This one is a hot mess but it's too late in the week to start over. Oh well! Someone recognized me at last week's Chipy and asked for my opinion on Sapir-Whorf hypothesis in programming languages. I thought this was interesting enough to make a newsletter. First what it is, then why it looks like it applies, and then why it doesn't apply after all. The Sapir-Whorf Hypothesis We dissect nature along lines laid down by our native language. — Whorf To quote from a Linguistics book I've read, the hypothesis is that "an individual's fundamental perception of reality is moulded by the language they speak." As a massive oversimplification, if English did not have a word for "rebellion", we would not be able to conceive of rebellion. This view, now called Linguistic Determinism, is mostly rejected by modern linguists. The "weak" form of SWH is that the language we speak influences, but does not decide our cognition. For example, Russian has distinct words for "light blue" and "dark blue", so can discriminate between "light blue" and "dark blue" shades faster than they can discriminate two "light blue" shades. English does not have distinct words, so we discriminate those at the same speed. This linguistic relativism seems to have lots of empirical support in studies, but mostly with "small indicators". I don't think there's anything that convincingly shows linguistic relativism having effects on a societal level.1 The weak form of SWH for software would then be the "the programming languages you know affects how you think about programs." SWH in software This seems like a natural fit, as different paradigms solve problems in different ways. Consider the hardest interview question ever, "given a list of integers, sum the even numbers". Here it is in four paradigms: Procedural: total = 0; foreach x in list {if IsEven(x) total += x}. You iterate over data with an algorithm. Functional: reduce(+, filter(IsEven, list), 0). You apply transformations to data to get a result. Array: + fold L * iseven L.2 In English: replace every element in L with 0 if odd and 1 if even, multiple the new array elementwise against L, and then sum the resulting array. It's like functional except everything is in terms of whole-array transformations. Logical: Somethingish like sumeven(0, []). sumeven(X, [Y|L]) :- iseven(Y) -> sumeven(Z, L), X is Y + Z ; sumeven(X, L). You write a set of equations that express what it means for X to be the sum of events of L. There's some similarities between how these paradigms approach the problem, but each is also unique, too. It's plausible that where a procedural programmer "sees" a for loop, a functional programmer "sees" a map and an array programmer "sees" a singular operator. I also have a personal experience with how a language changed the way I think. I use TLA+ to detect concurrency bugs in software designs. After doing this for several years, I've gotten much better at intuitively seeing race conditions in things even without writing a TLA+ spec. It's even leaked out into my day-to-day life. I see concurrency bugs everywhere. Phone tag is a race condition. But I still don't think SWH is the right mental model to use, for one big reason: language is special. We think in language, we dream in language, there are huge parts of our brain dedicated to processing language. We don't use those parts of our brain to read code. SWH is so intriguing because it seems so unnatural, that the way we express thoughts changes the way we think thoughts. That I would be a different person if I was bilingual in Spanish, not because the life experiences it would open up but because grammatical gender would change my brain. Compared to that, the idea that programming languages affect our brain is more natural and has a simpler explanation: It's the goddamned Tetris Effect. The Goddamned Tetris Effect The Tetris effect occurs when someone dedicates vast amounts of time, effort and concentration on an activity which thereby alters their thoughts, dreams, and other experiences not directly linked to said activity. — Wikipedia Every skill does this. I'm a juggler, so every item I can see right now has a tiny metadata field of "how would this tumble if I threw it up". I teach professionally, so I'm always noticing good teaching examples everywhere. I spent years writing specs in TLA+ and watching the model checker throw concurrency errors in my face, so now race conditions have visceral presence. Every skill does this. And to really develop a skill, you gotta practice. This is where I think programming paradigms do something especially interesting that make them feel more like Sapir-Whorfy than, like, juggling. Some languages mix lots of different paradigms, like Javascript or Rust. Others like Haskell really focus on excluding paradigms. If something is easy for you in procedural and hard in FP, in JS you could just lean on the procedural bits. In Haskell, too bad, you're learning how to do it the functional way.3 And that forces you to practice, which makes you see functional patterns everywhere. Tetris effect! Anyway this may all seem like quibbling— why does it matter whether we call it "Tetris effect" or "Sapir-Whorf", if our brains is get rewired either way? For me, personally, it's because SWH sounds really special and unique, while Tetris effect sounds mundane and commonplace. Which it is. But also because TE suggests it's not just programming languages that affect how we think about software, it's everything. Spending lots of time debugging, profiling, writing exploits, whatever will change what you notice, what you think a program "is". And that's a way useful idea that shouldn't be restricted to just PLs. (Then again, the Tetris Effect might also be a bad analogy to what's going on here, because I think part of it is that it wears off after a while. Maybe it's just "building a mental model is good".) I just realized all of this might have missed the point Wait are people actually using SWH to mean the weak form or the strong form? Like that if a language doesn't make something possible, its users can't conceive