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The author of "Choose boring technology" regretted the choice of the word "boring" [1].

Anyway, boring is bad. Boring is what spends your attention on irrelevant things. Cobol's syntax is boring in a bad way. Go's error handling is boring in a bad way. Manually clicking through screens again and again because you failed to write UI tests is boring in a bad way.

What could be "boring in a good way" is something that gets things done and gets out of your way. Things like HTTPS, or S3, or your keyboard once you have leaned touch typing, are "boring in a good way". They have no concealed surprises, are well-tested in practice, and do what they say on the tin, every time.

New and shiny things can be "boring in the good way", e.g. uv [2]. Old and established things can be full of (nasty) surprises, and, in this regard, the opposite of boring, e.g. C++.

[1]: https://boringtechnology.club/#30

[2]: https://github.com/astral-sh/uv

I feel that with LLMs and AI, people are furiously trying to argue the reality they desire into existence. I've never read more articles predicting the future than on this topic (I am guilty of it, too.)

I tend to think that the reason people over-index on complex use-cases for LLMs is actually reliability, not a lack of interest in boring projects.

If an LLM can solve a complex problem 50% of the time, then that is still very valuable. But if you are writing a system of small LLMs doing small tasks, then even 1% error rates can compound into highly unreliable systems when stacked together.

The cost of LLMs occasionally giving you wrong answers is worth it for answers to harder tasks, in a way that it is not worth it for smaller tasks. For those smaller tasks, usually you can get much closer to 100% reliability, and more importantly much greater predictability, with hand-engineered code. This makes it much harder to find areas where small LLMs can add value for small boring tasks. Better auto-complete is the only real-world example I can think of.

"LLMs are not intelligent and they never will be."

If he means they will never outperform humans at cognitive or robotics tasks, that's a strong claim!

If he just means they aren't conscious... then let's don't debate it any more here. :-)

I agree that we could be in a bubble at the moment though.

I think this is, essentially, a wishful take. The biggest barrier to models being able to do more advanced knowledge work is creating appropriately annotated training data, followed by a few specific technical improvements the labs are working on. Models have already nearly maxed out "work on a well-defined puzzle that can be feasibly solved in a few hours" -- stunning! -- and now labs will turn to expanding other dimensions.

The investment fund that acquired the company that acquired our company requests all that all companies it owns go big on cloud and AI, no matter what, because this raises valuation and they can sell them for bigger profits.

I have nothing against cloud or AI per se, but I still believe in the right tool for the right job and in not doing things just for the sake of it. While raising valuation is a good thing, raising costs, delaying more useful features and adding complexity should also be taken into account.

Great take. I personally find the thought of spec-driven development tedious and boring. But maybe that’s a good thing.

I like this article, and I didn't expect to because there's been volumes written about how you should be boring and building things in an interesting way just for the hell of it, is bad (something I don't agree with).

Small models doing interesting (boring to the author) use-cases is a fine frontier!

I don't agree at all with this though:

> "LLMs are not intelligent and they never will be."

LLMs already write code better than most humans. The problem is we expect them to one-shot things that a human may spend many hours/days/weeks/months doing. We're lacking coordination for long-term LLM work. The models themselves are probably even more powerful than we realize, we just need to get them to "think" as long as a human would.

OT: Since the author is a former Apple UX designer who worked on the Human Interface Guidelines, I hope he shares his thoughts on the recent macOS 26 and iOS updates - especially on Liquid Glass.

https://jenson.org/about-scott/

> He uses the example of the dynamo, an old-fashioned term for a powerful electric motor.

um, dynamo is a generator, it takes mechanical energy and turns into to electricity.

I also agree that boring is good, but in our current society you won't get a job for being boring, and when you get a job, it's is guaranteed you are not being paid to solve problems.