should come out by then, but I didn't do EC2 cuz I I didn't have money to pay for these instances. So, um yeah, that's that was the constraints and and I like bringing that up because I think so much of software engineering is understanding constraints and working with these constraints. And your prior podcast there were, you know, called the forces like static and dynamic forces. It's that and and I think that helps create

14:51 better software um when you have constraints and that was my constraint. So yeah, so that was we have vagrant, we have this failed infrastructure project. Um we have uh sort of the my boss at consultancy getting me into infrastructure and all of the and then I mean externally we had the cloud being introduced AWS I I went to school

Betting on a multicloud future early

15:10 University of Washington so

15:11 oh

15:11 I was right there

15:12 right in the epicenter of it Amazon was next door right

15:15 Amazon was right next door they donated a bunch of credits right away I knew about the launch um most of the CS students at UDub interned at at Amazon not necessarily AWS but also including AWS but all over Armon on uh interned at uh AWS and so like I I was in the bubble of like cloud cloud cloud AWS as a when people were pronouncing S3 like s cubed like people didn't know how to pronounce it right that's how that's how new it was and so yeah all this stuff kind of came together and uh kind of led me on the path to to build tooling to better manage it and

15:50 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 has become cuz this was I'm just trying to put yourself back like this was very very new back then right

16:04 totally

16:05 and and I think you know like if if if I imagine I assume more people would have been skeptics or think that is just a fat or whatever what was it like can you can you bring us back a little bit there compared to the to the to today it was very unpolished I guess as I describe it you know like EC2 was I mean I used AWS in general was very unreliable um S3 was the only ever reliable piece everything else was was totally unreliable. Um, 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 when I first started with it. It just felt very raw. Um, and I don't I don't I never really viewed it as this is going to be big. I mean, 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 that's what kind of pushed me towards it is and I say this

17:06 over and over I'm I'm really motivated by like what's the most fun and what like feels right and that it just felt right to me. Um I think where I started making the bet, me and Arman both started making some kind of bet was not just when we started Hashor but we

3 Highlight · Betting on multicloud when AWS stood alone

17:22 started Hashorb on the basis of like multicloud and I really like to like contextualize that at the time we were starting this which was like 2011 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.

17:42 Correct. Correct.

17:42 I I used to use that when it was App Engine. Yeah.

17:45 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.

18:18 That was our bet and uh it mostly played out that way.

18:22 So when you decided to start Hashi Corp, you had Vagrant like was the idea to like you know like 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?

18:33 It wasn't to commercialize Vagrant. But what we had done is Arman and I both worked at this mobile ads company startup. Um there's like less than 30 people and we had built like with Python and C like these really um rough prototypes of these ideas that I had in this notebook of like service discovery and um like an early version of Terraform we called Launchy. So we did DNS based service discovery service discovery by connecting an off-the-shelf DNS server with Postgress and we did all these like hacky things but they felt good u and again we like get back to this like how things feel to me to motivate me like it felt right directionally right. I graduated the the environment in Seattle was not very startup heavy at the time. It was 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 it and and like to a certain extent Facebook was starting to show up up there, but that was it. I knew I wanted to work for startups. So, I had I I moved to to San Francisco. So, I moved to San Francisco, found a startup that would hire me, which was a mobile ads thing. Um and uh just wanted

19:36 to learn. So, that that's the short step there. So, I ended up in San Francisco. Um and I convinced Arman was actually going to do a PhD at Berkeley and he was accepted and in and he was this a huge deal, huge deal. I mean, incredible program. Um, 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

Choosing venture capital to move fast

19:56 took a year to ferment on the PhD. He's like, I'll give it a year.

19:59 Yeah.

19:59 I'll join this mobile ads

20:01 and I'll go back for sure.

20:02 If it doesn't work, I'm going to go back. And what ended up happening in that year is is now where we get to. Um, which is that we had this these these this hodgepodge of proto 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 our 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. Um was like across the street was this company that was called Zimrite at the time ultimately became 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

20:44 Wow. Yeah. So, like stuff like that.

20:45 You were there when I was born.

20:47 Yeah. Yeah. Yeah. And like that happens all the time. Like all the time in San Francisco and and it's not unique to me at all. Like Yeah. There there's a bunch of stories there that I think aren't worth getting into. It's just like it's fun. But I went to all these things and people would just talk. They're all bunch of tech guys, right? And and you'd be like, "What what are you working on?" And 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

21:14 this was like in in 2011 2012 or so like like they they just like went and paid for paid for cloud which was brand new right the previous generation just had had onrem I repeat but server rooms and and server admins they had roles for those all all that jazz

21:28 that was just gone gone like

21:30 that must have been a massive shift

21:31 I I literally can't think of one social event I went to where there was somebody that had dedicated servers the only one is

21:37 Twitter yeah but I I think you like we probably have to emphasize that that This is a massive shift in the industry, right? And it probably was was only happening in Silicon Valley or like

21:45 probably

21:46 yeah well well ahead of of everyone else

21:49 at a scale that was larger than anywhere else. It was probably in Silicon Valley. The joke used to be cuz AWS is so unreliable. The joke used to be that when AWS went down uh all these startups finally became more cash flow neutral and they would lose less money. Um so there would be like a huge you know US east outage and and everyone would be like are you going to migrate regions? like no, we're saving money right now. But yeah, getting back to it, uh, everyone was cloud first, cloudorn, cloud native, whatever you want to call it. And, uh, the other thing was they were hitting all the same challenges that we were hitting and they didn't use our tools cuz they were just like internal prototype tools, but

22:30 but I knew that our tools felt good. So, I had these two things come 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 we could come together. Um, 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. Um but it was it I just had some foundation publicly with to give some credibility to head in this direction.

23:05 Um that was about it and we we started Ashp.

23:08 And then when you decided you incorporated you know got the things did you decide to raise money cuz again back then I guess it wasn't as common wisdom you know why cominator was probably starting around that time. So like startups were startups a big thing or was it a given that okay if you start a startup you're going to raise money

23:23 in my social bubble it was pretty much a given. Um, and and not not just that. So, I we incorporated um I self-funded um I I I transferred $20,000 from my savings account into this corporate account, initial funding. Um and I worked off of that. I didn't I paid myself $0 for the first 6 months. So, the 20,000 was purely towards whatever things the company needed. That was the first 6 months. And then Arman joined after 6 months. Um and and we decided to raise uh and the motivation there really is there weren't many there weren't many other options. There are basically three options as I saw it then which was uh bootstrapping um 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 not like not not Patreon style stuff today like that infrastructure didn't exist there was no subscriber donate type

24:24 infrastructure then um 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 Reddus at VMware and yeah and we kind of laid out this plan that we wanted to do um which was which at inception of the company included Terraform console no it included everything but Vault uh 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 Best case scenario, this is just going to be slow.

25:01 And and the problem with slow is that things have a window and and cloud was growing so fast that if we were that slow, someone else was going to do it their own way. I mean, that was I guess that was the primary issue is we really just wanted to go fast.

25:13 You need you knew you needed to.

25:15 Yeah. I needed to we needed to hire many engineers right away and start building

Building the foundational HashiCorp product stack

25:19 right away. And so VC was the route we chose.

25:22 Can you talk us through the the first several products and and what they do? you know, we we know Vagrant, but just for those who are less aware of of what what became the hashy stack later, right?

