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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!
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
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.
Here’s a sobering thought: today, data centers already consume 1-2% of the world’s power, and that percentage will likely rise to 3-4% by the end of the decade. According to Goldman Sachs research, that rise will include a doubling in data center carbon dioxide emissions. As the data and AI boom progresses, this thirst for power shows no signs of slowing down anytime soon. Two key challenges quickly become evident for the 85% of IT that currently lives on-premises. How can organizations reduce power consumption and corresponding carbon emissions? How can organizations keep pace with AI innovation as existing data centers run out of available power? Figure 1. Masanet et al. (2020), Cisco, IEA, Goldman Sachs Research Rack-scale design is critical to improved data center efficiency Traditional data center IT consumes so much power because the fundamental unit of compute is an individual server; like a house where rooms were built one at a time, with each room having its own central AC unit, gas furnace, and electrical panel. Individual rackmount servers are stacked together, each with their own AC power supplies, cooling fans, and power management. They are then paired with storage appliances and network switches that communicate at arm’s length, not designed as a cohesive whole. This approach fundamentally limits organizations' ability to maintain sustainable, high-efficiency computing systems. Of course, hyperscale public cloud providers did not design their data center systems this way. Instead, they operate like a carefully planned smart home where everything is designed to work together cohesively and is operated by software that understands the home’s systems end-to-end. High-efficiency, rack-scale computers are deployed at scale and operate as a single unit with integrated storage and networking to support elastic cloud computing services. This modern archietecture is made available to the market as public cloud, but that rental-only model is ill-fit for many business needs. Compared to a popular rackmount server vendor, Oxide is able to fill our specialized racks with 32 AMD Milan sleds and highly-available network switches using less than 15kW per rack, doubling the compute density in a typical data center. With just 16 of the alternative 1U servers and equivalent network switches, over 16kW of power is required per rack, leading to only 1,024 CPU cores vs Oxide’s 2,048. Extracting more useful compute from each kW of power and square foot of data center space is key to the future effectiveness of on-premises computing. At Oxide, we’ve taken this lesson in advancing rack-scale design, improved upon it in several ways, and made it available for every organization to purchase and operate anywhere in the world without a tether back to the public cloud. Our Cloud Computer treats the entire rack as a single, unified computer rather than a collection of independent parts, achieving unprecedented power efficiency. By designing the hardware and software together, we’ve eliminated unnecessary components and optimized every aspect of system operation through a control plane with visibility to end-to-end operations. When we started Oxide, the DC bus bar stood as one of the most glaring differences between the rack-scale machines at the hyperscalers and the rack-and-stack servers that the rest of the market was stuck with. That a relatively simple piece of copper was unavailable to commercial buyers — despite being unequivocally the right way to build it! — represented everything wrong with the legacy approach. The bus bar in the Oxide Cloud Computer is not merely more efficient, it is a concrete embodiment of the tremendous gains from designing at rack-scale, and by integrating hardware with software. — Bryan Cantrill The improvements we’re seeing are rooted in technical innovation Replacing low-efficiency AC power supplies with a high-efficiency DC Bus Bar This eliminates the 70 total AC power supplies found in an equivalent legacy server rack within 32 servers, two top-of-rack switches, and one out-of-band switch, each with two AC power supplies. This power shelf also ensures the load is balanced across phases, something that’s impossible with traditional power distribution units found in legacy server racks. Bigger fans = bigger efficiency gains using 12x less energy than legacy servers, which each contain as many as 7 fans, which must work much harder to move air over system components. Purpose-built for power efficiency less restrictive airflow than legacy servers by eliminating extraneous components like PCIe risers, storage backplanes, and more. Legacy servers need many optional components like these because they could be used for any number of tasks, such as point-of-sale systems, data center servers, or network-attached-storage (NAS) systems. Still, they were never designed optimally for any one of those tasks. The Oxide Cloud Computer was designed from the ground up to be a rack-scale cloud computing powerhouse, and so it’s optimized for exactly that task. Hardware + Software designed together By designing the hardware and software together, we can make hardware choices like more intelligent DC-DC power converters that can provide rich telemetry to our control plane, enabling future feature enhancements such as dynamic power capping and efficiency-based workload placement that are impossible with legacy servers and software systems. Learn more about Oxide’s intelligent Power Shelf Controller The Bottom Line: Customers and the Environment Both Benefit Reducing data center power demands and achieving more useful computing per kilowatt requires fundamentally rethinking traditional data center utilization and compute design. At Oxide, we’ve proven that dramatic efficiency gains are possible when you rethink the computer at rack-scale with hardware and software designed thoughtfully and rigorously together. Ready to learn how your organization can achieve these results? Schedule time with our team here. Together, we can reclaim on-premises computing efficiency to achieve both business and sustainability goals.
