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We were deeply saddened to learn that Khaled Bichara, one of Oxide’s angel investors, died in a car accident in Cairo on Friday night. Those of us who have known Khaled for years have known him to be a bold investor who appreciated hard technical problems – and also a profoundly decent person, who cared deeply for his family, his companies, and his country. Khaled’s stories of kindling entrepreneurship in Egypt were inspiring to any who heard them – and served as a reminder of the reponsibility that we all have to our broader communities. Our most recent conversation with Khaled – just a few short months ago – remains vivid: he was excited by our technical vision and thrilled to be a part of Oxide. For our part, we were looking forward to a long journey together, and to making good on his belief in us; we are gutted to have lost him so abruptly. Our heart goes out to his family, and to all those who he touched over his career; Silicon Valley may be a long way from Cairo, but his impact...
over a year ago

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More from Oxide Computer Company Blog

Our $100M Series B

We don’t want to bury the lede: we have raised a $100M Series B, led by a new strategic partner in USIT with participation from all existing Oxide investors. To put that number in perspective: over the nearly six year lifetime of the company, we have raised $89M; our $100M Series B more than doubles our total capital raised to date — and positions us to make Oxide the generational company that we have always aspired it to be. If this aspiration seems heady now, it seemed absolutely outlandish when we were first raising venture capital in 2019. Our thesis was that cloud computing was the future of all computing; that running on-premises would remain (or become!) strategically important for many; that the entire stack — hardware and software — needed to be rethought from first principles to serve this market; and that a large, durable, public company could be built by whomever pulled it off. This scope wasn’t immediately clear to all potential investors, some of whom seemed to latch on to one aspect or another without understanding the whole. Their objections were revealing: "We know you can build this," began more than one venture capitalist (at which we bit our tongue; were we not properly explaining what we intended to build?!), "but we don’t think that there is a market." Entrepreneurs must become accustomed to rejection, but this flavor was particularly frustrating because it was exactly backwards: we felt that there was in fact substantial technical risk in the enormity of the task we put before ourselves — but we also knew that if we could build it (a huge if!) there was a huge market, desperate for cloud computing on-premises. Fortunately, in Eclipse Ventures we found investors who saw what we saw: that the most important products come when we co-design hardware and software together, and that the on-premises market was sick of being told that they either don’t exist or that they don’t deserve modernity. These bold investors — like the customers we sought to serve — had been waiting for this company to come along; we raised seed capital, and started building. And build it we did, making good on our initial technical vision: We did our own board designs, allowing for essential system foundation like a true hardware root-of-trust and end-to-end power observability. We did our own microcontroller operating system, and used it to replace the traditional BMC. We did our own platform enablement software, eliminating the traditional UEFI BIOS and its accompanying flotilla of vulnerabilities. We did our own host hypervisor, assuring an integrated and seamless user experience — and eliminating the need for a third-party hypervisor and its concomitant rapacious software licensing. We did our own switch — and our own switch runtime — eliminating entire universes of integration complexity and operational nightmares. We did our own integrated storage service, allowing the rack-scale system to have reliable, available, durable, elastic instance storage without necessitating a dependency on a third party. We did our own control plane, a sophisticated distributed system building on the foundation of our hardware and software components to deliver the API-driven services that modernity demands: elastic compute, virtual networking, and virtual storage. While these technological components are each very important (and each is in service to specific customer problems when deploying infrastructure on-premises), the objective is the product, not its parts. The journey to a product was long, but we ticked off the milestones. We got the boards brought up. We got the switch transiting packets. We got the control plane working. We got the rack manufactured. We passed FCC compliance. And finally, two years ago, we shipped our first system! Shortly thereafter, more milestones of the variety you can only get after shipping: our first update of the software in the field; our first update-delivered performance improvements; our first customer-requested features added as part of an update. Later that year, we hit general commercial availability, and things started accelerating. We had more customers — and our first multi-rack customer. We had customers go on the record about why they had selected Oxide — and customers describing the wins that they had seen deploying Oxide. Customers starting landing faster now: enterprise sales cycles are infamously long, but we were finding that we were going from first conversations to a delivered product surprisingly quickly. The quickening pace always seemed to be due in some way to our transparency: new customers were listeners to our podcast, or they had read our RFDs, or they had perused our documentation, or they had looked at the source code itself. With growing customer enthusiasm, we were increasingly getting questions about what it would look like to buy a large number of Oxide racks. Could we manufacture them? Could we support them? Could we make them easy to operate together? Into this excitement, a new potential investor, USIT, got to know us. They asked terrific questions, and we found a shared disposition towards building lasting value and doing it the right way. We learned more about them, too, and especially USIT’s founder, Thomas Tull. The more we each learned about the other, the more there was to like. And importantly, USIT had the vision for us that we had for ourselves: that there was a big, important market here — and that it was uniquely served by Oxide. We are elated to announce this new, exciting phase of the company. It’s not necessarily in our nature to celebrate fundraising, but this is a big milestone, because it will allow us to address our customers' most pressing questions around scale (manufacturing scale, system scale, operations scale) and roadmap scope. We have always believed in our mission, but this raise gives us a new sense of confidence when we say it: we’re going to kick butt, have fun, not cheat (of course!), love our customers — and change computing forever.

a week ago 17 votes
Oxide’s Compensation Model: How is it Going?

