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Many (most?) engineers go from university to a sizable company significantly distancing them from the actual value their code creates. They labour under the delusion that they’re paid to write code. In fact, they’re paid to make money, and writing code is probably the most expensive way that they can do that. They will often say things like “We should scrap this entirely and re-write it, it will only take 8 months” – often about code that generates 8 figures in revenue and employs several dozen people. Code that pays for their smartwatches. But, of course: Engineers are hired to create business value, not to program things – Don’t Call Yourself A Programmer, And Other Career Advice In my estimate it takes about a decade of experience before engineers start to really internalize this. This can be significantly sped up by having a shorter feedback loop between the code written and the value realized by the engineer. There are two ways to do this: Freelancing Founding Freelancing By freelancing, and doing it well, the reward, is very directly tied to the code written. The best way to do freelance, for the sake of learning, would be to work on fixed cost contracts – which isn’t great freelancing advice, but is excellent for the longterm career. Delivering to someone elses specs makes engineers focused on delivery only the necessary and sufficient code to make that happen. All the correct decisions result in an improvement of the engineers earnings per hour and all mistakes in a reduction. That feedback loop very quickly teaches: The importance of quality and automated testing Architecture and keeping options open Communication and requirements gathering, asking the right questions All of these are factors that come into play once an engineer is breaking the barrier from Senior to management or Staff. Founding a company Founding a company, where the code that you produced secures your salary, teaches those lessons, plus a few others: Understanding the importance tradeoffs that companies make betwen velocity and tech debt It is also an opportunity to learn how to make those tradeoffs well, something engineers aren’t always great at Experience creating the most value possible with the least code Very few enginers pre-emtively suggest ways to test product hyptheses using cheaper appoaches Pragmatism and bias towards shipping and avoidingg gold-plating functionality that is immature Plus you very quickly start to understand why “We should re-write it” is almost never the right business decision. All software engineers should freelance or found a business was originally published by Ognjen Regoje at Ognjen Regoje • ognjen.io on April 18, 2025.
In The Innovator’s Dilemma Christensen talks about how when acquiring a company you might either be acquiring its product or its processes. Depending on which it is, you need to handle the integration differently. I’ve realized that hiring a new manager follows a similar pattern: either they’re expected to integrate into the organization, or be independent and create some change. That expectation depends on whether the team, and possibly the wider organization, function well. If the team is high-performing, why would adding or overhauling processes make sense over fine-tuning existing ones? But new managers often join and immediately start suggesting ways to fix things. In many of these cases, they aren’t suggesting some best practices but are simply trying to have the new company function in a similar way to their previous one. But they never have enough context to justify these changes. What they should do is take a step back and understand why they were hired and what already works. Are they there to run the team as it is and perhaps look for marginal gains in efficiency and effectiveness? Or are they there because things are fundamentally broken and they need to overhaul the organization? In 9 out of 10 cases, it’s the first one. They’re there to ensure the continuity of the team. Therefore in 9 out of 10 cases the objective should be to integrate into the processes as quickly as possible and help iterate. Why are you here, manager? was originally published by Ognjen Regoje at Ognjen Regoje • ognjen.io on April 18, 2025.
One of the best pieces of advice I’ve ever gotten is to take a minute, or a week, after you’ve had a difficult conversation. By and large, people are not unreasonable. They’re not out to get you. They’re not trying to make your life miserable. They’re probably trying to do what they think is right. But tough conversations happen and when they do it’s important to take time to process the information and formulate a more nuanced opinion. To take a work example: picture a conversation where you’re being some particularly heavy feedback You’re confused, you’re sad, you’re angry. You disagree. You want to protest, defend yourself, argue, explain. Doing so, however, would accomplish nothing in the immediate, and probably set you back in the long-term. The other person is probably also upset and stressed about having to have the conversation. Getting defensive would get make them to do the same and the conversation would quickly devolve into one run by emotions. Instead, listen and gather as much information as possible. If possible, try to write as much as you can down. Don’t say much except ask questions and then politely ask for a follow-up meeting in a few days. That will give you the time to process all the information and figure out if they were right, if it might not have been a big deal at all, if there is nuance in the situation or if you were indeed right. Or, as is most likely, some combination of all of the above. You’ll be able to formulate a cohesive model of the situation in your head, which will help you make a better decision or counter-argument if needed. It’ll also give you, and the others, time to cool down and prevent anyone from reacting too emotionally. Come to the follow-up meeting with humility and a willingness to compromise. Recap the previous meeting and make sure that everyone is on the same page. Then explain your understanding of the situation and present your opinion. The end result should be a much more amicable outcome without the need for a third meeting. And while my example is in the context of work, the same is true for personal conversations. So, take a minute. Or a week. It’ll help you make better decisions. During a difficult conversation, remember to take a minute was originally published by Ognjen Regoje at Ognjen Regoje • ognjen.io on April 17, 2025.
There is nothing as inevitable as a re-org when a new VP joins. When a new executive joins they’re often overwhelmed by the amount of context they need to absorb to start being effective. The more seasoned ones aren’t pertrubed by this: they understand that gathering this context is their full-time job for the next several weeks or months. There’s even a book about this period. The less savvy ones, on the other hand, often reach for one of the following coping strategies, depending on the type of role they occupy. This organization makes no sense, we must re-organize it immediately Spoken by a newly joined VP who needs to assess the organization and understand why it is set up the way it is. It results in several workshops about boundaries, Conway’s law and team topologies result in a slightly different, but not materially significant organization. And a VP with a much better understanding of their people, the culture, the product and the challenges. We must document/map it Spoken by a product manager getting to grips with the features they’ll be working on before having read the abundant sales, technical and product reference materials. This usually results in several workshops where there is a lot of “discovery” and “mapping”. In reality, the product manager is getting an in-person crash course. It rarely results in any new discoveries or documentation or maps being produced but always results in a much more confident product manager. We must have a process for that Spoken by a new engineering manager who’s not yet familiar with the existing processes and ways of working. This usually results in the engineering manager starting to write a Confluence page on how the process should work, until one of the team members sends them an existing, but finished, Confluence page on exactly that, but with slight differences. The new page gets a link to the existing ones and is promptly forgotten. Does this process really work for anyone? A sub-category of the above then the process in place is different from their previous employer. This code is so bad, we must re-write it entirely Spoken by a senior but not yet quite staff engineer who’s just getting to grips with a new codebase – often about code that generates 7 or 8 digits in revenue. It results in the engineer spending several hours on an alternative architecture and running it by their team several times. Eventually, they understand that what they’re suggesting is quite similar to what is actually in place, that there is some refactoring and improvements to be done, but it’s nowhere near as tragic as they imagined it to be. Why does this happen? A week or two after joining, depending on how generous the company is, the engineer gets a ticket to work on, the PM is asked about the backlog priority and the EM why their bug injection rate is so high and what they’re doing about it. And they naturally feel lost. The problem is that most companies don’t set an expected timeline for having a person become effective in their position. How to do better? The amount of context required to be effective increases with seniority. But everyone needs a couple of weeks outside of the default onboarding programme to read through their team’s wiki space, to look through the backlog, to pair with their colleagues, to get an understanding of the work the team is doing, to be present at the retrospectives to listen and not have to lead and facilitate. Only after they get the lay of the land can they start contributing in a meaningful way. The managerial fear of the unknown was originally published by Ognjen Regoje at Ognjen Regoje • ognjen.io on April 17, 2025.
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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!
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
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.
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 ↩