25:32 Yeah. Let me see if I can still get these in order. I'm pretty sure I can. So, this vagrant was predated it. The first product that came out of Hashore itself was a product called Packer. Um, kind of understated publicly, but kind of underpins a lot of things in the industry to this day. That's an image building uh tool. So, building Amazon images, VMware images, etc. Um, 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 autoscaling nature of AWS. That was the dream. And if you were, it's kind of like the u what the cold star problem with serverless today. If you were waiting tens of minutes for your server to be ready, you couldn't react. Um, and so my idea was do that, snapshot the image and then next time just spin up that image. Um, and so that was Packer.

26:29 That was Packer.

26:30 So Vagrant Packer. The next one that came out was console. Um, console was uh 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 conceptualize this, 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, you know, they're that's how I always describe breathing. They're they're creation destruction. Creation destruction like constantly. Then things are happening at a scale where the service discovery needs to be much faster. Um, 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 Yeah, I I think this is also kind of more mainstream with like Kubernetes readiness checks and health checks and things like that.

27:24 It was 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. Um, Terraform uh spins up infrastructure code. Describe your infrastructure in in AWS parlament. It was things like all the attachments to your 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 and you would wait whatever amount of time it took AWS and you would blink and you would have thousands of resources and then you with one command again you could just tear it down to zero. That was Terraform. So that came out like 2014. Um so that was the next thing. Uh and then was vault.

28:15 Yep. Um, vault is is is the easiest to describe as secrets management as core. Secrets management encryption grew to do a lot more things for that.

28:23 So it's like like well we have like our on your local developer machine you have you have like your environment variables and doing that at scale at a team level at a company level at a service services needs to access all these stuff securely.

28:35 Yeah, it was much more focused on the like the the production environment secrets. Um, I had dreams and visions of really solving the developer secret problem, but Vault really never never did that well.

28:47 Mitchell just talked about secrets management, which turned out to be a pretty important focus area 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 poll 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 than verify. The vibe part is about innovation, giving your teams and your AI agents the freedom to build and iterate at high velocity. The verify part is the essential automated guardrail. As agents start contributing more of our codebase, independent verification that checks every line, human or machine generated, against your quality and security standards, is more critical than ever before. Helping developers and organizational leaders get the most out of AI while ensuring quality, security, and maintainability is one of the main themes of the upcoming Sonar Summit. This isn't just a user conference. It's where devs,

29:49 platform engineers, and engineering leaders are coming together to share practical strategies for this new era. I'm excited to share that I'll be speaking there as well. If you're trying to figure out how to adopt AI without sacrificing code quality, join us at the Sonar Summit. To see the agenda and register for the free virtual event on March the 3rd, head to sonarsource.com/pragmatic/Sonarsummit. And with this, let's get back to Hashi Corp and why the company decided to raise 6 months after founding. But yeah, it it's just basically like, yeah, where do you store your secrets? And and the secrets were not just I I forgot the words I use 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

30:28 or credit card numbers?

30:29 Credit card numbers. Um so vault was core to all of that and continues to be

30:34 that that is a part to build something like that.

30:37 Yeah, we were really scared when we built that actually cuz um we kind of hid the fact we never lied about it but nobody on the team that build vault had more than one quarter of security undergraduate security experience. There was no professional security engineers from industry. There was no professional security academics and uh yeah we built it. We got a lot of audits because of that. Like we were scared. So we did get a couple we 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. Um 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. Um we got a lot of good feedback. Um but yeah, we we didn't want that exposed in a sense. So yeah, I I I understand, but I mean it kind of validates that you can build good stuff with I guess people who might not have the experience, but I guess people were learning, right? Like yeah, the security stuff ended up we you know, we really quickly hired professionals that helped of the product and and the security stuff was always pretty solid. Um, but

31:39 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 like what we were doing was not fundamentally different than existing multi-undred 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.

32:04 And after travault came

32:06 Nomad

32:06 Nomad. Yeah. Nomad which was our scheduler which was a couple years late for to the market. Yeah.

32:14 What was you sayuler was it not an orchestrator?

32:17 I always described it as scheduling.

32:19 What what did it do?

32:20 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.

32:29 Yeah. Yeah. The the underground problem we talked about. And as you're building out like these, you said like some of

Navigating early growth without revenue

32:35 these took years like how did the business like hash corp as a business work? Like did you did you start to generate some business?

32:43 There was so so like all right tell me about this one.

32:46 Yeah. I I think we waited too long to to develop a business but um for four years there was there was actually revenue from a couple random sources but there was no real reproducible growing business. So you were just building this vision of of the the you know your your the founders vision of like all right we need all these things that would have taken like a decade bootstrap let's build it

33:08 build in 5 years and figure it out.

33:10 That was literally it.

33:11 Yeah that was literally it and 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 there are good ideas the open source community will just continue. Um, 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 is like the technology was the most important thing to get out into the world. Um, 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 might be founders, how did this work with your investors? You know, when they put in money like did they get some board sees? Did you have to you manage manage expectations? cuz kind of I'm hearing just putting a bit of my business hat on is like you know for 4 years you're building these cool things.

33:55 You don't exactly have a business plan. How did that work or or they just believe that eventually you guys will figure it out or or they saw some kind of traction with like open source. It's traction and I don't think what we did was atypical for Silicon Valley. So the really broad handwavy way I like to describe it is you know your seed is about building the product. You don't even know if it's product market fit. you're just guess not you're you're making educated guess but you're building something getting the A you've sort of 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 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 in mind, we were on the right track. It was basically like to build the product. Um we had clear product

34:56 market fit by the a um 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 raise money and slowly slowly get closer and closer to solving the business problem. And I think we were just a year or two late or like later than the average startup, but the general key frames were the same,

Overcoming the first commercial failure

35:24 just on this slightly wrong timeline, I guess.

35:27 And then when you decided to do a business, this was you already had the the hashy stack and then you built managed offering. I remember.

35:34 Yeah. Our first foray into commercialization was a total failure. It was this

35:40 Oh, really?

35:40 Yeah. We had this product that some people You had You would have to have been a diehard like Hashukore 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 the, you know, there's a couple death nails there. Um, 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.

36:13 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

4 Highlight · Why departmental budget fights kill software sales

36:22 tools, we ran into the problem of who pays for it.

36:24 It wasn't as simple as engineers paying for it.

36:26 Correct. And I think um 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. 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.

36:40 Budgets are important, right?

36:41 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." They so I have more budget to buy my other toys that I want

36:50 or I can hire more people or uh yeah, it could get broken down into like vendor budget. So, it could already be earmarked for external purchase. Yeah. So, we have this product that was like, does security pay for it? Does networking pay for it? Does um infrastructure play pay for it? Like, does dev tooling pay for it? Like, where does 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

37:18 down, but uh we had a board meeting for sure on a Friday, and board meetings were usually on Fridays. And and we had this board meeting. We're based in the city of San Francisco. Um board meetings were an hour south in in um real Silicon Valley. And it didn't go well. It wasn't there was no 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.

37:49 You know,

37:49 but you know they're not happy with you. We had this board meeting. We drove home. our mom and I complete drive home was silent and nor Friday it's Friday night so usually what we do is we'd go straight to our mom lived in the city and I lived in LA already but we'd go straight back to where Arman's place and just like have a glass of wine debrief talk through things and we didn't talk on this car ride home Arman drove straight to the office I didn't question that uh we went into the office um sat at a table not much larger than this only difference was there would be a whiteboard I think one of us at that point said, "Well, that didn't go well." Uh, we both knew it. We didn't feel good. And, uh, the the sequence of events here is now very fuzzy. But at a certain point, we decided, let's play this experiment where if there was no sunk cost, if we were starting from scratch, what would we do differently today? We whiteboarded all this stuff out. What we whiteboarded out

38:50 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 Arman 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 to just throw it all away. Just throw everything we were doing before way. 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.