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As I slowly but surely work towards the next release of my setcmd project for the Amiga (see the 68k branch for the gory details and my total noob-like C flailing around), I’ve made heavy use of documentation in the AmigaGuide format. Despite it’s age, it’s a great Amiga-native format and there’s a wealth of great information out there for things like the C API, as well as language guides and tutorials for tools like the Installer utility - and the AmigaGuide markup syntax itself. The only snag is, I had to have access to an Amiga (real or emulated), or install one of the various viewer programs on my laptops. Because like many, I spend a lot of time in a web browser and occasionally want to check something on my mobile phone, this is less than convenient. Fortunately, there’s a great AmigaGuideJS online viewer which renders AmigaGuide format documents using Javascript. I’ve started building up a collection of useful developer guides and other files in my own reference library so that I can access this documentation whenever I’m not at my Amiga or am coding in my “modern” dev environment. It’s really just for my own personal use, but I’ll be adding to it whenever I come across a useful piece of documentation so I hope it’s of some use to others as well! And on a related note, I now have a “unified” code-base so that SetCmd now builds and runs on 68k-based OS 3.x systems as well as OS 4.x PPC systems like my X5000. I need to: Tidy up my code and fix all the “TODO” stuff Update the Installer to run on OS 3.x systems Update the documentation Build a new package and upload to Aminet/OS4Depot Hopefully I’ll get that done in the next month or so. With the pressures of work and family life (and my other hobbies), progress has been a lot slower these last few years but I’m still really enjoying working on Amiga code and it’s great to have a fun personal project that’s there for me whenever I want to hack away at something for the sheer hell of it. I’ve learned a lot along the way and the AmigaOS is still an absolute joy to develop for. I even brought my X5000 to the most recent Kickstart Amiga User Group BBQ/meetup and had a fun day working on the code with fellow Amigans and enjoying some classic gaming & demos - there was also a MorphOS machine there, which I think will be my next target as the codebase is slowly becoming more portable. Just got to find some room in the “retro cave” now… This stuff is addictive :)
A little while back I heard about the White House launching their version of a Drudge Report style website called White House Wire. According to Axios, a White House official said the site’s purpose was to serve as “a place for supporters of the president’s agenda to get the real news all in one place”. So a link blog, if you will. As a self-professed connoisseur of websites and link blogs, this got me thinking: “I wonder what kind of links they’re considering as ‘real news’ and what they’re linking to?” So I decided to do quick analysis using Quadratic, a programmable spreadsheet where you can write code and return values to a 2d interface of rows and columns. I wrote some JavaScript to: Fetch the HTML page at whitehouse.gov/wire Parse it with cheerio Select all the external links on the page Return a list of links and their headline text In a few minutes I had a quick analysis of what kind of links were on the page: This immediately sparked my curiosity to know more about the meta information around the links, like: If you grouped all the links together, which sites get linked to the most? What kind of interesting data could you pull from the headlines they’re writing, like the most frequently used words? What if you did this analysis, but with snapshots of the website over time (rather than just the current moment)? So I got to building. Quadratic today doesn’t yet have the ability for your spreadsheet to run in the background on a schedule and append data. So I had to look elsewhere for a little extra functionality. My mind went to val.town which lets you write little scripts that can 1) run on a schedule (cron), 2) store information (blobs), and 3) retrieve stored information via their API. After a quick read of their docs, I figured out how to write a little script that’ll run once a day, scrape the site, and save the resulting HTML page in their key/value storage. From there, I was back to Quadratic writing code to talk to val.town’s API and retrieve my HTML, parse it, and turn it into good, structured data. There were some things I had to do, like: Fine-tune how I select all the editorial links on the page from the source HTML (I didn’t want, for example, to include external links to the White House’s social pages which appear on every page). This required a little finessing, but I eventually got a collection of links that corresponded to what I was seeing on the page. Parse the