of it being possible. I've been arguing against the weaker form in software but I think I've seen strong form often too. Dammit. Well, it's already Thursday and far too late to rewrite the whole newsletter, so I'll just outline the problem with the strong form: we describe the capabilities of our programming languages with human language. In college I wrote a lot of crappy physics lab C++ and one of my projects was filled with comments like "man I hate copying this triply-nested loop in 10 places with one-line changes, I wish I could put it in one function and just take the changing line as a parameter". Even if I hadn't encountered higher-order functions, I was still perfectly capable of expressing the idea. So if the strong SWH isn't true for human language, it's not true for programming languages either. Systems Distributed talk now up! Link here! Original abstract: Building correct distributed systems takes thinking outside the box, and the fastest way to do that is to think inside a different box. One different box is "formal methods", the discipline of mathematically verifying software and systems. Formal methods encourages unusual perspectives on systems, models that are also broadly useful to all software developers. In this talk we will learn two of the most important FM perspectives: the abstract specifications behind software systems, and the property they are and aren't supposed to have. The talk ended up evolving away from that abstract but I like how it turned out! There is one paper arguing that people who speak a language that doesn't have a "future tense" are more likely to save and eat healthy, but it is... extremely questionable. ↩ The original J is +/ (* (0 = 2&|)). Obligatory Notation as a Tool of Thought reference ↩ Though if it's too hard for you, that's why languages have escape hatches ↩

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 ↩

The excellent-but-defunct blog Programming in the 21st Century defines "puzzle languages" as languages were part of the appeal is in figuring out how to express a program idiomatically, like a puzzle. As examples, he lists Haskell, Erlang, and J. All puzzle languages, the author says, have an "escape" out of the puzzle model that is pragmatic but stigmatized. But many mainstream languages have escape hatches, too. Languages have a lot of properties. One of these properties is the language's capabilities, roughly the set of things you can do in the language. Capability is desirable but comes into conflicts with a lot of other desirable properties, like simplicity or efficiency. In particular, reducing the capability of a language means that all remaining programs share more in common, meaning there's more assumptions the compiler and programmer can make ("tractability"). Assumptions are generally used to reason about correctness, but can also be about things like optimization: J's assumption that everything is an array leads to high-performance "special combinations". Rust is the most famous example of mainstream language that trades capability for tractability.1 Rust has a lot of rules designed to prevent common memory errors, like keeping a reference to deallocated memory or modifying memory while something else is reading it. As a consequence, there's a lot of things that cannot be done in (safe) Rust, like interface with an external C function (as it doesn't have these guarantees). To do this, you need to use unsafe Rust, which lets you do additional things forbidden by safe Rust, such as deference a raw pointer. Everybody tells you not to use unsafe unless you absolutely 100% know what you're doing, and possibly not even then. Sounds like an escape hatch to me! To extrapolate, an escape hatch is a feature (either in the language itself or a particular implementation) that deliberately breaks core assumptions about the language in order to add capabilities. This explains both Rust and most of the so-called "puzzle languages": they need escape hatches because they have very strong conceptual models of the language which leads to lots of assumptions about programs. But plenty of "kitchen sink" mainstream languages have escape hatches, too: Some compilers let C++ code embed inline assembly. Languages built on .NET or the JVM has some sort of interop with C# or Java, and many of those languages make assumptions about programs that C#/Java do not. The SQL language has stored procedures as an escape hatch and vendors create a second escape hatch of user-defined functions. Ruby lets you bypass any form of encapsulation with send. Frameworks have escape hatches, too! React has an entire page on them. (Does eval in interpreted languages count as an escape hatch? It feels different, but it does add a lot of capability. Maybe they don't "break assumptions" in the same way?) The problem with escape hatches In all languages with escape hatches, the rule is "use this as carefully and sparingly as possible", to the point where a messy solution without an escape hatch is preferable to a clean solution with one. Breaking a core assumption is a big deal! If the language is operating as if its still true, it's going to do incorrect things. I recently had this problem in a TLA+ contract. TLA+ is a language for modeling complicated systems, and assumes that the model is a self-contained universe. The client wanted to use the TLA+ to test a real system. The model checker should send commands to a test device and check the next states were the same. This is straightforward to set up with the IOExec escape hatch.2 But the model checker assumed that state exploration was pure and it could skip around the state randomly, meaning it would do things like set x = 10, then skip to set x = 1, then skip back to inc x; assert x == 11. Oops! We eventually found workarounds but it took a lot of clever tricks to pull off. I'll probably write up the technique when I'm less busy with The Book. The other problem with escape hatches is the rest of the language is designed around not having said capabilities, meaning it can't support the feature as well as a language designed for them from the start. Even if your escape hatch code is clean, it might not cleanly integrate with the rest of your code. This is why people complain about unsafe Rust so often. It should be noted though that all languages with automatic memory management are trading capability for tractability, too. If you can't deference pointers, you can't deference null pointers. ↩ From the Community Modules (which come default with the VSCode extension). ↩