39:33 Um I think and we might not have used Zoom then, 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 closed source features. Yeah, it was a fork but yeah um open core business model. Arman and I thought people would quit like we thought we would lose like we don't have exact number. We thought it would uh shatter some level of confidence and like wow these these guys have no idea what they're doing. We didn't have any idea what we're doing.

40:06 Um, and you know, yeah, 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." Um, enterprise was kind of just like a sudy boring thing. There was like so multiple facets of why people might quit. Nobody quit. Uh, the vibes in Slack were amazing, super positive.

40:31 Oh, what happened? You think like why people internal

40:34 we asked about it in oneon 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 but before there was this feeling of like we're just 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 gonna 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 um so yeah nobody quit it went super well and um we started I don't the I don't know the time of year but it was it was like in the fall we built Vault Enterprise um by the new year within like the first quarter of of trying to do sales we could just tell that it was different. It wasn't like obviously successful yet, but just the caliber of conversation we're having, the the distance we were getting

41:36 in the buying process and the speed we're doing it, it just felt different.

41:41 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 we should have listened from the beginning because uh our potential customers were 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 in the sky thing and and there were so many meetings where someone would be like okay I'll think about that but how do you replicate your secrets involve you know they would just like ask 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 is that was our first feature with with secrets replication.

42:29 Uh not even across data centers. The first feature was just like a cluster of vault servers in a single uh region. you would sell this more focused product, but now kind of the problems I talked about earlier, security was definitely the buyer. There was an obvious budget, 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 uh getting this done. Michelle just talked about how Hashorp managed to build a product that enterprise customers cared about and wanted to buy because it resonated with their scale.

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43:43 Feature flags, experiments, and analytics are in one place using the same underlying user assignments and data. This is why teams at companies like Notion, Brex, and Atlastian use Statig. Static has a generous free tier to get started, and pro pricricing for teams starts at $150 per month. To learn more and get a 30-day enterprise trial, go to statsic.com/pragmatic. And with this, let's get back to the episode and what came after they built Vault.

44:07 And I get asked on the open source side all the time, but these buyers, like corporate buyers, do not care at all about open source. They don't care at all. Like, they need a commercial agreement. And so the the closed source nature of it like some people needed like legal protections around like code escrow in terms of downtime and stuff like that. That was about the extent of it. Otherwise they were like you know we need support. We need proof of concept to prove it works. Um we need some white papers in terms of like other customers scale blah blah blah. Um and yeah that's what we had to build up after that and get going.

44:44 And then so you started selling at Vault and then you did it for the other products as well, right? Yeah, we did Terraform and we did console. Um, we had it for all the products but but you know all this data is public. You could look at it in and the well for a period of time it was public. You could look in like the public reports of when Hosk was a public company. Um, you know, it really broke down to all Terraform.

45:03 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 I only layer that all the other parts existed cuz like Terraform just seemed to be everywhere. Why why do you think that sudden popularity was?

45:20 It's so funny to hear that because I I 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 uh I one of the things that kind of frustrates me I haven't heard it recently but for a period of time one of the things that frustrated me was like oh they they only won because they were first to market. I hear that a lot and we were like seventh to market. Okay. So like

45:50 to market in in in what category?

45:52 In terms of that infrastructure as codes.

45:55 So there were like other like players who you know

45:58 so many Yeah. Yeah. And and no one was a clear winner. It was a waring market. But like that first year 2014 when we came out Terraform I you know at that time one of my marketing strategies was

Maintaining productivity through structured airplane coding

46:09 I was at every conference I I went to 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. Um, and there's actually a little anecdote here was when the COVID lockdowns happened in March 2020, my wife and I had nothing to do at night. 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 in almost 10 years of our relationship that I will have been in the same place longer than 8 days.

46:44 No, for for almost 10 it was at 9 years. For 9 years straight, I had been somewhere different at least every eight days.

46:50 That's how much you traveled.

46:51 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. Um

46:59 you must have like coded while you traveled as well.

47:02 All all the time. Yeah. I had a whole system. When I started traveling, inflight Wi-Fi didn't exist.

5 Highlight · Optimizing airplane coding with micro-tasks

47:05 Yeah. Yeah. Exactly.

47:07 Even now it's kind of patchy.

47:08 Yeah. So I wrote these scripts that I end up iterating on but mostly used. um 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 and when I was on the plane I would just one by one bust them out.

47:28 Uh there's no internet so just commit them locally.

47:30 Yeah.

47:30 And then I would get back and and some people used to notice this cuz I would land and you would get this push and people would get these email notifications where like 30 issues were closed all at once.

47:39 Wow. But I found the key was pre-planning what issues you were going to work on. I did that online on the ground.

47:46 Yeah.

47:47 And then breaking them down into 15-minute chunks because I found it was really hard to get into like multi-our 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

Navigating the intense IPO preparation process

48:02 just like bug fixes, right? Like just cleaning stuff up. And so that was my process. In 2021, Hashi Corp 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 6 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 I had visibility on to, I mean, it's it it takes over a year to 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.

48:52 Um, I don't remember what the drop deadad date is, but there's a date where you could just like cancel going public and it's pretty close. Like, it's like very close to 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 speaker phone and ask you the types of questions. Um your CFO or VP of finance gives the full report of the quarter.

49:26 They try to frame the types of questions you get. You run it and you try to figure out like whether it's running well enough, I guess. And that's sort of what the prep feels like. And there's a an obscene amount of secrecy because um from a regulation standpoint, you can't talk about any of this. And so I mean you could look back at at even the dumb stuff like hacker news comments like I just went radio it's the clearest signal that a company's going to go public cuz I went radio silent on every topic because everything became questionable.

49:54 I remember there was just a point cuz I I there was a hacker news comment I gave like eight months before it went public and our general counsel like in the middle of the night was like you have to delete that after he talked to me I was like I could see how that might affect things but like I didn't realize the matter and I ended up deleting it.

50:11 And is is this because you're not supposed to give public information away or something like that?

50:18 I don't remember the exact regulation to be honest.

50:20 Yeah. But there there's some regulation about like uh like not leaking information or

50:26 not. It's not really I mean it is it's all information but it's more about like you can't influence the market uh in any way. And so yeah and you can't make promises because if you say oh we're going to go public it might cause even private funding to froth up and it's it's a form of fraud. Um so yeah basically like I just stopped talking about everything. I don't know how seriously other people take it, but I took it to the point where I planned 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 go to New York. It's really really important.

51:04 It has to do with Ashp and they were like, "Sure." And I said, "I can't tell you about it." And they're 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. They like I didn't tell nobody except my parents knew basically. Um none of my friends, nothing. Um except the friends that worked at the company. Um but yeah, it it that's what it's like leading up to

Surviving a near-acquisition by VMware

51:31 it. Yeah, I I was at Uber when we went public and and previously I read that uh while before going public uh Hashi Corp VMware made an offer earlier way

51:42 early that was like in super early

51:44 like two years into the company. We went probably like 10 years in the company. So yeah.

51:47 Yeah. So so like when when they tried to to to buy you like what was it like did you almost sell at some point? Was there any point where where you were close to potentially selling?