How it started Four years ago, we were struggling to hire. Our team was small (~23 employees), and we knew that we needed many more people to execute on our audacious vision. While we had had success hiring in our personal networks, those networks now felt tapped; we needed to get further afield. As is our wont, we got together as a team and brainstormed: how could we get a bigger and broader applicant pool? One of our engineers, Sean, shared some personal experience: that Oxide’s principles and values were very personally important to him — but that when he explained them to people unfamiliar with the company, they were (understandably?) dismissed as corporate claptrap. Sean had found, however, that there was one surefire way to cut through the skepticism: to explain our approach to compensation. Maybe, Sean wondered, we should talk about it publicly? "I could certainly write a blog entry explaining it," I offered. At this suggestion, the team practically lunged with enthusiasm: the reaction was so uniformly positive that I have to assume that everyone was sick of explaining this most idiosyncratic aspect of Oxide to friends and family. So what was the big deal about our compensation? Well, as a I wrote in the resulting piece, Compensation as a Reflection of Values, our compensation is not merely transparent, but uniform. The piece — unsurprisingly, given the evergreen hot topic that is compensation — got a ton of attention. While some of that attention was negative (despite the piece trying to frontrun every HN hater!), much of it was positive — and everyone seemed to be at least intrigued. And in terms of its initial purpose, the piece succeeded beyond our wildest imagination: it brought a surge of new folks interested in the company. Best of all, the people new to Oxide were interested for all of the right reasons: not the compensation per se, but for the values that the compensation represents. The deeper they dug, the more they found to like — and many who learned about Oxide for the first time through that blog entry we now count as long-time, cherished colleagues. That blog entry was a long time ago now, and today we have ~75 employees (and a shipping product!); how is our compensation model working out for us? How it’s going Before we get into our deeper findings, two updates that are so important that we have updated the blog entry itself. First, the dollar figure itself continues to increase over time (as of this writing in 2025, $207,264); things definitely haven’t gotten (and aren’t getting!) any cheaper. And second, we did introduce variable compensation for some sales roles. Yes, those roles can make more than the rest of us — but they can also make less, too. And, importantly: if/when those folks are making more than the rest of us, it’s because they’re selling a lot — a result that can be celebrated by everyone! Those critical updates out of the way, how is it working? There have been a lot of surprises along the way, mostly (all?) of the positive variety. A couple of things that we have learned: People take their own performance really seriously. When some outsiders hear about our compensation model, they insist that it can’t possibly work because "everyone will slack off." I have come to find this concern to be more revealing of the person making the objection than of our model, as our experience has been in fact the opposite: in my one-on-one conversations with team members, a frequent subject of conversation is people who are concerned that they aren’t doing enough (or that they aren’t doing the right thing, or that their work is progressing slower than they would like). I find my job is often to help quiet this inner critic while at the same time stoking what I feel is a healthy urge: when one holds one’s colleagues in high regard, there is an especially strong desire to help contribute — to prove oneself worthy of a superlative team. Our model allows people to focus on their own contribution (whatever it might be). People take hiring really seriously. When evaluating a peer (rather than a subordinate), one naturally has high expectations — and because (in the sense of our wages, anyway) everyone at Oxide is a peer, it shouldn’t be surprising that folks have very high expectations for potential future colleagues. And because the Oxide hiring process is writing intensive, it allows for candidates to be thoroughly reviewed by Oxide employees — who are tough graders! It is, bluntly, really hard to get a job at Oxide. It allows us to internalize the importance of different roles. One of the more incredible (and disturbingly frequent) objections I have heard is: "But is that what you’ll pay support folks?" I continue to find this question offensive, but I no longer find it surprising: the specific dismissal of support roles reveals a widespread and corrosive devaluation of those closest to customers. My rejoinder is simple: think of the best support engineers you’ve worked with; what were they worth? Anyone who has shipped complex systems knows these extraordinary people — calm under fire, deeply technical, brilliantly resourceful, profoundly empathetic — are invaluable to the business. So what if you built a team entirely of folks like that? The response has usually been: well, sure, if you’re going to only hire those folks. Yeah, we are — and we have! It allows for fearless versatility. A bit of a corollary to the above, but subtly different: even though we (certainly!) hire and select for certain roles, our uniform compensation means we can in fact think primarily in terms of people unconfined by those roles. That is, we can be very fluid about what we’re working on, without fear of how it will affect a perceived career trajectory. As a concrete example: we had a large customer that wanted to put in place a program for some of the additional work they wanted to see in the product. The complexity of their needs required dedicated program management resources that we couldn’t spare, and in another more static company we would have perhaps looked to hire. But in our case, two folks came together — CJ from operations, and Izzy from support — and did something together that was in some regards new to both of them (and was neither of their putative full-time jobs!) The result was indisputably successful: the customer loved the results, and two terrific people