I'm a big (neo)vim buff. My config is over 1500 lines and I regularly write new scripts. I recently ported my neovim config to a new laptop. Before then, I was using VSCode to write, and when I switched back I immediately saw a big gain in productivity. People often pooh-pooh vim (and other assistive writing technologies) by saying that writing code isn't the bottleneck in software development. Reading, understanding, and thinking through code is! Now I don't know how true this actually is in practice, because empirical studies of time spent coding are all over the place. Most of them, like this study, track time spent in the editor but don't distinguish between time spent reading code and time spent writing code. The only one I found that separates them was this study. It finds that developers spend only 5% of their time editing. It also finds they spend 14% of their time moving or resizing editor windows, so I don't know how clean their data is. But I have a bigger problem with "writing is not the bottleneck": when I think of a bottleneck, I imagine that no amount of improvement will lead to productivity gains. Like if a program is bottlenecked on the network, it isn't going to get noticeably faster with 100x more ram or compute. But being able to type code 100x faster, even with without corresponding improvements to reading and imagining code, would be huge. We'll assume the average developer writes at 80 words per minute, at five characters a word, for 400 characters a minute.What could we do if we instead wrote at 8,000 words/40k characters a minute? Writing fast Boilerplate is trivial Why do people like type inference? Because writing all of the types manually is annoying. Why don't people like boilerplate? Because it's annoying to write every damn time. Programmers like features that help them write less! That's not a problem if you can write all of the boilerplate in 0.1 seconds. You still have the problem of reading boilerplate heavy code, but you can use the remaining 0.9 seconds to churn out an extension that parses the file and presents the boilerplate in a more legible fashion. We can write more tooling This is something I've noticed with LLMs: when I can churn out crappy code as a free action, I use that to write lots of tools that assist me in writing good code. Even if I'm bottlenecked on a large program, I can still quickly write a script that helps me with something. Most of these aren't things I would have written because they'd take too long to write! Again, not the best comparison, because LLMs also shortcut learning the relevant APIs, so also optimize the "understanding code" part. Then again, if I could type real fast I could more quickly whip up experiments on new apis to learn them faster. We can do practices that slow us down in the short-term Something like test-driven development significantly slows down how fast you write production code, because you have to spend a lot more time writing test code. Pair programming trades speed of writing code for speed of understanding code. A two-order-of-magnitude writing speedup makes both of them effectively free. Or, if you're not an eXtreme Programming fan, you can more easily follow the The Power of Ten Rules and blanket your code with contracts and assertions. We could do more speculative editing This is probably the biggest difference in how we'd work if we could write 100x faster: it'd be much easier to try changes to the code to see if they're good ideas in the first place. How often have I tried optimizing something, only to find out it didn't make a difference? How often have I done a refactoring only to end up with lower-quality code overall? Too often. Over time it makes me prefer to try things that I know will work, and only "speculatively edit" when I think it be a fast change. If I could code 100x faster it would absolutely lead to me trying more speculative edits. This is especially big because I believe that lots of speculative edits are high-risk, high-reward: given 50 things we could do to the code, 49 won't make a difference and one will be a major improvement. If I only have time to try five things, I have a 10% chance of hitting the jackpot. If I can try 500 things I will get that reward every single time. Processes are built off constraints There are just a few ideas I came up with; there are probably others. Most of them, I suspect, will share the same property in common: they change the process of writing code to leverage the speedup. I can totally believe that a large speedup would not remove a bottleneck in the processes we currently use to write code. But that's because those processes are developed work within our existing constraints. Remove a constraint and new processes become possible. The way I see it, if our current process produces 1 Utils of Software / day, a 100x writing speedup might lead to only 1.5 UoS/day. But there are other processes that produce only 0.5 UoS/d because they are bottlenecked on writing speed. A 100x speedup would lead to 10 UoS/day. The problem with all of this that 100x speedup isn't realistic, and it's not obvious whether a 2x improvement would lead to better processes. Then again, one of the first custom vim function scripts I wrote was an aid to writing unit tests in a particular codebase, and it lead to me writing a lot more tests. So maybe even a 2x speedup is going to be speed things up, too. Patreon Stuff I wrote a couple of TLA+ specs to show how to model fork-join algorithms. I'm planning on eventually writing them up for my blog/learntla but it'll be a while, so if you want to see them in the meantime I put them up on Patreon.