links and pull out the top-level domains so I could group links by domain occurrence. Create charts and graphs to visualize the structured data I had created. Selfish plug: Quadratic made this all super easy, as I could program in JavaScript and use third-party tools like tldts to do the analysis, all while visualizing my output on a 2d grid in real-time which made for a super fast feedback loop! Once I got all that done, I just had to sit back and wait for the HTML snapshots to begin accumulating! It’s been about a month and a half since I started this and I have about fifty days worth of data. The results? Here’s the top 10 domains that the White House Wire links to (by occurrence), from May 8 to June 24, 2025: youtube.com (133) foxnews.com (72) thepostmillennial.com (67) foxbusiness.com (66) breitbart.com (64) x.com (63) reuters.com (51) truthsocial.com (48) nypost.com (47) dailywire.com (36) From the links, here’s a word cloud of the most commonly recurring words in the link headlines: “trump” (343) “president” (145) “us” (134) “big” (131) “bill” (127) “beautiful” (113) “trumps” (92) “one” (72) “million” (57) “house” (56) The data and these graphs are all in my spreadsheet, so I can open it up whenever I want to see the latest data and re-run my script to pull the latest from val.town. In response to the new data that comes in, the spreadsheet automatically parses it, turn it into links, and updates the graphs. Cool! If you want to check out the spreadsheet — sorry! My API key for val.town is in it (“secrets management” is on the roadmap). But I created a duplicate where I inlined the data from the API (rather than the code which dynamically pulls it) which you can check out here at your convenience. Email · Mastodon · Bluesky
One of the first types we learn about is the boolean. It's pretty natural to use, because boolean logic underpins much of modern computing. And yet, it's one of the types we should probably be using a lot less of. In almost every single instance when you use a boolean, it should be something else. The trick is figuring out what "something else" is. Doing this is worth the effort. It tells you a lot about your system, and it will improve your design (even if you end up using a boolean). There are a few possible types that come up often, hiding as booleans. Let's take a look at each of these, as well as the case where using a boolean does make sense. This isn't exhaustive—[1]there are surely other types that can make sense, too. Datetimes A lot of boolean data is representing a temporal event having happened. For example, websites often have you confirm your email. This may be stored as a boolean column, is_confirmed, in the database. It makes a lot of sense. But, you're throwing away data: when the confirmation happened. You can instead store when the user confirmed their email in a nullable column. You can still get the same information by checking whether the column is null. But you also get richer data for other purposes. Maybe you find out down the road that there was a bug in your confirmation process. You can use these timestamps to check which users would be affected by that, based on when their confirmation was stored. This is the one I've seen discussed the most of all these. We run into it with almost every database we design, after all. You can detect it by asking if an action has to occur for the boolean to change values, and if values can only change one time. If you have both of these, then it really looks like it is a datetime being transformed into a boolean. Store the datetime! Enums Much of the remaining boolean data indicates either what type something is, or its status. Is a user an admin or not? Check the is_admin column! Did that job fail? Check the failed column! Is the user allowed to take this action? Return a boolean for that, yes or no! These usually make more sense as an enum. Consider the admin case: this is really a user role, and you should have an enum for it. If it's a boolean, you're going to eventually need more columns, and you'll keep adding on other statuses. Oh, we had users and admins, but now we also need guest users and we need super-admins. With an enum, you can add those easily. enum UserRole { User, Admin, Guest, SuperAdmin, } And then you can usually use your tooling to make sure that all the new cases are covered in your code. With a boolean, you have to add more booleans, and then you have to make sure you find all the places where the old booleans were used and make sure they handle these new cases, too. Enums help you avoid these bugs. Job status is one that's pretty clearly an enum as well. If you use booleans, you'll have is_failed, is_started, is_queued, and on and on. Or you could just have one single field, status, which is an enum with the various statuses. (Note, though, that you probably do want timestamp fields for each of these events—but you're still best