Vanessa Villa: Alright, so there’s this lady in the cattle space, and she wanted to move cattle essentially – like, how do you efficiently move cattle from pen to the butcher house? And she revolutionized the way that cattle – essentially, they approached this as like… Cattle move in herds, so you cannot move them in a straight line, and you cannot move them down a funnel. They’ll freak out. So you have to gradually move them along a curve in order for them to not freak out. And that’s just the way cattle move in herds, when they’re out in grasses… And so that’s what they did, and that’s now what they do at butchering houses.

But in order for her to get that movement up, it took her decades. It took decades for that to be adopted by the industry. Even though she had study after study showing that if you don’t want cattle to essentially spook, you have to move them along a curve.

So coming back to this - we introduced tech, and sensors, and moved tractors to the cloud. John Deere has done this, they’ve been doing it at least since 2012. Great. How much is a tractor now that’s connected to the cloud? How can you repair it if any of the sensors break? How much is that cloud subscription for them to even look at the data that they’re aggregating through the sensors, versus the generational knowledge that they’ve had passed down. So this is the big debate, is like how long is it going to get lay men tech to farmers, to everyday farmers, or is it going to be continued to be controlled by at-scale farming?

Adam Stacoviak: What you’re saying is resonating with me, because I kind of feel like it’s a when versus who. And we began the conversation with shift left, and the idea was - Deepak, you mentioned it’s not after deployment, it’s predeployment. And so we began with a conversation assuming “Oh, we’re putting more burden and pressure on the developer”, which - yes, kind of… Unless you do what you just said, which is it’s more of when, versus who. It’s at development, not so much by the developer. It’s meant to happen prior to going into production, so that you’re not shipping the defaults, in the old Mongo days or other scenarios, like a modem, you’ve got insecure defaults. It’s so that you can think about those things prior to shipping, and it doesn’t mean that it has to be the developer necessarily, it’s the team behind development… Which includes developers, but also may include compliance, it may include this MVSP, which is more of a kind of policy in a way of how to secure cloud applications… And as I’m reading this, there’s like security headers in there, there’s how you allow your customers to test the system, there’s all these sort of protocols that are in there, that can be tested… But this is something that the whole team says “Okay, this is what we want to do when it comes to shipping a cloud application that’s secure.” And that happens before development, it happens during development, but prior to production. So it’s more of a when versus a who; that’s kind of how I see your perspective shifting, at least the way you described it.

Mauricio Salatino: Yeah, because the applications and the examples were mostly created around for different conferences where I was presenting. So if I wanted to show, for example, how to orchestrate cloud events, very early on, when nobody was doing that, and Knative was trying to start to provide some of the risk mechanisms, I would just write an application in that repository, link a readme or a step by step tutorial, and then just have all the resources in there.

[15:57] I’ve found quickly enough that that repository is just the main entrypoint for all the repositories that are hosting different services, and different tutorials as well… But that’s kind of like the main entrypoint. That’s why the repository is pretty old; I would say that before that I had the repositories of the services, even before having a single depository for all the tutorials. And most of those services, if you look at my GitHub repository, I have like 450 repositories in there. And most of those are tutorials, or examples that I’ve used to present in different conferences around different years.

Regarding that, it’s funny enough - this year, if I manage to get into a conference in Barcelona that is called JBCNConf, it will be my tenth year in a row presenting in that conference.

Gerhard Lazu: So if we already emit logs to Grafana Cloud, and we emit metrics to Grafana Cloud, how do we make use of those with Grafana OnCall?