got a chance to work closely together without worrying about who was dotted-lined to whom. It has allowed us to organizationally scale. Many organizations describe themselves as flat, and a reasonable rebuttal to this are the "shadow hierarchies" created by the tyranny of structurelessness. And indeed, if one were to read (say) Valve’s (in)famous handbook, the autonomy seems great — but the stack ranking decidedly less so, especially because the handbook is conspicuously silent on the subject of compensation. (Unsurprisingly, compensation was weaponized at Valve, which descended into toxic cliquishness.) While we believe that autonomy is important to do one’s best work, we also have a clear structure at Oxide in that Steve Tuck (Oxide co-founder and CEO) is in charge. He has to be: he is held accountable to our investors — and he must have the latitude to make decisions. Under Steve, it is true that we don’t have layers of middle management. Might we need some in the future? Perhaps, but what fraction of middle management in a company is dedicated to — at some level — determining who gets what in terms of compensation? What happens when you eliminate that burden completely? It frees us to both lead and follow. We expect that every Oxide employee has the capacity to lead others — and we tap this capacity frequently. Of course, a company in which everyone is trying to direct all traffic all the time would be a madhouse, so we also very much rely on following one another too! Just as our compensation model allows us to internalize the values of different roles, it allows us to appreciate the value of both leading and following, and empowers us each with the judgement to know when to do which. This isn’t always easy or free of ambiguity, but this particular dimension of our versatility has been essential — and our compensation model serves to encourage it. It causes us to hire carefully and deliberately. Of course, one should always hire carefully and deliberately, but this often isn’t the case — and many a startup has been ruined by reckless expansion of headcount. One of the roots of this can be found in a dirty open secret of Silicon Valley middle management: its ranks are taught to grade their career by the number of reports in their organization. Just as if you were to compensate software engineers based on the number of lines of code they wrote, this results in perverse incentives and predictable disasters — and any Silicon Valley vet will have plenty of horror stories of middle management jockeying for reqs or reorgs when they should have been focusing on product and customers. When you can eliminate middle management, you eliminate this incentive. We grow the team not because of someone’s animal urges to have the largest possible organization, but rather because we are at a point where adding people will allow us to better serve our market and customers. It liberates feedback from compensation. Feedback is, of course, very important: we all want to know when and where we’re doing the right thing! And of course, we want to know too where there is opportunity for improvement. However, Silicon Valley has historically tied feedback so tightly to compensation that it has ceased to even pretend to be constructive: if it needs to be said, performance review processes aren’t, in fact, about improving the performance of the team, but rather quantifying and stack-ranking that performance for purposes of compensation. When compensation is moved aside, there is a kind of liberation for feedback itself: because feedback is now entirely earnest, it can be expressed and received thoughtfully. It allows people to focus on doing the right thing. In a world of traditional, compensation-tied performance review, the organizational priority is around those things that affect compensation — even at the expense of activity that clearly benefits the company. This leads to all sorts of wild phenomena, and most technology workers will be able to tell stories of doing things that were clearly right for the company, but having to hide it from management that thought only narrowly in terms of their own stated KPIs and MBOs. By contrast, over and over (and over!) again, we have found that people do the right thing at Oxide — even if (especially if?) no one is looking. The beneficiary of that right thing? More often than not, it’s our customers, who have uniformly praised the team for going above and beyond. It allows us to focus on the work that matters. Relatedly, when compensation is non-uniform, the process to figure out (and maintain) that non-uniformity is laborious. All of that work — of line workers assembling packets explaining themselves, of managers arming themselves with those packets to fight in the arena of organizational combat, and then of those same packets ultimately being regurgitated back onto something called a review — is work. Assuming such a process is executed perfectly (something which I suppose is possible in the abstract, even though I personally have never seen it), this is work that does not in fact advance the mission of the company. Not having variable compensation gives us all of that time and energy back to do the actual work — the stuff that matters. It has stoked an extraordinary sense of teamwork. For me personally — and as I relayed on an episode of Software Misadventures — the highlights of my career have been being a part of an extraordinary team. The currency of a team is mutual trust, and while uniform compensation certainly isn’t the only way to achieve that trust, boy does it ever help! As Steve and I have told one another more times that we can count: we are so lucky to work on this team, with its extraordinary depth and breadth. While our findings have been very positive, I would still reiterate what we said four years ago: we don’t know what the future holds, and it’s easier to make an unwavering commitment to the transparency rather than the uniformity. That said, the uniformity has had so many positive ramifications that the model feels more important than ever. We are beyond the point of this being a curiosity; it’s been essential for building a mission-focused team taking on a problem larger than ourselves. So it’s not a fit for everyone — but if you are seeking an extraordinary team solving hard problems in service to customers, consider Oxide!