having the status stored explicitly as well.) This begins to resemble a state machine once you store the status, and it means that you can make much cleaner code and analyze things along state transition lines. And it's not just for storing in a database, either. If you're checking a user's permissions, you often return a boolean for that. fn check_permissions(user: User) -> bool { false // no one is allowed to do anything i guess } In this case, true means the user can do it and false means they can't. Usually. I think. But you can really start to have doubts here, and with any boolean, because the application logic meaning of the value cannot be inferred from the type. Instead, this can be represented as an enum, even when there are just two choices. enum PermissionCheck { Allowed, NotPermitted(reason: String), } As a bonus, though, if you use an enum? You can end up with richer information, like returning a reason for a permission check failing. And you are safe for future expansions of the enum, just like with roles. You can detect when something should be an enum a proliferation of booleans which are mutually exclusive or depend on one another. You'll see multiple columns which are all changed at the same time. Or you'll see a boolean which is returned and used for a long time. It's important to use enums here to keep your program maintainable and understandable. Conditionals But when should we use a boolean? I've mainly run into one case where it makes sense: when you're (temporarily) storing the result of a conditional expression for evaluation. This is in some ways an optimization, either for the computer (reuse a variable[2]) or for the programmer (make it more comprehensible by giving a name to a big conditional) by storing an intermediate value. Here's a contrived example where using a boolean as an intermediate value. fn calculate_user_data(user: User, records: RecordStore) { // this would be some nice long conditional, // but I don't have one. So variables it is! let user_can_do_this: bool = (a && b) && (c || !d); if user_can_do_this && records.ready() { // do the thing } else if user_can_do_this && records.in_progress() { // do another thing } else { // and something else! } } But even here in this contrived example, some enums would make more sense. I'd keep the boolean, probably, simply to give a name to what we're calculating. But the rest of it should be a match on an enum! * * * Sure, not every boolean should go away. There's probably no single rule in software design that is always true. But, we should be paying a lot more attention to booleans. They're sneaky. They feel like they make sense for our data, but they make sense for our logic. The data is usually something different underneath. By storing a boolean as our data, we're coupling that data tightly to our application logic. Instead, we should remain critical and ask what data the boolean depends on, and should we maybe store that instead? It comes easier with practice. Really, all good design does. A little thinking up front saves you a lot of time in the long run. I know that using an em-dash is treated as a sign of using LLMs. LLMs are never used for my writing. I just really like em-dashes and have a dedicated key for them on one of my keyboard layers. ↩ This one is probably best left to the compiler. ↩
SumatraPDF is a fast, small, open-source PDF reader for Windows, written in C++. This article describes how I implemented StrVec class for efficiently storing multiple strings. Much ado about the strings Strings are among the most used types in most programs. Arrays of strings are also used often. I count ~80 uses of StrVec in SumatraPDF code. This article describes how I implemented an optimized array of strings in SumatraPDF C++ code . No STL for you Why not use std::vector<std::string>? In SumatraPDF I don’t use STL. I don’t use std::string, I don’t use std::vector. For me it’s a symbol of my individuality, and my belief in personal freedom. As described here, minimum size of std::string on 64-bit machines is 32 bytes for msvc / gcc and 24 bytes for short strings (15 chars for msvc / gcc, 22 chars for clang). For longer strings we have more overhead: 32⁄24 bytes for the header memory allocator overhead allocator metadata padding due to rounding allocations to at least 16 bytes There’s also std::vector overhead: for fast appends (push()) std::vectorimplementations over-allocated space Longer strings are allocated at random addresses so they can be spread out in memory. That is bad for cache locality and that often cause more slowness than executing lots of instructions. Design and implementation of StrVec StrVec (vector of strings) solves all of the above: per-string overhead of only 8 bytes strings are laid out next to each other in memory StrVec High level design of StrVec: backing memory is allocated in singly-linked pages similar to std::vector, we start with