Jacob DePriest: [01:11:52.19] That’s a great question. I think it goes back to what we were talking about earlier, to be honest. I think that today there is a lot of variation and freedom… Which is a good thing, to be clear. I’m not suggesting we take that away. But there’s not necessarily clear, paved paths for open source developers, hobbyists, even more corporation-backed open source efforts, to know what the best practices are for building, securing, deploying, attesting, signing… It’s complicated, right? And I’ve been in this space for a really long time, and so when I rattle some of these things off it, it may feel like “Oh, yeah, okay, cool. That’s “easy”, but it’s not. We didn’t have SLSA frameworks 10-15 years ago. The frameworks and the thinking are there, but I don’t know that as an ecosystem - and this is beyond GitHub; GitHub is part of that ecosystem - makes it really easy for people to do the right thing. So build the right way, secure it, update it, patch it, deploy it, assign it… Like, that end-to-end flow is still complicated. People use – maybe they store their source code on GitHub, but they build it somewhere else. And then when they build it somewhere else, they’re not scanning it, or that place isn’t secure. And then when they upload it, we don’t have any way to see where it came from. And then when we download it on the other side, we don’t really have a way to automatically get a sense of the risk, because it’s difficult to tie all those things together.

And so I think if I could wave a magic wand, it would be essentially to have OS partners in industry - I think GitHub’s part of this - to make those things easier for developers to just do the right thing out of the box. And then, of course, have the freedoms if they need to do something more complex or different; that’s totally fine. But I think a lot of use cases just want to know “Okay, how do I build this thing and deploy it to this cloud provider? How do I build this thing and make it show up in PyPI, and have it trusted with a little badge on it?” And I think to do that well takes a significantly higher amount of work and expertise than would be optimal if we really want to scale this.

Kelsey Hightower: If you’re a fractional CTO, you might come in and say “Hey, here’s how you lay out your two-year plan for spending money in the cloud. Here’s how you negotiate discounts.” You wouldn’t believe most people don’t know how to negotiate a cloud contract. So you can hire someone that can save you 30%. That’s a good deal. Maybe you’re not ready for a full-time CTO, so you bring in someone that has that function… Some people just need an outbound CTO that could talk to customers. “Hey, this is what we’re doing. Here’s the vision. Here’s the story.” Maybe you don’t have anyone on your team that can do that.

My advisor is more like “Kelsey, we’ve seen your work. We know what you do. Your credentials are valid for us. We can’t afford to hire you though. But we would love some input on what we’re doing. We’ve got developers, they do a good job, we have some serial founders, they do a good job… But we’re missing something.” And sometimes it’s as simple as “Give me the story.” And I’m like “That’s not the story.” And I play back the story for them, and they’re like “We never positioned what we were building in that way”, and they go test it with customers. They go test it at the conferences, and they come back like “Kelsey, people get it now. We’re going to rename the product. We’re going to make a little pivot on how we approach this.” How much is that worth, if you have only two years of runway? If you’re out here telling the wrong story for a year and a half, you’ve only got six more months before it’s over. So getting the story right within three months means a lot.

[00:20:19.02] I’d rather people understand what we’re doing, and they just don’t want to buy, versus they don’t even understand what we’re doing, so we can’t even get to a conversation about what to buy. And that is surprisingly big for a large set of companies that are out here, that no one knows what the hell they do, and they run out of money before they can help them understand.

Autumn Nash: How do you build the trust for them to take that PR though? Because also just - I mean, giving the control away to automate things by other people… You know what I mean? Like, when you’ve been running the Bash scripts, and you’ve been running your infrastructure a certain way… Especially if you’ve been running on AWS, and now you’re learning how to run hybrid cloud. Or you’ve been running with TerraForm or Ansible, and you’ve done all this automation, and now you’re kind of trusting people… I feel like it’s hard, even in open source, where there’s a governing body of some sorts - like, you’ve got maintainers - to get people to trust the next upgrade, or the next change, or the next, you know…

Tom Pansino: So I think especially with Terraform and things like this, having layers where we say “This is platform; this is then another layer that’s maybe team specific, that’s like, these are resources that change infrequently. Maybe we put the database there”, put your networking stack there if it’s not part of your platform, put your CDN configuration, your S3 buckets, put those stateful resources… We call that the static layer; the layer that changes very infrequently.