51:54 It felt close and and and I got a lot of accounts afterwards that it was very close. It came down to like one vote on the VMware board was what I heard about two years in the company. We were only three employees me including me and Armon. So we had one employee I guess it was two founders and employees three of us. We got approached by VMware. Um you know I didn't know what this would be like and and it is not what it what it isn't is they don't show up and say we would like to buy you.

52:20 No

52:20 no no

52:21 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 buying you. The 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? Um oh our VP of engineering swung by let's talk to him nice to meet you blah blah that uh then I think this is like our actual timeline and then I think uh there was a dinner where there was three VMware executives at the dinner. Um at that point we thought they might be interested but it was still so so much dancing. Oh, this is not even this just this is months before there was even an offer. It was still so social like we drank, we talked about

53:23 our hobbies and interests and very not about I mean very basic about like tech. It's really more vibes. They go to dinner. Uh and then it started to get more serious. We spent more time impella at the VMware offices where we started talking about partnerships about how 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 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 um honestly we were getting tired of it because nothing was happening anyway

54:00 sounds like you're a startup and you're going to all these meetings

54:03 oh I don't even live in the bear area. So I was flying up all the time. It was a waste of time. And um 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 mer acquisition.

54:14 M acquisitions becomes a waste of time. So I'll tell you another anecdote after this but um ultimately we kind of politely had the like okay let's like or get off the pot kind of conversation and they uh put an LOI in front of us which is a uh letter of intent. Letter of intent. The LOI was it was one page. Um, you know, it was it's basically like a non like semi-binding promise that we're pursuing buying you.

54:44 Uh, no number on there. It's just like kind of vain.

54:47 Still no number.

54:48 Yeah. Well, verbally, but they're not 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,

54:55 we had gotten a drop of $20 million, which

54:59 doesn't sound that much. Well, yeah, but we're 23 years old. Oh, yeah. The three of you 23 years old.

55:05 I'm 23 years old. Me and Arman together own 70% of the company. Um

55:11 Okay. Yeah.

55:12 Yeah. It you know it 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 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.

55:47 And uh and that was verbal, too. So, there was nothing binding about that. Yes. It was just it was it wasn't like yes. It was more like okay, we'll work on that, you know, but very positive.

55:55 Yeah. like in this indirect. It's an indirect

55:58 indirect business sense in indirect. Yes. And it turned into come meet the CEO of VMware, you know, like clearly they're interested cuz we're like climbing still. Arman and I we kind of started getting cold feet because it's it's it's the way we described it is it's a dreamkilling amount of money. It's like you would take the money but you're too small to be important to a company like VMware. So they're gonna just

56:20 because because even though it's like so much money but

56:23 personally it's so much money

56:24 but but you know that at VMware level I guess you see their revenue their all that you realize that for them it's not a big deal.

56:29 It's meaningless to them. Yeah. It's meaningless.

56:32 It's crazy. That's that messes with your mind you know.

56:34 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

56:51 you would get a manager of a VM where

6 Highlight · Dodging the dream-killing acquisition bullet

56:53 not even the CEO

56:54 the executives make it sound like they're going to do all this stuff with your products but like that's just one executive in a cog of corporate machinery. So we started getting cold feed 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 it's like a dream killer. Arman very maturely and he's two years younger than me so he's 21 at this time.

57:17 No, he's he sounds like the older one.

57:19 Yeah. Yeah. Yeah. Yeah. Yeah. He's very mature. And Arman very maturely came up with the uh I forgot where it comes from but the the risk minimization uh not risk um the um 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 cuz we had a we were going to have a lock up no matter what.

57:47 you're gonna 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 what our numbers were but they were pretty close and we ended up at 100 and we 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

58:11 and they they wasn't a yes this one had a lot more hesitance it was a lot more like

58:15 uh 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 their board 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

58:37 it's just crazy how

58:39 so such small things could you know like influence if if that was an extra yes

58:45 who knows what your one person you you might have, you know, like it's hard to but you know in VMware you might have been clogging away on like this this project.

58:54 Yeah. Yeah. I mean we didn't build terform yet. So Terraform

58:57 terform might have not probably never would have existed. High confidence I know who the vote was. I know why they

Varying relationships with major cloud providers

59:02 voted that way. Like I know a lot more details but it's like I it's it worked out obviously in my favor. But yeah.

59:07 So you've you've left Hashi Corp and and you're 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 on Twitter you wrote about you said like ask me anything about the big cloud providers because at Hashior Corp you work with all of them. What was your experience back then uh of you know like Azure, AWS, Google Cloud like like your kind of honest view of how they work back then and possibly like how has your views changed on them. The precursor to that is while I was at hosp I I obviously had 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 and so I was just very professional about all their relationships and like

59:51 we like all of them like

59:53 yeah or just say nothing. You have 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 I was still flying too close to the sun as they say. and then enough time passed where I was like ah like my opinion doesn't really matter and um yeah so my to answer your question um my broad view of all of them was that AWS was really arrogant annoyingly arrogant was how I describe it

1:00:19 and and when you say arrogant like can you help us understand like how you work with them or what part of them or like is just general I

1:00:27 I'll start disclaiming this though that you know we worked with so many people there that there for individuals and all of them who are awesome and nice and kind. And 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 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 it felt that no one ever said that. Um 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 it did kind of come to that,

1:01:11 but you know, we did see that later on with Elastic and

1:01:15 Oh, that had already happened.

1:01:17 Oh, it happened already.

1:01:18 Yeah. Just not with us, but with other open search.

1:01:20 Yeah. and they always publicly spun it as like, oh, it's so great and 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.

1:01:33 No, I I I think like um I I I don't think people paying attention to open source appreciated what Amazon did with with it. It it really hurt Elastic's business and it showed how open source can be weaponized against a company that spends, you know, their blood, sweat, and tears. And I guess you know Hashorp you had the same thing right cuz cuz you you were publishing permissive but I mean open source needs to be permissive so

1:01:54 it was MIT or MBL license. Yeah.

1:01:57 So like Amazon could have spun up any anything they wanted.

1:02:00 Yeah.

1:02:00 There was like a 2-year period where I 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. Um and so yeah that's 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. Um 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.

1:02:37 Um and all of that was pure open source pure integration with a commercial entity and they were not helping us all. and and they were the last of any of the cloud providers to to provide any sort of help there. And it it came down to some drama where we went to a meeting and basically said that we're going to publicly say that the AWS provider is deprecated and we're done. Like the community could pick it up or whatever, but we're not we're going to

1:03:07 Yeah. Cuz you didn't get any help from them.

1:03:08 Yeah. And it's taking up too much 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 started 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. Um Microsoft I would 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

1:03:36 Azure

1:03:37 Azure and a lot of nouns like like principles and I I didn't I still to this day and I've integrated with the service don't fully understand the AM hierarchy of Azure um I just kind of bolted it and got it working with a team and and that was that but so technically kind of h but from the business side competent um professionals and team players that's like how I describe it. They we 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 and yeah, very pleasant. Awesome. They were the first people to jump on board uh supporting Terraform. Sure, that's some kind of bias, but like they were consistent throughout the years. So positive on Microsoft. Um and Google Cloud, you know, my my or yeah, Google Cloud in general, it was always like the best technology, the most incredible technology and architectural thinking.

1:04:37 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 I think the the best public example that you could just see in history was they were the only company that when 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 they they fully automated the whole thing. So when they shipped the new Google Cloud thing, it had a Terraform fighter 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 that they had that.