small page and increase the size of the page. This strikes a balance between speed of accessing a string at random index and wasted space unlike std::vector we don’t reallocate memory (most of the time). That saves memory copy when re-allocating backing space Here’s all there is to StrVec: struct StrVec { StrVecPage* first = nullptr; int nextPageSize = 256; int size = 0; } size is a cached number of strings. It could be calculated by summing the size in all StrVecPages. nextPageSize is the size of the next StrVecPage. Most array implementation increase the size of next allocation by 1.4x - 2x. I went with the following progression: 256 bytes, 1k, 4k, 16k, 32k and I cap it at 64k. I don’t have data behind those numbers, they feel right. Bigger page wastes more space. Smaller page makes random access slower because to find N-th string we need to traverse linked list of StrVecPage. nextPageSize is exposed to allow the caller to optimize use. E.g. if it expects lots of strings, it could set nextPageSize to a large number. StrVecPage Most of the implementation is in StrVecPage. The big idea here is: we allocate a block of memory strings are allocated from the end of memory block at the beginning of the memory block we build and index of strings. For each string we have: u32 size u32 offset of the string within memory block, counting from the beginning of the block The layout of memory block is: StrVecPage struct { size u32; offset u32 } [] … not yet used space strings This is StrVecPage: struct StrVecPage { struct StrVecPage* next; int pageSize; int nStrings; char* currEnd; } next is for linked list of pages. Since pages can have various sizes we need to record pageSize. nStrings is number of strings in the page and currEnd points to the end of free space within page. Implementing operations Appending a string Appending a string at the end is most common operation. To append a string: we calculate how much memory inside a page it’ll need: str::Len(string) + 1 + sizeof(u32) + sizeof(u32). +1 is for 0-termination for compatibility with C APIs that take char*, and 2xu32 for size and offset. If we have enough space in last page, we add size and offset at the end of index and append a string from the end i.e. `currEnd - (str::Len(string) + 1). If there is not enough space in last page, we allocate new page We can calculate how much space we have left with: int indexEntrySize = sizeof(u32) + sizeof(u32); // size + offset char* indexEnd = (char*)pageStart + sizeof(StrVecPage) + nStrings*indexEntrySize int nBytesFree = (int)(currEnd - indexEnd) Removing a string Removing a string is easy because it doesn’t require moving memory inside StrVecPage. We do nStrings-- and move index values of strings after the removed string. I don’t bother freeing the string memory within a page. It’s possible but complicated enough I decided to skip it. You can compact StrVec to remove all overhead. If you do not care about preserving order of strings after removal, I haveRemoveAtFast() which uses a trick: instead of copying memory of all index values after removed string, I copy a single index from the end into a slot of the string being removed. Replacing a string or inserting in the middle Replacing a string or inserting a string in the middle is more complicated because there might not be enough space in the page for the string. When there is enough space, it’s as simple as append. When there is not enough space, I re-use the compacting capability: I compact all existing pages into a single page with extra space for the string and some extra space as an optimization for multiple inserts. Iteration A random access requires traversing a linked list. I think it’s still fast because typically there aren’t many pages and we only need to look at a single nStrings value. After compaction to a single page, random access is as fast as it could ever be. C++ iterator is optimized for sequential access: struct iterator { const StrVec* v; int idx; // perf: cache page, idxInPage from prev iteration int idxInPage; StrVecPage* page; } We cache the current state of iteration as page and idxInPage. To advance to next string we advance idxInPage. If it exceeds nStrings, we advance to page->next. Optimized search Finding a string is as optimized as it could be without a hash table. Typically to compare char* strings you need to call str::Eq(s, s2) for every string you compare it to. That is a function call and it has to touch s2 memory. That is bad for performance because it blows the cache. In StrVec I calculate length of the string to find once and then traverse the size / offset index. Only when size is different I have to compare the strings. Most of the time we just look at offset / size in L1 cache, which is very fast. Compacting If you know that you’ll not be adding more strings to StrVec you can compact all pages into a single page with no overhead of empty space. It also speeds up