3 months ago 21 votes
dtrace.conf(24)

Sometime in late 2007, we had the idea of a DTrace conference. Or really, more of a meetup; from the primordial e-mail I sent: The goal here, by the way, is not a DTrace user group, but more of a face-to-face meeting with people actively involved in DTrace — either by porting it to another system, by integrating probes into higher level environments, by building higher-level tools on top of DTrace or by using it heavily and/or in a critical role. That said, we also don’t want to be exclusionary, so our thinking is that the only true requirement for attending is that everyone must be prepared to speak informally for 15 mins or so on what they are doing with DTrace, any limitations that they have encountered, and some ideas for the future. We’re thinking that this is going to be on the order of 15-30 people (though more would be a good problem to have — we’ll track it if necessary), that it will be one full day (breakfast in the morning through drinks into the evening), and that we’re going to host it here at our offices in San Francisco sometime in March 2008. This same note also included some suggested names for the gathering, including what in hindsight seems a clear winner: DTrace Bi-Mon-Sci-Fi-Con. As if knowing that I should leave an explanatory note to my future self as to why this name was not selected, my past self fortunately clarified: "before everyone clamors for the obvious Bi-Mon-Sci-Fi-Con, you should know that most Millennials don’t (sadly) get the reference." (While I disagree with the judgement of my past self, it at least indicates that at some point I cared if anyone got the reference.) We settled on a much more obscure reference, and had the first dtrace.conf in March 2008. Befitting the style of the time, it was an unconference (a term that may well have hit its apogee in 2008) that you signed up to attend by editing a wiki. More surprising given the year (and thanks entirely to attendee Ben Rockwood), it was recorded — though this is so long ago that I referred to it as video taping (and with none of the participants mic’d, I’m afraid the quality isn’t very good). The conference, however, was terrific, viz. the reports of Adam, Keith and Stephen (all somehow still online nearly two decades later). If anything, it was a little too good: we realized that we couldn’t recreate the magic, and we demurred on making it an annual event. Years passed, and memories faded. By 2012, it felt like we wanted to get folks together again, now under a post-lawnmower corporate aegis in Joyent. The resulting dtrace.conf(12) was a success, and the Olympiad cadence felt like the right one; we did it again four years later at dtrace.conf(16). In 2020, we came back together for a new adventure — and the DTrace Olympiad was not lost on Adam. Alas, dtrace.conf(20) — like the Olympics themselves — was cancelled, if implicitly. Unlike the Olympics, however, it was not to be rescheduled. More years passed and DTrace continued to prove its utility at Oxide; last year when Adam and I did our "DTrace at 20" episode of Oxide and Friends, we vowed to hold dtrace.conf(24) — and a few months ago, we set our date to be December 11th. At first we assumed we would do something similar to our earlier conferences: a one-day participant-run conference, at the Oxide office in Emeryville. But times have changed: thanks to the rise of remote work, technologists are much more dispersed — and many more people would need to travel for dtrace.conf(24) than in previous DTrace Olympiads. Travel hasn’t become any cheaper since 2008, and the cost (and inconvenience) was clearly going to limit attendance. The dilemma for our small meetup highlights the changing dynamics in tech conferences in general: with talks all recorded and made publicly available after the conference, how does one justify attending a conference in person? There can be reasonable answers to that question, of course: it may be the hallway track, or the expo hall, or the after-hours socializing, or perhaps some other special conference experience. But it’s also not surprising that some conferences — especially ones really focused on technical content — have decided that they are better off doing as conference giant O’Reilly Media did, and going exclusively online. And without the need to feed and shelter participants, the logistics for running a conference become much more tenable — and the price point can be lowered to the point that even highly produced conferences like P99 CONF can be made freely available. This, in turn, leads to much greater attendance — and a network effect that can get back some of what one might lose going online. In particular, using chat as the hallway track can be more much effective (and is certainly more scalable!) than the actual physical hallways at a conference. For conferences in general, there is a conversation to be had here (and as a teaser, Adam and I are going to talk about it with Stephen O’Grady and Theo Schlossnagle on Oxide and Friends next week, but for our quirky, one-day, Olympiad-cadence dtrace.conf, the decision was pretty easy: there was much more to be gained than lost by going exclusively on-line. So dtrace.conf(24) is coming up next week, and it’s available to everyone. In terms of platform, we’re going to try to keep that pretty simple: we’re going to use Google Meet for the actual presenters, which we will stream in real-time to YouTube — and we’ll use the Oxide Discord for all chat. We’re hoping you’ll join us on December 11th — and if you want to talk about DTrace or a DTrace-adjacent topic, we’d love for you to present! Keeping to the unconference style, if you would like to present, please indicate your topic in the #session-topics Discord channel so we can get the agenda fleshed out. While we’re excited to be online, there are some historical accoutrements of conferences that we didn’t want to give up. First, we have a tradition of t-shirts with dtrace.conf. Thanks to our designer Ben Leonard, we have a banger of a t-shirt, capturing the spirit of our original dtrace.conf(08) shirt but with an Oxide twist. It’s (obviously) harder to make those free but we have tried to price them reasonably. You can get your t-shirt by adding it to your (free) dtrace.conf ticket. (And for those who present at dtrace.conf, your shirt is on us — we’ll send you a coupon code!) Second, for those who can make their way to the East Bay and want some hangout time, we are going to have an après conference social event at the Oxide office starting at 5p. We’re charging something nominal for that too (and like the t-shirt, you pay for that via your dtrace.conf ticket); we’ll have some food and drinks and an Oxide hardware tour for the curious — and (of course?) there will be Fishpong. Much has changed since I sent that e-mail 17 years ago — but the shared values and disposition that brought together our small community continue to endure; we look forward to seeing everyone (virtually) at dtrace.conf(24)!

8 months ago 90 votes
Advancing Cloud and HPC Convergence with Lawrence Livermore National Laboratory

