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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. ↩
One of the best known hard problems in computer science is the halting problem. In fact, it's widely thought[1] that you cannot write a program that will, for any arbitrary program as input, tell you correctly whether or not it will terminate. This is written from the framing of computers, though: can we do better with a human in the loop? It turns out, we can. And we can use a method that's generalizable, which many people can follow for many problems. Not everyone can use the method, which you'll see why in a bit. But lots of people can apply this proof technique. Let's get started. * * * We'll start by formalizing what we're talking about, just a little bit. I'm not going to give the full formal proof—that will be reserved for when this is submitted to a prestigious conference next year. We will call the set of all programs P. We want to answer, for any p in P, whether or not p will eventually halt. We will call this h(p) and h(p) = true if p eventually finished and false otherwise. Actually, scratch that. Let's simplify it and just say that yes, every program does halt eventually, so h(p) = true for all p. That makes our lives easier. Now we need to get from our starting assumptions, the world of logic we live in, to the truth of our statement. We'll call our goal, that h(p) = true for all p, the statement H. Now let's start with some facts. Fact one: I think it's always an appropriate time to play the saxophone. *honk*! Fact two: My wife thinks that it's sometimes inappropriate to play the saxophone, such as when it's "time for bed" or "I was in the middle of a sentence![2] We'll give the statement "It's always an appropriate time to play the saxophone" the name A. We know that I believe A is true. And my wife believes that A is false. So now we run into the snag: Fact three: The wife is always right. This is a truism in American culture, useful for settling debates. It's also useful here for solving major problems in computer science because, babe, we're both the wife. We're both right! So now that we're both right, we know that A and !A are both true. And we're in luck, we can apply a whole lot of fancy classical logic here. Since A and !A we know that A is true and we also know that !A is true. From A being true, we can conclude that A or H is true. And then we can apply disjunctive syllogism[3] which says that if A or H is true and !A is true, then H must be true. This makes sense, because if you've excluded one possibility then the other must be true. And we do have !A, so that means: H is true! There we have it. We've proved our proposition, H, which says that for any program p, p will eventually halt. The previous logic is, mostly, sound. It uses the principle of explosion, though I prefer to call it "proof by married lesbian." * * * Of course, we know that this is wrong. It falls apart with our assumptions. We built the system on contradictory assumptions to begin with, and this is something we avoid in logic[4]. If we allow contradictions, then we can prove truly anything. I could have also proved (by married lesbian) that no program will terminate. This has been a silly traipse through logic. If you want a good journey through logic, I'd recommend Hillel Wayne's Logic for Programmers. I'm sure that, after reading it, you'll find absolutely no flaws in my logic here. After all, I'm the wife, so I'm always right. It's widely thought because it's true, but we don't have to let that keep us from a good time. ↩ I fact checked this with her, and she does indeed hold this belief. ↩ I had to look this up, my uni logic class was a long time ago. ↩ The real conclusion to draw is that, because of proof by contradiction, it's certainly not true that the wife is always right. Proved that one via married lesbians having arguments. Or maybe gay relationships are always magical and happy and everyone lives happily ever after, who knows. ↩
I've been publishing at least one blog post every week on this blog for about 2.5 years. I kept it up even when I was very sick last year with Lyme disease. It's time for me to take a break and reset. This is the right time, because the world is very difficult for me to move through right now and I'm just burnt out. I need to focus my energy on things that give me energy and right now, that's not writing and that's not tech. I'll come back to this, and it might look a little different. This is my last post for at least a month. It might be longer, if I still need more time, but I won't return before the end of May. I know I need at least that long to heal, and I also need that time to focus on music. I plan to play a set at West Philly Porchfest, so this whole month I'll be prepping that set. If you want to follow along with my music, you can find it on my bandcamp (only one track, but I'll post demos of the others that I prepare for Porchfest as they come together). And if you want to reach out, my inbox is open. Be kind to yourself. Stay well, drink some water. See you in a while.