And then we have the dynamic layer, which is where - if I’m a developer and I’m pushing up a feature fix, I want to get a really quick deploy on the platform using the cloud environment to see how it performs. And we write some of our pipelines so that we can have multiple copies of the application stack, multiple features being worked on at the same time. And the majority of our teams have something like that. It’s analogous to what you’re talking about with your dev, using local as a dev. I’ve worked in an environment like that, which is really great. One of the challenges of that - this one of the tradeoffs - is that you don’t get to see how does it perform in the cloud environment.

Now, with Docker, that’s pretty given these days. If it performs locally on the laptop, it’s probably going to work well in the cloud. But there are certain features that maybe Docker is not able to replicate, like SQS or SageMaker, how those things are going to perform.

And so sometimes having a way – in the cloud environment, having a sandbox area can be really helpful. We don’t mandate that teams use all of our dev and stage and prod pattern. The only thing we mandate is that you have a production account, and that you need to have some way of doing things outside of production to be able to test with as well. But that’s a really example of how to approach a problem; if you trust your observability, then why not push straight to production? I think that’s really compelling, and I’m going to get some mileage out of that, talking with some of my team members. Thanks.

David Yakobovitch: Yeah, especially with financial services… I’ve seen that reemerge over the years, and… Although that’s a tangent, but I think that’s interesting, because as everyone’s moving to cloud, it’s still “How do we maintain these systems with these languages?” But I love it, because – I’ll go back into the background, but when I teach a lot today, I tell the students “Hey, if you wanna work in Jupyter, if you wanna work in an IDE, guess what - it supports Fortran and COBOL. So you can always pick up those old languages.”

Adam Stacoviak: No EC2… I’m just kidding. I’m just hating a little bit. We’re not paid to hate. We’ve just got a little bit of hate in our hearts, okay? That’s how it works. That’s cool… I had to learn how to self-host in the cloud better, which I don’t have experience there. My confidence would come from past success. I don’t have past success.

Spencer Kimball: Yeah. It was like 5 megabytes what you got before, which you filled up with one mp3 somebody sent you, or whatever; a couple photos. So this is a huge innovation, obviously; it just set a new standard for what web mail should feel like. And while Gmail is free, if you want a hundred gigabytes, you pay for that extra storage space. That’s exactly how Cockroach Cloud is going to feel to a developer. We wanna make perpetually free relational databases that are the seed of an extraordinarily powerful production database. Something that can scale to run retail banking for the world’s largest banks, that has geo-replication for a high level of resilience, and that is capable of truly global operations, so that even a startup could use the free tier of Cockroach Cloud and store data for customers in Brazil, in Brazil. Store data for customers in Japan, in Japan, and give them a local experience. That’s how big tech builds services and applications. We really wanna make that so that every company in the world, even every developer, even in a hackathon, can build that way. And it’s ideally easier to build that way than it is to stand something up yourself in a single availability zone.

Gerhard Lazu: So one of the things that we wanted to do - I think we were mentioning this towards the beginning of the show… We were saying how Erlang 24 just shipped. It was a few weeks ago, the final 24 release. We have the first patch release… And we wanted to upgrade the Changelog app to use Erlang 24. So here’s the plan… By the time you’re listening to this, either next day or a few days after, we will be performing a live upgrade on the Changelog.com website, from Erlang 23 to Erlang 24. We have PromEx running, we have all the metrics, and we will see live what difference Erlang 24 makes to Changelog.com.

[39:40] PromEx is obviously instrumental, all the metrics and all the logs get shipped to Grafana Cloud, so that’s how we will be observing things, and we will be commenting out what is different, what is better, what is worse. So with that in mind, I’m wondering if there’s any assumptions or expectations that we can set ahead of time. What are you thinking, Alex?

