I want to raise a point that so far I have not seen in the top N comments: Often simplicity and cleverness are not opposites, because you can only find the simplest way to express something, when you are clever about it. Your solution doesn't have to "smell clever", no, it can be deceptively simple, making the casual reader think "of course it works this way", but when such casual reader was supposed to come up with such a solution, they might invent something unnecessarily convoluted, because their mind has not cut through all the stuff like a knife through butter and they were not so clever. To find the simplest abstraction, that is still able to represent everything you need to represent, and at the same time doesn't have unpleasant consequences like being very slow, or super difficult to understand is the actual cleverness. Writing code no one can understand is not actually that clever. It might be clever in terms of oneself solving a problem at a specific time, but it is not so clever in terms of that thing being maintainable, adaptable, readable, improvable, in the mid and long term.

We don't write everything in lambda calculus or SKI calculus or something like that, even though those things are made up of very simple parts, complexity would emerge. They are not the right choice for most tasks. How do you make it readable? Can you write something that is very clear and performant at the same time, while preventing yourself from programming yourself into a corner? That's where you need to get really clever.

Simplicity and complexity are not opposites. Things become complex when we attend to multiple simple things at the same time.

For example, we have an algorithm that requires a key-value store with typical semantic. For the purposes of our algorithm we could simulate that store using an array and straightforward search and insert routines that just loop through the array without trying to be smart. Then we could attend to details of that key-value store and use a more efficient approach, this time without thinking about our original algorithm; or, perhaps, with a clear understanding of its access pattern.

In both cases the task at hand won't be more complex than necessary. But if we try to do both at the same time, it will be way more complex.

Here the separation is clear, but in real programming it is not clear and to discover these lines of separations is basically the essence of building a system. I think Brad Cox was occupied with that with his Software-IC concept and I kind of share his view that this is yet to happen. Things we build are not as composable as they should be; as they are in other industries.

While I agree with MacKinnon in principle, we must acknowledge that simplicity is a luxury enabled by the surplus of compute power. > There are domains where 'cleverness' isn't just a vanity project, but a hard requirement. Take the Fast Inverse Square Root from Quake III or modern Zero-copy networking in high-throughput systems like Kafka. If we prioritized 'simple and readable' code in those contexts, we would be leaving orders of magnitude of performance on the table.

Sometimes, 'clever' code is simply code that refuses to ignore the underlying reality of the hardware. The danger isn't cleverness itself, but unnecessary cleverness applied to problems where the bottleneck is human understanding rather than machine execution.

I try to put clever solutions in their own file so that I can later replace them with something boring.

Really solid discussion on maintainability. I liked the recurring theme that clarity beats cleverness, especially the points on over-abstraction RFCs as a cultural tool and documentation focusing on why instead of restating code. The examples from consulting and legacy systems made it feel very grounded and practical.

What I truly enjoy is software that has:

- One tiny piece of extremely clever abstraction.

- A huge amount of simple pieces that would be more complex without that tiny piece.

In other words, the clever abstraction can be justified if it enables lots of simplicity. It has to do it right now, not in the future.

If your kernel is complicated but writing drivers is simple, people won't even notice the abstractions. They will think of the system as "simple", without realizing there is some clever stuff making that "simple" possible.

People talk about "complexity" and "simplicity" in code without defining them. I've arrived at a way of looking it that I think makes it reasonably unambiguous. I prefer "opaque" to "complicated" for this concept, but I think it's really the same thing people mean when they talk about code being over-complicated.

Opaque code is code that requires you to form an unnecessarily large, detailed mental model of it in order to answer a particular question you may have about it.

People rarely read code in its entirety, like a novel. There is almost always a specific question they want to answer. It might be "how will it behave in this use case?", "how will this change affect its behaviour?" or "what change should I make it to achieve this new behaviour?". Alternatively, it might be something more high level, but still specific, like "how does this fit together?" (i.e. there's a desire to understand the overall organisational principles of the code, rather than a specific detail).

Opaque code typically:

* Requires you to read and understand large volumes of what should be irrelevant code in order to answer your question, often across multiple codebases.

* Requires you to do difficult detective work in order to identify what code needs to be read and understood to answer the question with confidence.

* Only provides an answer to your question with caveats/assumptions about human behaviour, such as "well unless someone has done X somewhere, but I doubt anyone would do that and would have to read the entire codebase to be sure".

Of course, this doesn't yield some number as to how "opaque" the code is, and importantly it depends on the question you're asking. A codebase might be quite transparent to some questions and opaque to others. It can be a very useful exercise to think about what questions people are likely to seek answers for from a given codebase.

When you think about things this way, you come to realise a lot of supposedly good practices actually exacerbate code opacity, often for the sake of "reusability" of things that will never be reused. Dependency injection containers are a bête noire of mine for this reason. There's nothing wrong with dependency injection itself (giving things their dependencies rather than having them create them), but DI containers tend to end up being dependency obfuscators, and the worst ones import a huge amount of quirky, often poorly-documented behaviour into your system. They are probably the single biggest cause of having to spend an entire afternoon trawling through code, often including that of the blasted container itself (and runtime config!), to answer what should be a very simple and quick question about a codebase.

"Clever" is a different thing to "complicated" or "opaque", and it's not always a negative. People can certainly make code much more opaque by doing "clever" things, but sometimes (maybe rather too rarely) they can do the opposite. A small, well thought out bit of "clever" code can often greatly reduce the opacity of a much larger amount of code that uses it. Thinking about what a particular "clever" idea will do to the opacity (as defined above) of the codebase can be a good way to figure out whether it is worth doing.

Obvious is good. Optimization can come later. Cleverness is for when you are out of options.

The programming landscape 30+ years ago and its severely constrained resources strongly biased our idea of "good software" in favor of cleverness. I think we can say we know better now. Having been responsible for picking up someone else's clever code myself.