1:05:22 But whenever we would get into how do we do co-ail? How do we like attribute your sales engineers quota to selling like infrastructure that's spun up by Terraform? Like how do like how do we do this? to like the business side of things.

1:05:37 Crickets like impossible to get anyone. Not just impossible, it was 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 was it that's what it felt like. And the other disclaimer I give is all this knowledge was circa, I don't know 2019

AI tools paradoxically increasing terminal usage

1:05:56 something like that. So maybe in the past seven years things have dramatically changed, but that's what it felt like.

1:06:01 Yeah. going to to open source. You're you're actively involved in in open source and open source today and it seems open source is changing a lot especially with with with AI and and you know you're seeing stuff at ghosty like can can you tell us like how you know open source has has changed with ghosty with AI contributions and and what what are you seeing with 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 um is I mean there's there's multiple but the one that I feel is most prevalent across industries right now is AI contributions and the 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 yeah.

1:06:56 And and so after you left left Hashi Corp, you you started Ghosty. Uh how many years ago was that? Was that like two years or so?

1:07:04 Well, I left Hashorb over two years ago or a little over two years ago. I had like

1:07:09 poked around with prototypes of Ghosty like maybe 3 years ago. But after I left Hashorb, I started just like kind of working on it like 20 hours like much more um just because it was the thing that I had.

1:07:20 What drew you to go see? What was your kind of vision? Why did you start working on it? It's a it's a it's a better terminal, right?

1:07:26 It's a terminal. Better is subjective.

1:07:30 Well, I I installed it cuz I I I like it better. But yes, a a terminal and opinionated terminal, right?

1:07:35 Opinionated. um very modern in terms of like supporting as many of the newer specs as possible that enable functionality like displaying images or um you know clicking on your prompt to move the cursor and but like dozens more uh examples like that. The original

Choosing Zig and a multi-threaded architecture

1:07:52 thing that drew me to it is 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 pick the best technology that then solve 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 Hash Corp incorporated and 3 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. Um I had never really worked with GPUs and GPUs I guess crypto was happening but I kind of ignored that whole trend. Um uh but this is preAI so um but GPUs were obviously in use and I

1:08:53 I just felt like I had no idea how they worked so I wanted to go to desktop. So, I picked all these like different technologies and I said, "Okay, Zigg." Cuz it looked cool to me. I just wanted to try it.

1:09:03 Can for those of us I'm I'm not into Zigg. I heard good things about it. Can you explain why Zigg is so interesting, innovative, and why does it grab so many so many devs attention? I don't know why it grabs other people's attention, but for me, it was it it just felt like the the best better C 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 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 programming language on the surface. So, I wanted to build something with it. And so, yeah, I I picked the GPUs, desktop software, what could I build? Uh for for all my time at Hashorp, um I built CLIs. And I was like, well, I live in a terminal.

1:09:53 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. That's how it started. And and as as with a lot of 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. is once I dug beneath the surface, I realized how much they were doing, how brittle some things were, how much better certain things could be, and I I I got sucked into being like, I want to do this better. So,

1:10:28 okay, for like someone who's a a dev, you know, like I I use terminals as well. I'm going to ask the stupidest question. How hard could it be? What does a terminal actually do? And then can you maybe tell us like how Ghosty is is structured or like what what are the things that it it needs to do? just give a little empathy of like actually all the work that you're doing.

1:10:47 Yeah. Yeah. Yeah. I I actually get that a lot. I 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 are like, "I thought they were done." Is usually the most feedback I get. Like, what is there to do in a terminal? Um so, at a basic level, they don't do a lot. The problem is that the the functionality's grown significantly of what terminal developers want to do. But let me let me just give um what they do. It's kind of like an application development platform, right? It's like a it's it's not an operating system. You're not dealing with like hardware level problems, but it is like a application sandbox on top of that. And that other applications run within it and need to render text. They need to render colors and images and widgets and mouse events and all this stuff. Like you're 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 it gets, you know, you start bringing in more and more problems. Like

1:11:47 as soon as you brought images into a terminal, you introduce like a whole new ecosystem of problems. But the tongue-in-cheek answer I like to give to Ghosty's complexity is that it's 30% of terminal and 70% a font renderer. Uh and uh yeah, that's what it feels like. It's really like a problem of uh you know that terminal screen you see whether it's GPU or CPU rendered that terminal screen you see is like you're drawing on a canvas so you are building

7 Highlight · Ghostty's multi-threaded terminal architecture

1:12:13 a renderer for text uh in there everything kind of bubbles from there so from a rough architecture standpoint of Ghosty I like breaking it down in terms of threads because GOI is multi-threaded not all most terminals are not um 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 desktops software and then we have an IO 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 IO 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 as is it's on a VSYNC clock through 30 60 120 frames per second is it's just sampling what the terminal state is and then drawing it. And the renderer 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

1:13:14 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 the big picture. Um it's quite simple at that level. and then just you know extend all the functionality the terminals have into that.

Extreme performance optimization for smooth rendering

1:13:28 So you're kind of like building a like a 2D graphics engine a little bit that has like very focused on fonts.

1:13:35 Yeah. Yeah. It's it's a from a renderer side it's very simple. The renderer is actually not that complicated and and I won't over complicate the hardest part is actually maintaining the terminal state. So the way terminals work is they're a they're a grid of monospace cells. So you'll have like 80 by 2480 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 that 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 which people are used to in terminals going back and that's where the challenge is is doing that in a fast performant way and that's what I try to do with GOI. And I I show this there's so many benchmarks we run, but one of the most obvious ones that shows the speed, which also gets a lot of criticism um is just catting a large reading a large file. If you just like dump a bunch of text, how fast can it 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 if you take ghosty kitty, um

1:14:38 elacrity, um any of these newer terminals, they're all going to do great compared to terminal app on Mac OS. um or traditional like Linux terminals. The criticism is why does that matter? And you know the the easy answer is when you um when you accidentally cut a file like a lot of people will force close. Um the creator of Reddus posted a great comment for me, a great uh comment on hackernews about why he loves Ghosty, which is that he previously used to tail production Reddus logs and you know it 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.

1:15:16 So he could render it and actually work with it. And he doesn't have to do that anymore because Ghost is fast enough that he could just let it dump while he's going through it, parsing it, like like mentally parsing it, things like that. and that just saves him time and um yeah

1:15:30 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 and I think it's refreshing to actually have examples and I I hope we will at some point maybe get back to it you know we'll talk about AI but that might not help but there's a level of craftsmanship right just like not wasting resources or being efficient or I I think we all like I I see in in my day-to-day life like we have more powerful resources laptop tops, phones, and they're not getting any faster and it's just frustrating at times.

1:16:01 It's kind of like the love of the game. I mean, a lot of lot of ghosty is just the love of the game. Um like like I like to say like our renderer cuz cuz like like I disclaimed before like it's not complicated. I'm not I'm not ever going to say that ghosty is like a a 2D game because a 2D game from a rendering standpoint is much more complicated. Um, but I do care a lot about the render and we got our renderer down to for a full screen on on my Mac um set of grids.

1:16:27 Each frame updates in roughly I don't know it's like it's something like 9 microsconds or something. Um, that doesn't include the draw time. That's just like taking the state and submitting work to the GPU. It's about 9 micro and the GPU takes some time. 120 hertz 120 frames per second frame is 8,333 microsconds. So if you have nine, you know, again, we don't have the number of how long the GPU takes, but it's super per it doesn't take much time at all.