Spencer Kimball: Yeah. It was like 5 megabytes what you got before, which you filled up with one mp3 somebody sent you, or whatever; a couple photos. So this is a huge innovation, obviously; it just set a new standard for what web mail should feel like. And while Gmail is free, if you want a hundred gigabytes, you pay for that extra storage space. That’s exactly how Cockroach Cloud is going to feel to a developer. We wanna make perpetually free relational databases that are the seed of an extraordinarily powerful production database. Something that can scale to run retail banking for the world’s largest banks, that has geo-replication for a high level of resilience, and that is capable of truly global operations, so that even a startup could use the free tier of Cockroach Cloud and store data for customers in Brazil, in Brazil. Store data for customers in Japan, in Japan, and give them a local experience. That’s how big tech builds services and applications. We really wanna make that so that every company in the world, even every developer, even in a hackathon, can build that way. And it’s ideally easier to build that way than it is to stand something up yourself in a single availability zone.

Adam Stacoviak: Essentially, how can we demonstrate to cloud providers the safety of deploying Redox? Maybe it’s answered somewhere, but here’s a contributor…

Andrew Poydence: Bosh Deployment is the new project name for it, and they have a virtual box kind of CPI for that, so… CPI is a cloud provider interface, which is a Bosh term for basically how Bosh speaks to a cloud API.

Shay Banon: Yeah. I mean, we had a great story around how to get people on our cloud, we had a great story around like commercial features on prem… The whole business model that around open source that I think we were a big part in building as a company and as a community, with other open source projects… Like, this worked. You don’t want that headache.

Steve Tuck: We had one company that we were talking to in the finance space that was like “We have a 500-person engineering operations team, and we have to put them out of business, because our customers don’t – and not get rid of them; we need to reapply those folks to be able to work on the things that our customers are waiting for, and want.” But it is folks that are not gonna necessarily sign the PO, but they’re the ones that are making the noise to get to the folks that do sign the POs. And it’s been great to have that kind of community support. And it is that clarifying time when companies have moved certain things to the public cloud, and realized how much less operational overhead there is, to help sharpen, like “Wait a minute… How come we can’t have that same operational efficiency internally?” Back to like the CIO and the CTO; it’s like, “Wait, we can vastly improve being able to focus our talented folks on our business, and then give those developers a much better developer experience.” Which - I think that’s kind of the all-important bit. The amount of importance placed on shipping new features, shipping new products. Focusing on what their actual business is has been super-important.

Nikita Shamgunov: [08:11] You know, similar things, right? So you deploy your app into Fly, and they are able to deploy that app around the world. They don’t do serverless, but I think they will over time. They already have machines that can scale down to zero, and stuff like that. So now the question is, “Can we can we have that in a completely elastic way over time?” This scaling down to zero is a big deal, for all the things that we’ve talked about before.

Finally, there’s the database. You have a frontend and a backend database. That’s the majority of the apps that need all three. Now, the tricky thing about databases is - well, you either build a completely new one from scratch - you know, DynamoDB, or something - or you take advantage of something that is extremely popular, like Postgres, but then it’s much trickier to make it serverless, because Postgres is a package; it has storage, compute, metadata, all in one box. And then in order to make it serverless, you need to cut the system in the right way; and what we did - we separated storage and compute.

The adoption has been phenomenal. And when we announced the system, just in June, we now have close to 10,000 users coming into the platform and signing up for this system. And we haven’t even lifted the invite gates. So we are onboarding people in batches, and we’re seeing a lot of interest of people coming into the platform and using the system. Granted, all of that is free right now, which is attracting a lot of tire kickers and people who are just trying things out… But we are in communication with those folks. They’re filling up surveys and we are engaging with them directly… And so we see a lot of excitement around serverless. That excitement can be probably split in three categories. The first one is “I’m an indie developer. I just want something cheap or free” or whatever. And some of that is a Heroku fallout as well. Another use case is, “Well, I’m doing a lot of software development. I need this developer environment.” So that’s where scaling to zero, branching (it’s another thing that we bring to the table) allows you to very easily create developer environments and don’t sweat bullets that you can just like over-create those developer environments and forget to turn them off, because they all scale to zero.