A few months ago, I was talking with a friend about my ergonomic setup and they asked if being vertical helps it with cooling. I wasn't sure, because it seems like it could help but it was probably such a small difference that it wouldn't matter. So, I did what any self-respecting nerd would do: I procrastinated. The question didn't leave me, though, so after those months passed, I did the second thing any self-respecting nerd would do: benchmarks. The question and the setup What we want to find out is whether or not the position of the laptop would affect its CPU performance. I wanted to measure it in three positions: normal: using it the way any normal person uses their laptop, with the screen and keyboard at something like a 90-degree angle closed: using it like a tech nerd, closed but plugged into a monitor and peripherals vertical: using it like a weird blogger who has sunk a lot of time into her ergonomic setup and wants to justify it even further My hypothesis was that using it closed would slightly reduce CPU performance, and that using it normal or vertical would be roughly the same. For this experiment, I'm using my personal laptop. It's one of the early Framework laptops (2nd batch of shipments) which is about four years old. It has an 11th gen Intel CPU in it, the i7-1165G7. My laptop will be sitting on a laptop riser for the closed and normal positions, and it will be sitting in my ergonomic tray for the vertical one. For all three, it will be connected to the same set of peripherals through a single USB-C cable, and the internal display is disabled for all three. Running the tests I'm not too interested in the initial boost clock. I'm more interested in what clock speeds we can sustain. What happens under a sustained, heavy load, when we hit a saturation point and can't shed any more heat? To test that, I'm doing a test using heavy CPU load. The load is generated by stress-ng, which also reports some statistics. Most notably, it reports CPU temperatures and clock speeds during the tests. Here's the script I wrote to make these consistent. To skip the boost clock period, I warm it up first with a 3-minute load Then I do a 5-minute load and measure the CPU clock frequency and CPU temps every second along the way. #!/bin/bash # load the CPU for 3 minutes to warm it up sudo stress-ng --matrix $2 -t 3m --tz --raplstat 1 --thermalstat 1 -Y warmup-$1.yaml --log-file warmup-$1.log --timestamp --ignite-cpu # run for 5 minutes to gather our averages sudo stress-ng --matrix $2 -t 5m --tz --raplstat 1 --thermalstat 1 -Y cputhermal-$1.yaml --log-file cputhermal-$1.log --timestamp --ignite-cpu We need sudo since we're using an option (--ignite-cpu) which needs root privileges[1] and attempts to make the CPU run harder/hotter. Then we specify the stressor we're using with --matrix $2, which does some matrix calculations over a number of cores we specify. The remaining options are about reporting and logging. I let the computer cool for a minute or two between each test, but not for a scientific reason. Just because I was doing other things. Since my goal was to saturate the temperatures, and they got stable within each warmup period, cooldowh time wasn't necessary—we'd warm it back up anyway. So, I ran this with the three positions, and with two core count options: 8, one per thread on my CPU; and 4, one per physical core on my CPU. The results Once it was done, I analyzed the results. I took the average clock speed across the 5 minute test for each of the configurations. My hypothesis was partially right and partially wrong. When doing 8 threads, each position had different results: Our baseline normal open position had an average clock speed of 3.44 GHz and an average CPU temp of 91.75 F. With the laptop closed, the average clock speed was 3.37 GHz and the average CPU temp was 91.75 F. With the laptop open vertical, the average clock speed was 3.48 GHz and the average CPU temp was 88.75 F. With 4 threads, the results were: For the baseline normal open position, the average clock speed was 3.80 GHz with average CPU temps of 91.11 F. With the laptop closed, the average clock speed was 3.64 GHz with average CPU temps of 90.70 F. With the laptop open vertical, the average clock speed was 3.80 GHz with average CPU temps of 86.07 F. So, I was wrong in one big aspect: it does make a clearly measurable difference. Having it open and vertical reduces temps by 3 degrees in one test and 5 in the other, and it had a higher clock speed (by 0.05 GHz, which isn't a lot but isn't nothing). We can infer that, since clock speeds improved in the heavier load test but not in the lighter load test, that the lighter load isn't hitting our thermal limits—and when we do, the extra cooling from the vertical position really helps. One thing is clear: in all cases, the CPU ran slower when the laptop was closed. It's sorta weird that the CPU temps went down when closed in the second test. I wonder if that's from being able to cool down more when it throttled down a lot, or if there was a hotspot that throttled the CPU but which wasn't reflected in the temp data, maybe a different sensor. I'm not sure if having my laptop vertical like I do will ever make a perceptible performance difference. At any rate, that's not why I do it. But it does have lower temps, and that should let my fans run less often and be quieter when they do. That's a win in my book. It also means that when I run CPU-intensive things (say hi to every single Rust compile!) I should not close the laptop. And hey, if I decide to work from my armchair using my ergonomic tray, I can argue it's for efficiency: boss, I just gotta eke out those extra clock cycles. I'm not sure that this made any difference on my system. I didn't want to rerun the whole set without it, though, and it doesn't invalidate the tests if it simply wasn't doing anything. ↩
More in programming
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 ↩