1:16:57 You're leaving a lot of options and work for

1:17:00 what I'm saying is like we could have made it 2,000 microsconds and it wouldn't have mattered. It like you would you would still get that performance, but that's not fun. Like I want to make it sub 10.

1:17:10 I I I I like the fun.

1:17:11 Yeah. So, we spent a lot of time just like it was a big I I blogged about it. It was this thing where we got it down from it used to be about 800 microsconds

Managing multiple AI agents as a developer

1:17:19 and got it down to like nine and uh I thought that was awesome. Even though for end users it doesn't make a difference.

1:17:26 But as you say the the craft and the level of the game. So when you started out building go that that was around the time where I think Chad GPD was out. There were some tools. How did your tool set change in terms of how you're developing day-to-day?

1:17:38 There's two sides to that. So one AI gave a huge boost to terminals which is a funny thing like a like oh how so the number because of cloud 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 um I 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 and even when you're seeing like codeex apps and claude apps like is leaving the terminal. They're still executing so many things in a pseudo terminal. The number of terminals out there is is massively larger than there was in 2023, which is hilarious.

1:18:23 Oh wow.

1:18:24 Yeah.

1:18:25 So random.

1:18:26 Super random. And so that's part of why uh one of the things I'm doing with Ghosty is extracting. It's actually extracted already what I've called lib ghosty which is everyone reinvents this very small surface area of a terminal and because they do 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

1:18:50 all over the place

1:18:51 all over the place. Yeah. Um 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 is this minimal zero dependency library that people can embed terminals anywhere.

1:19:03 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. Um, 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 I'm a big fan, but you know, within the right categories of things. Like I think that it's a revolutionary tool and I get a lot of joy using it. It Yeah, I use it every day. Um, I use tools like Cloud Code and AMP and Codeex and 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 and that's the best to me.

1:20:03 In your workflow, do you just use a single agent? Do you use multiple agents? Have you have have you experimented with them?

1:20:08 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. Uh 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. Um, but while I'm working, I basically say I want an agent if I'm coding, I want an agent planning. If I if if they're coding, I want to be reviewing or, you know, that there should always be an agent doing something.

1:20:36 So, you have it in a separate tab.

1:20:38 Yeah. Separate tab. And 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 gas townesque like things. And so I'm the 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 I'll run two um in competition with each other because I it's a it's a harder task and I I don't have a high confidence that they're going to just like crush it. So I'll just run Claude versus Codeex or something like that. Or I'll have one coding, I'll have one doing like some sort of research task. Um I absolutely love them for research. That's awesome.

1:21:16 Um, and then I'll be doing something else, but no more than two, I would say. Yeah.

1:21:20 The code that they generate, do you always review it or have you kind of got a bit more loose? And, you know, some people swear on having a closing the loop, having validation for it, or are you like still like, all right, I I want to see the exact code and I'll review if it's correct and what I expected.

1:21:36 Uh, matters what I'm working on. And

1:21:38 if it's ghosty, I'm reviewing everything that's going into it. Um, 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 there three browsers that I tried? Yes. Did it render right like on my phone? Yes. Don't care what the code like. Doesn't make any networks. No. Has no secrets access. I don't care. Like ship it. It's only going to be online for 2 months. So, ship it.

Overhauling open source governance against AI PR spam

1:22:00 Yeah. And then how did the AI policy at Ghostly change? I remember that in maybe a year ago or so, you asked for disclosures if someone is using it. And just very recently you kind of crammed cracked down and said like all right no more.

1:22:15 Yeah we're going to change again too. Well not gonna change we're gonna iterate. Um so yeah a year ago started asking for disclosure and people you know the the 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 if you produce the code with AI and you did it really quickly, then I'm not going to spend hours fixing up your code. You you you spend your time fixing.

1:22:47 Yeah. Cuz cuz you know that that person did put much time and not much human time. You're kind trying to mirror it, right?

1:22:53 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 throw 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 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 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 and the disclosure worked decently well.

1:23:44 The issue wasn't the disclosure. The issue was that the quantity of lowquality AI PRs that we were getting reached a a point where it was too high. Like do you know why that might have happened? more people instructed agents to contribute a PR to fix an issue they had like do you have theories or actually like 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, a step change that I saw at a certain point, and I don't know when it happened cuz 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 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 is a dead giveaway at AI because at least to this day to at the point we're recording this, the way Claude opens a PR is it opens a draft with no body and then it

1:24:48 edits a body later and then reopens it for review,

1:24:51 which is not how a human would do it.

1:24:53 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 happens at a speed that's unrealistic. It opened the body came in less than a minute later and it opened less than a minute later. Like,

1:25:07 yeah,

1:25:08 pure AI. I I just tweeted about this a couple days ago, which is just like I I wish that these agentic tools would put a pause on opening PRs for a second. Um because I think that's the point where it's really causing a lot of friction.

1:25:21 How did you change the policy? Are are you considering closing down PRs? You mentioned that recently that you've you've the the thought crossed your mind.

Open source repos adopt community vouching systems

8 Highlight · Moving open source to explicit vouching systems

1:25:29 I would say I was crashing out in that moment. Uh but I but kind of um so we shipped this policy update where PRs 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 you about. Here you go. It we and we we get about two or three of those a day. And so we just close this thing. I don't even I literally don't even read the content. I could see it's AI. Uh I can see there's no fixes issue number. I just close it.

1:26:01 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 uh we're recording this in the middle of another transition, which I already have the PR open. Um 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, don't care 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. Um, and if they 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 from the repo.

1:26:48 This reminds you a little bit of, you know, the social lobsters.

1:26:51 Lobsters. Yes. That's what it's based off of. So, the idea is that you're putting your own reputation on the line by vouching for somebody else. I'm a reasonable person. If if this happens and I or one of our maintainers or community made a mistake, if you just like hop into Discord or email and 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 we're sort of moving that system. I think one thing that's a little bit different is um I should say that this is one inspired by lobsters but specifically in the AI space is inspired by this project called PI. Um they do this uh well they do they

1:27:32 call it is built on pi it's a self-improving

1:27:35 like uh build your own agent toolkit. Um so you know kind of ironically it's 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 other 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

1:28:01 there's a bad actor, you could actually ban them. Not not just like you can't even attempt to contribute again. And um that's just a yeah, we had one yesterday where someone opened PR, we closed it because it violated it they had no associated issue uh 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 um stuff like that is just it's the problem is it's just wasting time.

1:28:29 It feels like most of open source will have to change because of AI, right? like it's you you probably know more more maintainers but I I hear this your story is not the only one you know like the project closed down uh PRs GitHub is I think just shipping a feature that projects can automatically close or reject PRs.

1:28:50 Yeah, I think open source will have to change in a lot of ways. I mean I think I for 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 could just build it right

1:29:00 theoretically. Yes,

1:29:01 that's 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

1:29:12 and now that that has been eliminated by AI. It's I like the wording that PI uses which is that AI makes it trivial to create plausible looking but incorrect and lowquality 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. Um but yeah, it's been that reputation system has been taken advantage of in a certain sense with with AI um or the default allow PRs has you know has been taken advantage of. And so I think like like this vouching system that 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

Forking is the true core privilege of open source

1:29:57 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.

1:30:05 Do you think we might see a lot more forking happening though?

1:30:07 I hope so. I hope so

1:30:09 because until now forking used to be a you know like like a fork off a little bit because it was a lot of effort that it wasn't to to keep up like it it never seemed viable to fork a proper project. Right.