random access because we don’t have multiple pages to traverse to find the item and a given index. Representing a nullptr char* Even though I have a string class, I mostly use char* in SumatraPDF code. In that world empty string and nullptr are 2 different things. To allow storing nullptr strings in StrVec (and not turning them into empty strings on the way out) I use a trick: a special u32 value kNullOffset represents nullptr. StrVec is a string pool allocator In C++ you have to track the lifetime of each object: you allocate with malloc() or new when you no longer need to object, you call free() or delete However, the lifetime of allocations is often tied together. For example in SumatraPDF an opened document is represented by a class. Many allocations done to construct that object last exactly as long as the object. The idea of a pool allocator is that instead of tracking the lifetime of each allocation, you have a single allocator. You allocate objects with the same lifetime from that allocator and you free them with a single call. StrVec is a string pool allocator: all strings stored in StrVec have the same lifetime. Testing In general I don’t advocate writing a lot of tests. However, low-level, tricky functionality like StrVec deserves decent test coverage to ensure basic functionality works and to exercise code for corner cases. I have 360 lines of tests for ~700 lines of of implementation. Potential tweaks and optimization When designing and implementing data structures, tradeoffs are aplenty. Interleaving index and strings I’m not sure if it would be faster but instead of storing size and offset at the beginning of the page and strings at the end, we could store size / string sequentially from the beginning. It would remove the need for u32 of offset but would make random access slower. Varint encoding of size and offset Most strings are short, under 127 chars. Most offsets are under 16k. If we stored size and offset as variable length integers, we would probably bring down average per-string overhead from 8 bytes to ~4 bytes. Implicit size When strings are stored sequentially size is implicit as difference between offset of the string and offset of next string. Not storing size would make insert and set operations more complicated and costly: we would have to compact and arrange strings in order every time. Storing index separately We could store index of size / offset in a separate vector and use pages to only allocate string data. This would simplify insert and set operations. With current design if we run out of space inside a page, we have to re-arrange memory. When offset is stored outside of the page, it can refer to any page so insert and set could be as simple as append. The evolution of StrVec The design described here is a second implementation of StrVec. The one before was simply a combination of str::Str (my std::string) for allocating all strings and Vec<u32> (my std::vector) for storing offset index. It had some flaws: appending a string could re-allocate memory within str::Str. The caller couldn’t store returned char* pointer because it could be invalidated. As a result the API was akward and potentially confusing: I was returning offset of the string so the string was str::Str.Data() + offset. The new StrVec doesn’t re-allocate on Append, only (potentially) on InsertAt and SetAt. The most common case is append-only which allows the caller to store the returned char* pointers. Before implementing StrVec I used Vec<char*>. Vec is my version of std::vector and Vec<char*> would just store pointer to individually allocated strings. Cost vs. benefit I’m a pragmatist: I want to achieve the most with the least amount of code, the least amount of time and effort. While it might seem that I’m re-implementing things willy-nilly, I’m actually very mindful of the cost of writing code. Writing software is a balance between effort and resulting quality. One of the biggest reasons SumatraPDF so popular is that it’s fast and small. That’s an important aspect of software quality. When you double click on a PDF file in an explorer, SumatraPDF starts instantly. You can’t say that about many similar programs and about other software in general. Keeping SumatraPDF small and fast is an ongoing focus and it does take effort. StrVec.cpp is only 705 lines of code. It took me several days to complete. Maybe 2 days to write the code and then some time here and there to fix the bugs. That being said, I didn’t start with this StrVec. For many years I used obvious Vec<char*>. Then I implemented somewhat optimized StrVec. And a few years after that I implemented this ultra-optimized version. References SumatraPDF is a small, fast, multi-format (PDF/eBook/Comic Book and more), open-source reader for Windows. The implementation described here: StrVec.cpp, StrVec.h, StrVec_ut.cpp By the time you read this, the implementation could have been improved.