And finally, we see professional, like bigger organizations that are saying, “Well, we are an RDS, but it’s getting extremely hard to deal with Amazon. We just want simpler, we need more reliable, and we need something that plugs into the next-generation infrastructure”, which is the Vercels of the world, which is AWS Lambda, and which is something like Fly as well. So that’s where we see kind of the categories of people coming in.

There are other serverless offerings on the market, I think namely Planet Scale and Aurora. When I started the company, I had a little bit of an insight into AWS Aurora, and they always track; they build something and they see how much of an impact this is to the overall business. And when they shipped Aurora serverless v1, which is their first implementation - now they’re on the v2, which by the way, doesn’t scale all the way to zero… But that thing took off like there was no tomorrow for Aurora. So that was a big deal and a signal for me then, figuring out how to build a dominant OLTP cloud database.

Tammer Saleh: There’s a variety of tools for deploying Kubernetes to bare metal installations. And that’s not really the hard part with Kubernetes. In the cloud, there’s managed Kubernetes, and that solves all your problems. That’s not the problem with Kubernetes in complexity. In fact, getting a Kubernetes cluster up and running is fairly easy. On bare metal, you have some issues with the networking, but there’s projects to solve there. You’ve got kube-router, and you’ve got MetalLB, and you’ve got others that solve that problem for you.

[15:56] It’s interesting that you brought up BOSH and Cloud Foundry. For those who don’t know, the way that Cloud Foundry was designed was that we had two different products. We had BOSH, which was sort of a competitor to Terraform and Ansible and Salt. I don’t know this for sure, but I think it came right out of the Google’s Borg. It’s like a rewrite of Borg, basically. And it’s very difficult to use. But once you use it, once you learn it, Stockholm Syndrome kicks in and you start to love it. There’s huge BOSH fanatics, right? And BOSH was the tool that the operator used to deploy Cloud Foundry; very difficult to use, but very powerful. And Cloud Foundry was the interface that the operator then could present to the application developers, which was basically a blatant rip-off of Heroku, which was a great model. Twelve-factor buildpacks, all that stuff made it really easy for application developers. But here’s the interesting thing - I refer to that as the great wall DevOps model where Cloud Foundry allowed the operator to serve the application developer well by giving the operator this beautiful wall that both sides really appreciated. The operator appreciated how easy it was to manage Cloud Foundry through BOSH, and the application developer appreciated how powerful it was for them to manage their application through Cloud Foundry.

Kubernetes is entirely different from that, right? Kubernetes is what I call the kumbaya DevOps model where everybody has to know everything, right? Kubernetes doesn’t have the concept of an operator, versus an application developer. At best, it gives you some tools where you can kind of build that using rbacks and stuff, but that’s really difficult to do. And nobody knows quite where the line is supposed to be. Yeah, so everybody does it differently, you know?

Adam Stacoviak: I know we’re camping out tremendously in this lesson, which really is just the beginning of this mountain that you’ve climbed and the value that you’ve brought to market. Laser-focused on software developers, enabling software developers to be able to do things that they weren’t able to do before because of the cloud, because of the need to take applications of the cloud; how can you do that with identity, with security, with all those things in mind.

You’d mentioned the most recent funding round, which you said you got a lot of no’s to, or at least a few, led by Salesforce Ventures…

Paul Biggar: Yeah. The ideas behind Dark came when I was working at CircleCI. There’s a lot of seeing the world adopt for continuous delivery, and looking at the programming languages we use, and how difficult it is to add cloud into them, looking at how teams build things, and looking at how hard it is to build your own internal tools, because every internal tool that you build is infrastructure that you now need to support and maintain etc. So all these ideas were sort of fomenting in my head around 2013-2015.

Then I left Circle in late 2015. I remember I was massively burnt out; startups are hard, and I made lots of mistakes as a first-time founder, that led to more incredible burnouts… So I took 18 months off. Then towards the end of that I started thinking “What is it that I’m going to do next?” A lot of the things that I thought might be cool to do, a lot of startups that I thought might be fun to build, that kind of thing - they all had this infrastructure component; if I’m gonna build a small company, a one-person company, a two-person, a five-person company, whatever, I don’t want to spend all of my time dealing with AWS, and that kind of thing. So all these ideas just kept pointing me back to this other idea I had, which was Dark… So it kind of got to the point where I just couldn’t stop thinking about it, so that’s what I’ve decided to do next.