1:30:21 Yeah. And I and okay I I am separate from AI and everything I have always been a huge proponent uh 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.

1:31:08 But the thing is I I've said this since 10 years ago in Hash Base, hitting the merge button is the easiest step. Getting 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 your road map. um the bugs um 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 or anything, remove anything. So the core privilege you get with open source like OSI open source is forking and you should take that's that's the right you got you should fork it and maintain your own software.

1:31:44 Yeah. One interesting impact of AI as someone tweeted about how there's a

Git and version control face breaking scale limits

9 Highlight · Why Git struggles with massive agentic code churn

1:31:50 rumor that big tech is looking into rearchitecting their monor repos because of agentic tooling AI tooling just a lot more code being turned out. What's actually what's actually happening? What's the problem with git? The problem with git, I mean there I think there's a lot of problems with git, but uh the monor repo problem with git is that git is is relatively bad at very large repositories because you you pretty much have to clone the entire repository. There's there's some extensions to like fix that, but like official mainline git can't really do that, right? And so for very large uh changes the uh very large repositories um 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 concept rebase merge Q solves that to a certain extent. I think merge cues works for humans at a certain scale, but the merge cues 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 thousandx that, I think it gets completely untenable in terms of

1:32:50 how are you ever getting any semblance of cohesiveness onto the main branch um quickly. And and so yeah, I I think there's a confluence of problems there, which is which is the merge cube problem, the dispace problem, the like branching review type problem. Oh, I I also treated the other 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 you don't accept it, like you pretty much are the branch.

1:33:23 GitHub, you could reaccess closed PRs, but you 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 and that's like relatively important information. Relatively important. It's not as important as the positive, but there like I 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 who never had to think about

1:33:59 their tagline or something was like never deleted email I remember seeing that in some s of marketing was like

1:34:03 just archive it right never delete it and that's where I feel like where we should be at with code which is like just this huge repos lot of context we need better tooling in order to find relevant context in that git repo um or version controlled repo. I would say that the real you ask for like real examples. I do advise um a company that's currently stealth but working in this space and they're the real examples is is is driven by the highly adventic companies. The companies that are like going really allin and drinking the Kool-Aid and their struggling in terms of the amount of churn that these agents is causing is so much greater than humans. And it's not a 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,

1:34:51 so are problems performance problems mainly with w with with git or or just like even the workflow of

1:34:57 Yeah. Yeah. All of it. Performance for sure, but workflow. Yeah. I mean like every time you pull you're you can't you can't push because every time you pull there's another chain like every time you push it's

1:35:06 there. Yeah. There there's a lot of parallel work happening as well. Do you think Git will be around with with with the judge in a few years?

1:35:12 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.

1:35:23 We're not laughing,

1:35:24 right? Like like if you if 5 years ago you said, "Will Git be around in 5 years?" You'd be like, "Are you 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 will laugh, but like there are people that critically think that Git might not be around in 5 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 code generated, the pull request is meaningless.

1:35:45 Changes will h like Git and GitHub forges in their current form do not work with agentic infrastructure today and it's nent today. So yeah, where change will happen and I'm not exactly sure and it's not something I'm trying to change myself but it it you know I'm on the receiving end in terms of a agent user and a maintainer where I'm like this isn't working. What other engineering practices that you know we have been

Testing and harness engineering must evolve for AI

1:36:13 relatively stable for like 10 20 or even more years you think have to change or or are looking to change thinking things like CI/CD testing code review other you know ways of

1:36:24 yeah you know AMP has this saying which is is is it's kind of clickbaity but it's so true everything is changing and this is this is the first time really where it feels like it is the first time in my you know short relatively short to other people, but still a 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. Um I 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 and just jumping around is is unreal. So there's a bunch of mobility there in terms of uh I mean cursor itself is a great example of a company that reached an insane valuation that you could never have gotten preai on an editor product. So editor forges um CI/CD for sure and I 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

1:37:27 even the best test case scenarios don't have like I mean the best I guess have full coverage but that that's a very extreme the the very good test case scenarios just test like one of the edge cases and one of the happy cases and you know bad case and they 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 on its path to its own goal. And so uh I've heard this called a lot of things. I mean the one I like the most is like kind of like harness engineering which is like

1:38:10 harness engineering.

1:38:11 Yeah. That'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 it could have called out to to have prevented that bad thing or course corrected that bad thing. And so sort of like moving from the product to working on the harness for the product or product development. And so, yeah, there there's there's a lot of that where I think testing has to change to be far more expansive, but CI/CD is not set up just resource performance- wise to be able to do stuff like that. Um, so yeah, I'm I'm not sure how it changes, but that's going to change, too. So, everything is on the table. It's really interesting.

1:38:51 Yeah. And a lot of tools to be built. One other thing, observability.

1:38:57 Yeah. And then and I guess on that same topic I mean of the volume and and scale and observability it's also like the sandbox like I didn't think even being in infrastructure and being heavily into infrastructure you know containers blew up the amount of like 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 changed 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 I think you know the things that I worked 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 engineered for some level of scale but this is a different type of particularly non-production workload scale that you have to support. So, um, yeah, it's it's fun fun proms.

1:39:54 Going back to hiring, you you've hired a

The best engineers context switch the least

1:39:57 lot of engineers and you previously talked about something really interesting. This was, I think, in the context of maybe Hashi Corp, how some of the best engineers you've hired had really boring backgrounds. Can you talk about that? Like who who were the best engineers you hired and like how how

1:40:12 that's a better way to frame it. Yeah, I I I stand by this. Most the best engineers I can remember from my time at Hoskar, 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 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 9 to5 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 and they're really good. And it's not about putting the hours, it's also just skill-wise, um, super strong. Um, so yeah, I always found like when I 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 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 have never heard of before, it kind of is interesting to me, which is like,

1:41:13 okay, you you might know something like deep. Um, so yeah, I I think that, you know, the problem is, and I the funny the ironic thing is I spend a lot of time on social media and these engineers are better than me. Um, but the the funny thing is every moment you spend on social media, time is zero sum. So any every any moment you spend on social media is taking away from something else and 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 you if something's compiling and you tab over and you spend time you you've given something up in terms of thinking I I think one of the best things I do spend a lot of time on social media but maybe unhealthy the um amount of time on social media, but also an unhealthy amount of time um at night. I I don't have insomnia, but it takes me a long time to fall asleep. And and it's because I just sit there in the dark,

1:42:13 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 CLI in my head of how it's going to feel. And sometimes last night I I went to bed at 9:30 because I'm a I'm a dad so I go to bed early

1:42:37 and you have to wake up and you don't know when you have to wake up.

1:42:40 Yeah. Yeah. And I didn't even feel like I was up that long and I was like I got to go to the bathroom. I should go I should really actually like go to sleep and I looked and it was 12:30 and all I was thinking about was uh it's so dumb but all I was thinking about was this vouching system of how vouching might work and might not work. And I've always had this thing where I'm willing to I like competing. I I think competition's fun. Um 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 I don't I try not to turn it off. So um yeah, I mean I think the point of all that is the best engineers are the ones that context switch the least. Probably

1:43:22 having used AI AI agents. Do you think this might change because you know like these agents can go on and think or or or do work for you? Like how would you hire in this in in this new world where where using AI is kind of a given? Most devs will prompt uh and fewer and fewer right even though best devs clearly know how to write code as well.