Oxide Computer Company and Lawrence Livermore National Laboratory Work Together to Advance Cloud and HPC Convergence Oxide Computer Company and Lawrence Livermore National Laboratory (LLNL) today announced a plan to bring on-premises cloud computing capabilities to the Livermore Computing (LC) high-performance computing (HPC) center. The rack-scale Oxide Cloud Computer allows LLNL to improve the efficiency of operational workloads and will provide users in the National Nuclear Security Administration (NNSA) with new capabilities for provisioning secure, virtualized services alongside HPC workloads. HPC centers have traditionally run batch workloads for large-scale scientific simulations and other compute-heavy applications. HPC workloads do not exist in isolation—there are a multitude of persistent, operational services that keep the HPC center running. Meanwhile, HPC users also want to deploy cloud-like persistent services—databases, Jupyter notebooks, orchestration tools, Kubernetes clusters. Clouds have developed extensive APIs, security layers, and automation to enable these capabilities, but few options exist to deploy fully virtualized, automated cloud environments on-premises. The Oxide Cloud Computer allows organizations to deliver secure cloud computing capabilities within an on-premises environment. On-premises environments are the next frontier for cloud computing. LLNL is tackling some of the hardest and most important problems in science and technology, requiring advanced hardware, software, and cloud capabilities. We are thrilled to be working with their exceptional team to help advance those efforts, delivering an integrated system that meets their rigorous requirements for performance, efficiency, and security. — Steve TuckCEO at Oxide Computer Company Leveraging the new Oxide Cloud Computer, LLNL will enable staff to provision virtual machines (VMs) and services via self-service APIs, improving operations and modernizing aspects of system management. In addition, LLNL will use the Oxide rack as a proving ground for secure multi-tenancy and for smooth integration with the LLNL-developed Flux resource manager. LLNL plans to bring its users cloud-like Infrastructure-as-a-Service (IaaS) capabilities that work seamlessly with their HPC jobs, while maintaining security and isolation from other users. Beyond LLNL personnel, researchers at the Los Alamos National Laboratory and Sandia National Laboratories will also partner in many of the activities on the Oxide Cloud Computer. We look forward to working with Oxide to integrate this machine within our HPC center. Oxide’s Cloud Computer will allow us to securely support new types of workloads for users, and it will be a proving ground for introducing cloud-like features to operational processes and user workflows. We expect Oxide’s open-source software stack and their transparent and open approach to development to help us work closely together. — Todd GamblinDistinguished Member of Technical Staff at LLNL Sandia is excited to explore the Oxide platform as we work to integrate on-premise cloud technologies into our HPC environment. This advancement has the potential to enable new classes of interactive and on-demand modeling and simulation capabilities. — Kevin PedrettiDistinguished Member of Technical Staff at Sandia National Laboratories LLNL plans to work with Oxide on additional capabilities, including the deployment of additional Cloud Computers in its environment. Of particular interest are scale-out capabilities and disaster recovery. The latest installation underscores Oxide Computer’s momentum in the federal technology ecosystem, providing reliable, state-of-the-art Cloud Computers to support critical IT infrastructure. To learn more about Oxide Computer, visit https://oxide.computer. About Oxide Computer Oxide Computer Company is the creator of the world’s first commercial Cloud Computer, a true rack-scale system with fully unified hardware and software, purpose-built to deliver hyperscale cloud computing to on-premises data centers. With Oxide, organizations can fully realize the economic and operational benefits of cloud ownership, with access to the same self-service development experience of public cloud, without the public cloud cost. Oxide empowers developers to build, run, and operate any application with enhanced security, latency, and control, and frees organizations to elevate IT operations to accelerate strategic initiatives. To learn more about Oxide’s Cloud Computer, visit oxide.computer. About LLNL Founded in 1952, Lawrence Livermore National Laboratory provides solutions to our nation’s most important national security challenges through innovative science, engineering, and technology. Lawrence Livermore National Laboratory is managed by Lawrence Livermore National Security, LLC for the U.S. Department of Energy’s National Nuclear Security Administration. Media Contact LaunchSquad for Oxide Computer oxide@launchsquad.com

8 months ago 84 votes
Remembering Charles Beeler

We are heartbroken to relay that Charles Beeler, a friend and early investor in Oxide, passed away in September after a battle with cancer. We lost Charles far too soon; he had a tremendous influence on the careers of us both. Our relationship with Charles dates back nearly two decades, to his involvement with the ACM Queue board where he met Bryan. It was unprecedented to have a venture capitalist serve in this capacity with ACM, and Charles brought an entirely different perspective on the practitioner content. A computer science pioneer who also served on the board took Bryan aside at one point: "Charles is one of the good ones, you know." When Bryan joined Joyent a few years later, Charles also got to know Steve well. Seeing the promise in both node.js and cloud computing, Charles became an investor in the company. When companies hit challenging times, some investors will hide — but Charles was the kind of investor to figure out how to fix what was broken. When Joyent needed a change in executive leadership, it was Charles who not only had the tough conversations, but led the search for the leader the company needed, ultimately positioning the company for success. Aside from his investment in Joyent, Charles was an outspoken proponent of node.js, becoming an organizer of the Node Summit conference. In 2017, he asked Bryan to deliver the conference’s keynote, but by then, the relationship between Joyent and node.js had become…​ complicated, and Bryan felt that it probably wouldn’t be a good idea. Any rational person would have dropped it, but Charles persisted, with characteristic zeal: if the Joyent relationship with node.js had become strained, so much more the reason to speak candidly about it! Charles prevailed, and the resulting talk, Platform as Reflection of Values, became one of Bryan’s most personally meaningful talks. Charles’s persistence was emblematic: he worked behind the scenes to encourage people to do their best work, always with an enthusiasm for the innovators and the creators. As we were contemplating Oxide, we told Charles what we wanted to do long before we had a company. Charles laughed with delight: "I hoped that you two would do something big, and I am just so happy for you that you’re doing something so ambitious!" As we raised seed capital, we knew that we were likely a poor fit for Charles and his fund. But we also knew that we deeply appreciated his wisdom and enthusiasm; we couldn’t resist pitching him on Oxide. Charles approached the investment in Oxide as he did with so many other aspects: with curiosity, diligence, empathy, and candor. He was direct with us that despite his enthusiasm for us personally, Oxide would be a challenging investment for his firm. But he also worked with us to address specific objections, and ultimately he won over his partnership. We were thrilled when he not only invested, but pulled together a syndicate of like-minded technologists and entrepreneurs to join him. Ever since, he has been a huge Oxide fan. Befitting his enthusiasm, one of his final posts expressed his enthusiasm and pride in what the Oxide team has built. Charles, thank you. You told us you were proud of us — and it meant the world. We are gutted to no longer have you with us; your influence lives on not just in Oxide, but also in the many people that you have inspired. You were the best of venture capital. Closer to the heart, you were a terrific friend to us both; thank you.