Consent morality is the idea that there are no higher values or virtues than allowing consenting adults to do whatever they please. As long as they're not hurting anyone, it's all good, and whoever might have a problem with that is by definition a bigot. This was the overriding morality I picked up as a child of the 90s. From TV, movies, music, and popular culture. Fly your freak! Whatever feels right is right! It doesn't seem like much has changed since then. What a moral dead end. I first heard the term consent morality as part of Louise Perry's critique of the sexual revolution. That in the context of hook-up culture, situationships, and falling birthrates, we have to wrestle with the fact that the sexual revolution — and it's insistence that, say, a sky-high body count mustn't be taboo — has led society to screwy dating market in the internet age that few people are actually happy with. But the application of consent morality that I actually find even more troubling is towards parenthood. As is widely acknowledged now, we're in a bit of a birthrate crisis all over the world. And I think consent morality can help explain part of it. I was reminded of this when I posted a cute video of a young girl so over-the-moon excited for her dad getting off work to argue that you'd be crazy to trade that for some nebulous concept of "personal freedom". Predictably, consent morality immediately appeared in the comments: Some people just don't want children and that's TOTALLY OKAY and you're actually bad for suggesting they should! No. It's the role of a well-functioning culture to guide people towards The Good Life. Not force, but guide. Nobody wants to be convinced by the morality police at the pointy end of a bayonet, but giving up on the whole idea of objective higher values and virtues is a nihilistic and cowardly alternative. Humans are deeply mimetic creatures. It's imperative that we celebrate what's good, true, and beautiful, such that these ideals become collective markers for morality. Such that they guide behavior. I don't think we've done a good job at doing that with parenthood in the last thirty-plus years. In fact, I'd argue we've done just about everything to undermine the cultural appeal of the simple yet divine satisfaction of child rearing (and by extension maligned the square family unit with mom, dad, and a few kids). Partly out of a coordinated campaign against the family unit as some sort of trad (possibly fascist!) identity marker in a long-waged culture war, but perhaps just as much out of the banal denigration of how boring and limiting it must be to carry such simple burdens as being a father or a mother in modern society. It's no wonder that if you incessantly focus on how expensive it is, how little sleep you get, how terrifying the responsibility is, and how much stress is involved with parenthood that it doesn't seem all that appealing! This is where Jordan Peterson does his best work. In advocating for the deeper meaning of embracing burden and responsibility. In diagnosing that much of our modern malaise does not come from carrying too much, but from carrying too little. That a myopic focus on personal freedom — the nights out, the "me time", the money saved — is a spiritual mirage: You think you want the paradise of nothing ever being asked of you, but it turns out to be the hell of nobody ever needing you. Whatever the cause, I think part of the cure is for our culture to reembrace the virtue and the value of parenthood without reservation. To stop centering the margins and their pathologies. To start centering the overwhelming middle where most people make for good parents, and will come to see that role as the most meaningful part they've played in their time on this planet. But this requires giving up on consent morality as the only way to find our path to The Good Life. It involves taking a moral stance that some ways of living are better than other ways of living for the broad many. That parenthood is good, that we need more children both for the literal survival of civilization, but also for the collective motivation to guard against the bad, the false, and the ugly. There's more to life than what you feel like doing in the moment. The worst thing in the world is not to have others ask more of you. Giving up on the total freedom of the unmoored life is a small price to pay for finding the deeper meaning in a tethered relationship with continuing a bloodline that's been drawn for hundreds of thousands of years before it came to you. You're never going to be "ready" before you take the leap. If you keep waiting, you'll wait until the window has closed, and all you see is regret. Summon a bit of bravery, don't overthink it, and do your part for the future of the world. It's 2.1 or bust, baby!