1:43:46 Um 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 like 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 suboptimal in a time. I mean, the best example to me is pro 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 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 or organically as a human like cuz 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

1:44:47 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 it's the right thing to do but I would strive that that goal that I have I would strive for everyone to have an

10 Highlight · Always keep an asynchronous agent running in the background

1:45:01 agent running at 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 uh I 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, before I leave the house or something, I spend 30 minutes stop working. What can my agent be doing next? That's that's slow. What's a slow thing my agent could do for the next time? And I knew 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. Um, okay. find all the libraries that have these properties that are licensed in this way and I was looking up some like HP3 stuff quick stuff and so find build that ecosystem graph for me. Um, 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

1:46:01 and I use AMP to like consult the Oracle like think deeply about um what the edge cases might be, what am I missing? If I had another two hours to work, I wouldn't need the 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 cuz they finished it all right now. Interesting. And so this agent running there is kind of does 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 you ping it or you start it or it's it's now it's so it's not distracting, right? Cuz I think that's

1:46:35 Yes. I actually turn off all the agenda tools do this and I turn off the desktop notifications. Yeah. Um I I think the desktop notifications are for the most part a mistake. Um so yeah, I turn those off. I choose when I interrupt the agent, not it doesn't get to interrupt me. Um, so for sure and and then there's another aspect where I think my

1:46:57 engineering has changed where I try to identify the tasks that don't require thinking and the tasks that do require thinking and and just delegate like delegate the work to an agent. like it sometimes it just feels productive to do the the non-thinking tasks and you're like, "Yeah, I did a lot today. I got got this." But but a lot of times I just try to just delegate that out. There's a lot of people that 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 the problem is uh the majority of the population probably won't do that.

1:47:41 Yeah. But it's still I think it's good food for thought and it's good to hear from you on how you're using and it's working for you. When did you start to to have this second agent running? What made the switch? Was it the models getting better or

1:47:53 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.

1:48:00 Yeah, it was March the beta. Yeah. And the May public release.

1:48:03 Okay. I I don't think I used the beta, so it was probably May. um wasn't a hu wasn't super impressed honestly. Um and then I mean really quickly by like the summer at some point during the summer oh I remember I remember um 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 it was they're slow and it's we're going back and forth and I already had the work done and all this stuff, but I was forcing myself to do it and you find stuff that I couldn't figure it out. I couldn't like it just wasn't there yet.

1:48:54 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 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 I think everyone starts with like no agents. MD or claw.MD or anything. Same thing. I realized, oh, if it makes a mistake and I add that just to agents MD, it never makes that mistake again. Like, oh, and like these these are just like incremental things that I recognize when I see people that are new or I've watched a couple 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 the thing is like it's 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

1:49:56 an hour to get proficient with Git, 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.

1:50:03 What What would your first advice be for someone who's like not

1:50:06 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 uh but yeah just kind of delegate some of the other research part. There's so many places it could be helpful. So it 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 the corners of where you work and and replace those parts. One thing that you you give people is you give advice on for potential founders because you're a successful founder. You 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's your advice?

1:50:51 And you you wrote about this. You shared the email, but but can you tell us like what advice you typically give people and how is it received?

1:50:59 Uh well, I usually ask for something more specific. Uh because yeah, if someone's like, "What could I do to be successful?" one, I will always disclaim that you're consulting someone with survivorship bias. So, you need to take that into account. Um, but I'm willing to share my experience as a survivor, but just understand that there's survivorship bias. Um, but usually I ask for like what's what's something more specific like what are you trying to do? Um, and so we usually get to like should

Startups demand decade-long commitment and hubris

1:51:22 I open source my project or not or should I be remote or not or should I do enterprise and and I don't know. Um, but my my the most general advice I usually give people is startups are much longer than you think. Um, you're going to probably work on it for I I say imagine 10 years. A lot of people say 5 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 truly believe I'm going to do it better than anyone else. There's nothing behind that. no substance behind it other than hubris.

1:51:59 So you need to have a certain amount of 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 cuz a lot of people have cool ideas but they're going to burn out you know relatively quickly. So uh that's where I start. So currently you're advising some companies. What are you seeing with them? Like what what are servers doing these days? What are they doing differently than you know like earlier?

1:52:24 How's that landscape? Uh again it's really contextual in terms of like if you're an AI startup it's it's very very different.

1:52:32 How how are AI startups

1:52:34 working differently?

1:52:35 They are there's a lot of pressure to go faster than I've ever seen any startup though. Um, I I think the industry is moving so fast that I I don't advise any AI startups, but I've talked to some of them and it it's even as an adviser, 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, um, the change is happening. I think that's the one thing. Outside of that, I mean, like I said, it's just 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 engineers changing now, especially at the A&E companies where engineers like like yourself, they're actually being way more productive? They they can produce a lot more code, a lot more output. Are they being pushed into being like, you know, like wearing more hats, talking to

1:53:36 the business, or being a bit more like a mini founder, if you will?

1:53:39 I hesitate to say more productive. I I I view that there's an expectation 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 and in a sense handle more vague tasks. I'm seeing that a lot more which is like just the capacity to experiment is so much higher I would say. But then when it turns into productionizing something uh it feels similar to what it's always been. I I think that there's a lot of companies that are eating the you know the dog food of of of the AI companies of shipping whatever and I think that's a little scary.

1:54:33 Yeah. They look at entropic and they're like oh they build cloth cowork in 10 days and it'll be billion dollar company. They're freaking out of why they're not doing that

1:54:41 there. I think a big change is from like a preede perspective or yeah preede 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 be able to build that really quickly for most things. There's still hard tech out there that you can do that.

1:54:56 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 uh 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 um 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, I'm feeling unproductive or some something's going on, like just closing my laptop and taking a walk outside, it 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 it's that more than anything. I know there's a lot of people it's like going out with friends or something like that, then I like that, but that's not the full recharge for me.

1:55:49 And what's a book that would you recommend and why?

1:55:52 Um, so I only I I pretty much only read fiction outside of news. Um,

1:55:57 great.

1:55:58 Great. Okay. Uh, the most recent book of fiction I read is an older book and it it is an easy read, so I hope people are like not like, "Oh, he's an idiot for reading this." But, um, it was uh, what is it called? The the Something Life of Addie Laroo. It's just like kind of a romantic type of fiction novel, but yeah, it's just about it's about I think it's like 10 years old. It's older now. Um, but uh it's just about a 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. Um, what that is like and know I I like reading fiction.

1:56:40 though.

1:56:40 I I like reading fiction at night. It I don't know. I don't know if it's escapism or just like you just like, you know, you get a little bit different. Well, it's so so different to the coding or anything. It may maybe just helps me turns off the thing. I I personally I probably read way more fiction than I do professional non-fiction, honestly.

1:56:59 Yeah. Yeah. I'm I'm the same way. It's my version of TV, too. TV to me is more a social activity. Like, if 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 gonna read probably.

1:57:12 Awesome. Well, well, thanks so much for going through all all of these details. It was just not great to hear from how you're working, the history of of Hashi Corp. This was all just really interesting and motivating.

1:57:24 Yeah, thank you. Thank you.

1:57:26 I hope you enjoyed this long and interesting conversation with Michelle. One thing that really stuck with me from this conversation is Michel'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 asks 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. Michelle 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 enjoy this episode, share with a colleague who's thinking about where software engineering could be heading. And if you've not subscribed yet, now is a good time. We have more conversations like this one coming.

1:58:21 Thanks and see you in the next