8 months ago 71 votes

More in programming

The Framework Desktop is a beast

I've been running the Framework Desktop for a few months here in Copenhagen now. It's an incredible machine. It's completely quiet, even under heavy, stress-all-cores load. It's tiny too, at just 4.5L of volume, especially compared to my old beautiful but bulky North tower running the 7950X — yet it's faster! And finally, it's simply funky, quirky, and fun! In some ways, the Framework Desktop is a curious machine. Desktop PCs are already very user-repairable! So why is Framework even bringing their talents to this domain? In the laptop realm, they're basically alone with that concept, but in the desktop space, it's rather crowded already. Yet it somehow still makes sense. Partly because Framework has gone with the AMD Ryzen AI Max 395+, which is technically a laptop CPU. You can find it in the ASUS ROG Flow Z13 and the HP ZBook Ultra. Which means it'll fit in a tiny footprint, and Framework apparently just wanted to see what they could do in that form factor. They clearly had fun with it. Look at mine: There are 21 little tiles on the front that you can get in a bunch of different colors or with logos from Framework. Or you can 3D print your own! It's a welcome change in aesthetic from the brushed aluminum or gamer-focused RGBs approach that most of the competition is taking. But let's cut to the benchmarks. That's really why you'd buy a machine like the Framework Desktop. There are significantly cheaper mini PCs available from Beelink and others, but so far, Framework has the only AMD 395+ unit on sale that's completely silent (the GMKTec very much is not, nor is the Z3 Flow). And for me, that's just a dealbreaker. I can't listen to roaring fans anymore. Here's the key benchmark for me: That's the only type of multi-core workload I really sit around waiting on these days, and the Framework Desktop absolutely crushes it. It's almost twice as fast as the Beelink SER8 and still a solid third faster than the Beelink SER9 too. Of course, it's also a lot more expensive, but you're clearly getting some multi-core bang for your buck here! It's even a more dramatic difference to the Macs. It's a solid 40% faster than the M4 Max and 50% faster than the M4 Pro! Now some will say "that's just because Docker is faster on Linux," and they're not entirely wrong. Docker runs natively on Linux, so for this test, where the MySQL/Redis/ElasticSearch data stores run in Docker while Ruby and the app code runs natively, that's part of the answer. Last I checked, it was about 25% of the difference. But so what? Docker is an integral part of the workflow for tons of developers. We use it to be able to run different versions of MySQL, Redis, and ElasticSearch for different applications on the same machine at the same time. You can't really do that without Docker. So this is what Real World benchmarks reveal. It's not just about having a Docker advantage, though. The AMD 395+ is also incredibly potent in RAW CPU performance. Those 16 Zen5 cores are running at 5.1GHz, and in Geekbench 6 multicore, this is how they stack up: Basically matching the M4 Max! And a good chunk faster than the M4 Pro (as well as other AMDs and Intel's 14900K!). No wonder that it's crazy quick with a full-core stress test like running 30,000 assertions for our HEY test suite. To be fair, the M4s are faster in single-core performance. Apple holds the crown there. It's about 20%. And you'll see that in benchmarks like Speedometer, which mostly measures JavaScript single-core performance. The Framework Desktop puts out 670 vs 744 on the M4 Pro on Speedometer 2.1. On SP 3.1, it's an even bigger difference with 35 vs 50. But I've found that all these computers feel fast enough in single-core performance these days. I can't actually feel the difference browsing on a machine that does 670 vs 744 on SP2.1. Hell, I can barely feel the difference between the SER8, which does 506, and the M4 Pro! The only time I actually feel like I'm waiting on anything is in multi-core workloads like the HEY test suite, and here the AMD 395+ is very near the fastest you can get for a consumer desktop machine today at any price. It gets even better when you bring price into the equation, though. The Framework Desktop with 64GB RAM + 2TB NVMe is $1,876. To get a Mac Studio with similar specs — M4 Max, 64GB RAM, 2TB NVMe — you'll literally spend nearly twice as much at $3,299! If you go for 128GB RAM, you'll spend $2,276 on the Framework, but $4,099 on the Mac. And it'll still be way slower for development work using Docker! The Framework Desktop is simply a great deal. Speaking of 64GB vs 128GB, I've been running the 64GB version, and I almost never get anywhere close to the limits. I think the highest I've seen in regular use is about 20GB of RAM in action. Linux is really efficient. Especially when you're using a window manager like Hyprland, as we do in Omarchy. The only reason you really want to go for the full 128GB RAM is to run local LLM models. The AMD 395+ uses unified memory, like Apple, so nearly all of it is addressable to be used by the GPU. That means you can run monster models, like the new 120b gpt-oss from OpenAI. Framework has a video showing them pushing out 40 tokens/second doing just that. That seems about in range of the numbers I've seen from the M4 Max, which also seem in the 40-50 token/second range, but I'll defer to folks who benchmark local LLMs for the exact details on that. I tried running the new gpt-oss-20b on my 64GB machine, though, and I wasn't exactly blown away by the accuracy. In fact, I'd say it was pretty bad. I mean, exceptionally cool that it's doable, but very far off the frontier models we have access to as SaaS. So personally, this isn't yet something I actually use all that much in day-to-day development. I want the best models running at full speed, and right now that means SaaS. So if you just want the best, small computer that runs Linux superbly well out of the box, you should buy the Framework Desktop. It's completely quiet, fantastically fast, and super fun to look at. But I think it's also fair to mention that you can get something like a Beelink SER9 for half the price! Yes, it's also only 2/3 the performance in multi-core, but it's just as fast in single-core. Most developers could totally get away with the SER9, and barely notice what they were missing. But there are just as many people for whom the extra $1,000 is worth the price to run the test suite 40 seconds quicker! You know who you are. Oh, before I close, I also need to mention that this thing is a gaming powerhouse. It basically punches about as hard as an RTX 4060! With an iGPU! That's kinda crazy. Totally new territory on the PC side for integrated graphics. ETA Prime has a video showing the same chip in the GMK Tech running premier games at 1440p High Settings at great frame rates. You can run most games under Linux these days too (thanks Valve and Steam Deck!), but if you need to dual boot with Windows, the dual NVMe slots in the Framework Desktop come very handy. Framework did good with this one. AMD really blew it out of the water with the 395+. We're spoiled to have such incredible hardware available for Linux at such appealing discounts over similar stuff from Cupertino. What a great time to love open source software and tinker-friendly hardware!