Emily Robinson: I think one thing that is distinct is that there’s not necessarily this well-trodden path, in that the field is not as well defined. That can mean, for example – interviews at a company can just totally run the gamut. For computer science folks, for example, you’re trying to get into a software engineering job, there’s a book “Cracking the Coding Interview”, there’s tons of resources out there, like “What questions do Google ask, or Facebook?” if you look at bigger companies, and even smaller companies have kind of adopted this.

[11:53] But in data science right now, you might have one company that doesn’t give you any coding in your interview. And another one has you whiteboard code. And another one has a take-home project to do in Python. And another one asks you to derive something mathematical while you’re in the interview. Another one has you do consulting-type problems. I think as a field, that can make it really challenging to enter… That’s coming from – data science is such a broad field, and there’s so many different parts to it, and it’s very easy to feel impostor syndrome, because you know, how are you ever gonna know it? And the answer is “Well, no one knows it.” And because it’s all these separate, overlapping fields that are very deep in their own right, I think it can be quite intimidating, especially when you come up with lists of, you know, “True data scientists need to know these ten algorithms, and they know how to deploy things in the cloud, and they are an expert at managing stakeholder relationships, and have a degree in math.”

Craig Wiley: [31:42] Right. Technologies like that start to diminish the damage of saying “Hey, I can only have my data here, and I can’t move it elsewhere”, kind of thing. So in one direction, their strategy is like that. And then in the other direction, as cloud continues to grow - and I will routinely meet companies who will say “Well, my industry (and you can fill in with whatever industry) doesn’t allow me to put data in the cloud.” We’ll talk to them about how many references we have in their industry who are putting data in the cloud, and very quickly they realize that it’s not their industry, it’s their company or even their department that has built these rules, probably years ago, before it was understood the benefits of things like cloud security and reliability and resiliency.

I think those kinds of blockers are falling by the wayside, either because companies are realizing that the cloud is not as scary as maybe they imagined, and then secondly because the cloud is coming to the customers in a big way.

Gregory Kurtzer: For Singularity, we never considered it. For our cloud development – I mean, it is a cloud service, so that’s not something that we’re open sourcing. So that is definitely a closed source. But we’ve actually gotten some really interesting information, which is - everybody’s talking about cloud, everybody’s talking about getting all their apps and everything up to the cloud, and so we developed this cloud service thinking everybody wants to go up to the cloud. A really interesting spin on this, and something I totally didn’t expect, is that almost everybody - well, maybe not everybody; I’m exaggerating… Maybe about half of the people that we talk to wants to run that on-prem, and I wasn’t expecting that.

So we are figuring out how to relicense and rebrand our cloud services, and allow people to run those on-prem. And we have several different cloud services at this point. The key store that I was mentioning before is just one of them. Another one is a build service, so you can actually build containers without requiring root or without requiring any sort of privilege escalation, because we have a service that does that in a controlled way. We also have something called the container library.

Now, in compute - and I’ll give a little bit of background on this - there is various regulations. In pharma, for example, on the science side and the bio side, they have various FDA regulations even, that they actually have to manage the environments for any software that contributes to a diagnosis of a medical issue. Anytime software is involved in that, that entire stack has to be treated as a medical device, and a medical device has to be archival for 5 to 10 years, meaning we have to be able to reproduce those results and reproduce that environment for 5 to 10 years. Well, Singularity provides a perfect solution for that.

[47:59] Our container library, one of our services, is kind of built around the idea of “What are the specific benefits of this binary image format that we have, and how do we archive and always allow people to go back to previous workflows?” and so on and so forth. That’s a feature that we have in this container library.