21 hours ago 4 votes
Writing: Blog Posts and Songs

I was listening to a podcast interview with the Jackson Browne (American singer/songwriter, political activist, and inductee into the Rock and Roll Hall of Fame) and the interviewer asks him how he approaches writing songs with social commentaries and critiques — something along the lines of: “How do you get from the New York Times headline on a social subject to the emotional heart of a song that matters to each individual?” Browne discusses how if you’re too subtle, people won’t know what you’re talking about. And if you’re too direct, you run the risk of making people feel like they’re being scolded. Here’s what he says about his songwriting: I want this to sound like you and I were drinking in a bar and we’re just talking about what’s going on in the world. Not as if you’re at some elevated place and lecturing people about something they should know about but don’t but [you think] they should care. You have to get to people where [they are, where] they do care and where they do know. I think that’s a great insight for anyone looking to have a connecting, effective voice. I know for me, it’s really easily to slide into a lecturing voice — you “should” do this and you “shouldn’t” do that. But I like Browne’s framing of trying to have an informal, conversational tone that meets people where they are. Like you’re discussing an issue in the bar, rather than listening to a sermon. Chris Coyier is the canonical example of this that comes to mind. I still think of this post from CSS Tricks where Chris talks about how to have submit buttons that go to different URLs: When you submit that form, it’s going to go to the URL /submit. Say you need another submit button that submits to a different URL. It doesn’t matter why. There is always a reason for things. The web is a big place and all that. He doesn’t conjure up some universally-applicable, justified rationale for why he’s sharing this method. Nor is there any pontificating on why this is “good” or “bad”. Instead, like most of Chris’ stuff, I read it as a humble acknowledgement of the practicalities at hand — “Hey, the world is a big place. People have to do crafty things to make their stuff work. And if you’re in that situation, here’s something that might help what ails ya.” I want to work on developing that kind of a voice because I love reading voices like that. Email · Mastodon · Bluesky

2 days ago 4 votes
Doing versus Delegating

A staff+ skill

2 days ago 7 votes
p-fast trie, but smaller

Previously, I wrote some sketchy ideas for what I call a p-fast trie, which is basically a wide fan-out variant of an x-fast trie. It allows you to find the longest matching prefix or nearest predecessor or successor of a query string in a set of names in O(log k) time, where k is the key length. My initial sketch was more complicated and greedy for space than necessary, so here’s a simplified revision. (“p” now stands for prefix.) layout A p-fast trie stores a lexicographically ordered set of names. A name is a sequence of characters from some small-ish character set. For example, DNS names can be represented as a set of about 50 letters, digits, punctuation and escape characters, usually one per byte of name. Names that are arbitrary bit strings can be split into chunks of 6 bits to make a set of 64 characters. Every unique prefix of every name is added to a hash table. An entry in the hash table contains: A shared reference to the closest name lexicographically greater than or equal to the prefix. Multiple hash table entries will refer to the same name. A reference to a name might instead be a reference to a leaf object containing the name. The length of the prefix. To save space, each prefix is not stored separately, but implied by the combination of the closest name and prefix length. A bitmap with one bit per possible character, corresponding to the next character after this prefix. For every other prefix that matches this prefix and is one character longer than this prefix, a bit is set in the bitmap corresponding to the last character of the longer prefix. search The basic algorithm is a longest-prefix match. Look up the query string in the hash table. If there’s a match, great, done. Otherwise proceed by binary chop on the length of the query string. If the prefix isn’t in the hash table, reduce the prefix length and search again. (If the empty prefix isn’t in the hash table then there are no names to find.) If the prefix is in the hash table, check the next character of the query string in the bitmap. If its bit is set, increase the prefix length and search again. Otherwise, this prefix is the answer. predecessor Instead of putting leaf objects in a linked list, we can use a more complicated search algorithm to find names lexicographically closest to the query string. It’s tricky because a longest-prefix match can land in the wrong branch of the implicit trie. Here’s an outline of a predecessor search; successor requires more thought. During the binary chop, when we find a prefix in the hash table, compare the complete query string against the complete name that the hash table entry refers to (the closest name greater than or equal to the common prefix). If the name is greater than the query string we’re in the wrong branch of the trie, so reduce the length of the prefix and search again. Otherwise search the set bits in the bitmap for one corresponding to the greatest character less than the query string’s next character; if there is one remember it and the prefix length. This will be the top of the sub-trie containing the predecessor, unless we find a longer match. If the next character’s bit is set in the bitmap, continue searching with a longer prefix, else stop. When the binary chop has finished, we need to walk down the predecessor sub-trie to find its greatest leaf. This must be done one character at a time – there’s no shortcut. thoughts In my previous note I wondered how the number of search steps in a p-fast trie compares to a qp-trie. I have some old numbers measuring the average depth of binary, 4-bit, 5-bit, 6-bit and 4-bit, 5-bit, dns qp-trie variants. A DNS-trie varies between 7 and 15 deep on average, depending on the data set. The number of steps for a search matches the depth for exact-match lookups, and is up to twice the depth for predecessor searches. A p-fast trie is at most 9 hash table probes for DNS names, and unlikely to be more than 7. I didn’t record the average length of names in my benchmark data sets, but I guess they would be 8–32 characters, meaning 3–5 probes. Which is far fewer than a qp-trie, though I suspect a hash table probe takes more time than chasing a qp-trie pointer. (But this kind of guesstimate is notoriously likely to be wrong!) However, a predecessor search might need 30 probes to walk down the p-fast trie, which I think suggests a linked list of leaf objects is a better option.