The other one is - you know, a lot of people talk about DevOps, and they pass recipes around; they pass the source code, in a manner of speaking, around, or their environment. Well, again, because our containers are 100% immutable and cryptographically verifiable, why don’t we just pass the container around? And that way you never have to rebuild it. So it goes from the developer, you build that binary container, and then it can run through a DevOps pipeline that can be completely built up for whatever the pipeline is, and customized via CI/CD integrations and whatnot, and then come out the other side as “This binary immutable image has passed all of this.” This gives us the ability now to inject things like the security teams into the DevOps workflow… Because once this pipeline, once this container image is going through this pipeline and it gets to security, and security is like “Okay, I’ve audited this. I feel good about it. I’m gonna now sign it”, and then continue it on its way - well, we just added a cryptographic signature on it that wherever you run in production, you can say “I’m never gonna trust this container unless it has this key fingerprint. And if it has this fingerprint that your security team owns, then I will allow it to run in production.”

So it gives us the ability to do things like inject security back into the DevOps workflow, and it changes things. It changes how we’re doing this.

Julia White: I’m sure it’s right, but… But you know, the worldview that he’s created, of intelligent cloud, intelligent edge, and he spent some time on this keynote talking about intelligent cloud, intelligent edge, and I’ve gotten a lot of questions like “Okay, so is that IoT? Does that mean hybrid? What exactly is that thing that he’s talking about?” So I tried to break it down for folks… It literally is his point of view and a shared point of view that every application type that we build moving forward will be this combination of the technology that is the public cloud, and the technology like compute and data sitting on these edge nodes; and the edge nodes, if you think about it today, the biggest edge nodes are these giant data centers, or technologies running, and they’re using the cloud, and most people call that hybrid.

[44:12] But that data center is gonna evolve into a huge set of distributed connected devices, from cars to tiny little sensors in refrigerators and thermostats and that type of thing, and each one of those will hold application code, and will be running local processing, local compute and AI and make meaningful things, not just dumb sensors that ping back to the cloud, and just ping on a regular basis.

So the edge is gonna become maybe just as powerful as a data center, but a far more distributed technology set… So how do we think about it as a developer, how do you think about “Okay, my world is gonna look like that, and every application is gonna have a cloud and edge component to it. How do I start thinking about that?” And the most obvious way that happens today is IoT. So we bring it right back to like “Okay, is IoT, is this a use case people can kind of get their head around today, right now, and understand it?” But I do believe that IoT and edge will converge over time into this – just an example of what this intelligent edge looks like.

Tim Hockin: Yeah, sure. Early on there was a prototype that was put together by Joe Bada, Brendan Burns and some other engineers inside Google, just to prove out some of the ideas that we had pushed forward through Omega. For history, Brian Grant was the lead developer, designer of Omega and he had some interesting ideas that were different from Borg. So Brendan and Joe and Craig and Ville took those ideas and they sort of glued them together with some Go and some shell script and some Docker and some Salt, and they took all these open source things and they threw them together into a very rough prototype, and they showed us at one of our joint cloud infrastructure meetings, and my jaw hit the floor. It was like “This is awesome. This is amazing. It’s a little rough around the edges, but that’s incredible.”

Then it sort of sat on the shelf while we tried to figure out what to do with it. Docker was this brand new thing, we didn’t know if it was really gonna take off or not, we weren’t sure how we were going to staff this, the organization wasn’t really behind it, cloud was still very immature at Google, and we had a bunch of other sort of false starts on how to make containers in Google Cloud a better place. It was later in 2013 when we said “We really think the answer here is to build this Kubernetes thing.” It wasn’t called Kubernetes at the time, but we decided to take this thing off the shelf and dust it off and make it into a real product. So we started on the process of that and we did an internal product requirements doc and we brought it to our VPs and we showed them the ideas and we made our pitches, and we understood their concerns about what was giving away the golden goose and what was okay to talk about and release. We had sort of our rules of engagement, and that was how we got towards the Kubernetes announcement that we made at DockerCon 1.