2 days ago 4 votes
Software books I wish I could read

New Logic for Programmers Release! v0.11 is now available! This is over 20% longer than v0.10, with a new chapter on code proofs, three chapter overhauls, and more! Full release notes here. Software books I wish I could read I'm writing Logic for Programmers because it's a book I wanted to have ten years ago. I had to learn everything in it the hard way, which is why I'm ensuring that everybody else can learn it the easy way. Books occupy a sort of weird niche in software. We're great at sharing information via blogs and git repos and entire websites. These have many benefits over books: they're free, they're easily accessible, they can be updated quickly, they can even be interactive. But no blog post has influenced me as profoundly as Data and Reality or Making Software. There is no blog or talk about debugging as good as the Debugging book. It might not be anything deeper than "people spend more time per word on writing books than blog posts". I dunno. So here are some other books I wish I could read. I don't think any of them exist yet but it's a big world out there. Also while they're probably best as books, a website or a series of blog posts would be ok too. Everything about Configurations The whole topic of how we configure software, whether by CLI flags, environmental vars, or JSON/YAML/XML/Dhall files. What causes the configuration complexity clock? How do we distinguish between basic, advanced, and developer-only configuration options? When should we disallow configuration? How do we test all possible configurations for correctness? Why do so many widespread outages trace back to misconfiguration, and how do we prevent them? I also want the same for plugin systems. Manifests, permissions, common APIs and architectures, etc. Configuration management is more universal, though, since everybody either uses software with configuration or has made software with configuration. The Big Book of Complicated Data Schemas I guess this would kind of be like Schema.org, except with a lot more on the "why" and not the what. Why is important for the Volcano model to have a "smokingAllowed" field?1 I'd see this less as "here's your guide to putting Volcanos in your database" and more "here's recurring motifs in modeling interesting domains", to help a person see sources of complexity in their own domain. Does something crop up if the references can form a cycle? If a relationship needs to be strictly temporary, or a reference can change type? Bonus: path dependence in data models, where an additional requirement leads to a vastly different ideal data model that a company couldn't do because they made the old model. (This has got to exist, right? Business modeling is a big enough domain that this must exist. Maybe The Essence of Software touches on this? Man I feel bad I haven't read that yet.) Computer Science for Software Engineers Yes, I checked, this book does not exist (though maybe this is the same thing). I don't have any formal software education; everything I know was either self-taught or learned on the job. But it's way easier to learn software engineering that way than computer science. And I bet there's a lot of other engineers in the same boat. This book wouldn't have to be comprehensive or instructive: just enough about each topic to understand why it's an area of study and appreciate how research in it eventually finds its way into practice. MISU Patterns MISU, or "Make Illegal States Unrepresentable", is the idea of designing system invariants in the structure of your data. For example, if a Contact needs at least one of email or phone to be non-null, make it a sum type over EmailContact, PhoneContact, EmailPhoneContact (from this post). MISU is great. Most MISU in the wild look very different than that, though, because the concept of MISU is so broad there's lots of different ways to achieve it. And that means there are "patterns": smart constructors, product types, properly using sets, newtypes to some degree, etc. Some of them are specific to typed FP, while others can be used in even untyped languages. Someone oughta make a pattern book. My one request would be to not give them cutesy names. Do something like the Aarne–Thompson–Uther Index, where items are given names like "Recognition by manner of throwing cakes of different weights into faces of old uncles". Names can come later. The Tools of '25 Not something I'd read, but something to recommend to junior engineers. Starting out it's easy to think the only bit that matters is the language or framework and not realize the enormous amount of surrounding tooling you'll have to learn. This book would cover the basics of tools that enough developers will probably use at some point: git, VSCode, very basic Unix and bash, curl. Maybe the general concepts of tools that appear in every ecosystem, like package managers, build tools, task runners. That might be easier if we specialize this to one particular domain, like webdev or data science. Ideally the book would only have to be updated every five years or so. No LLM stuff because I don't expect the tooling will be stable through 2026, to say nothing of 2030. A History of Obsolete Optimizations Probably better as a really long blog series. Each chapter would be broken up into two parts: A deep dive into a brilliant, elegant, insightful historical optimization designed to work within the constraints of that era's computing technology What we started doing instead, once we had more compute/network/storage available. c.f. A Spellchecker Used to Be a Major Feat of Software Engineering. Bonus topics would be brilliance obsoleted by standardization (like what people did before git and json were universal), optimizations we do today that may not stand the test of time, and optimizations from the past that did. Sphinx Internals I need this. I've spent so much goddamn time digging around in Sphinx and docutils source code I'm gonna throw up. Systems Distributed Talk Today! Online premier's at noon central / 5 PM UTC, here! I'll be hanging out to answer questions and be awkward. You ever watch a recording of your own talk? It's real uncomfortable! In this case because it's a field on one of Volcano's supertypes. I guess schemas gotta follow LSP too ↩

2 days ago 9 votes