> Event sourcing: the version problem as a way of life
> Temporal and bitemporal databases: time as a first-class citizen
> Semantic drift: the type didn’t change, but the meaning did
> Knowing what’s running changes everything
> What if the old code just kept working?
> The right tools, pointed at the wrong level
Presentation matters as much as content. Particularly if you want somebody to read 10,000 words, making that reading go down smoothly is a good thing to strive for. If this was by chance written by a human who happened to have absorbed LLM-like writing tendencies, I would still find fault in this article for how it is written, and would suggest they spend more time revising it rather than publishing a 5k-10k word technical article daily. Much like writing code, sheer lines written is not the goal; the actual goal is to succinctly and clearly represent your ideas in as refined a form as possible. This article dragged on and on and on, with fatiguing prose, for an idea that can be well expressed without such length.
Perhaps this is just a form of technical writing you're unfamiliar with? Those titles are pretty standard for what I consider good technical writing section headers. LLM writing tendencies are tendencies LLMs have integrated by encountering those tendencies. If your assessment standard for AI is just "common best practices for a subset of good writers", then I think perhaps you need to adjust how you assess to be a bit more nuanced.
For some reason people frequently suggest that my problem with LLM writing is that it's too good. Allow me to restate that I find fault with how the article is written, and that I do not in any way perceive this to be good writing. The flaws happen to manifest in a way that I would expect LLM flaws to manifest, which I also do not find to be good writing. I do not find LLMs to have absorbed good technical writing tendencies at all. Instead they absorb sensationalist tendencies that are likely both more common in their dataset and that are likely intentionally selected for in the reinforcement learning phase. Writing which is effective, in the same way that clickbait headlines and Youtube thumbnails are effective, but not good. I felt as though this article was, through its headers and overuse of specific rhetorical devices, constantly trying to grab my attention in that same shallow manner. This gets tiring at length, and good technical writing does not need to engage in such tendencies.
If you disagree and find this to be good writing, you are entitled to your opinion, but nonetheless this is my own feedback on the article.
Sure, I guess? I feel like this is getting rather in the weeds and will not necessarily lead the conversation in any kind of particularly productive direction, but I will nonetheless take the opportunity to promote what I consider to be excellent writing. Dan Luu is a favorite of mine, and offers what I find to be a much more rewarding use of reading time. A sample picked basically at random: https://danluu.com/ftc-google-antitrust/
> For some reason people frequently suggest that my problem with LLM writing is that it's too good.
> I felt as though this article was, through its headers and overuse of specific rhetorical devices, constantly trying to grab my attention in that same shallow manner.
I think perhaps you're quick to assess a certain type of writing, which many see as done quite well and in a way that's approachable and is good at retaining interest, as AI. Perhaps you just don't like this type of writing that many do, and AI tries to emulate it, and you're keying on specific aspects of both the original and the emulation and because you don't appreciate either it's hard for you to discern between them? Or maybe there is no difference between the AI and non-AI articles that utilize these, and it's just your dislike of them which colors your view?
I, for one, found the article fairly approachable and easy to read given the somewhat niche content and that it was half survey of the current state of our ability to handle change in systems like these. Then again, I barely pay any attention to section titles. I couldn't even remember reading the ones you presented. Perhaps I've trained myself to see them just as section separators.
In any case, nothing in this stuck out as AI generated to me, and if it was, it was well enough done that I don't feel I wasted any time reading it.
I am a technical writer. This article is not good technical writing.
Good technical writing allows you to get to and understand the point in a minimum of time, has a clear and obvious structure, and organizes concepts in such a way that their key relationships are readily apparent. In my opinion this article achieves none of these things (and it also is just bad insofar as its thesis is confused and misleading in a very basic way—namely the relationship between functional programming philosophy and distributed systems design is far more aligned than it suggests, and it sets up a false dichotomy of FP versus systems, when really the dichotomy is just one of different levels of design (one could write the exact same slop article about what OOP "gets wrong" about systems—it gets it "wrong" because low level programming paradigms techniques are in fact about structuring programs, not systems, and system design is largely up to designers—the thesis is basically "why don't these pragmatic program-leave techniques help me design systems at scale" or in other words "why don't all these hammering techniques help me design a house?")
I would only loosely categorize this as technical writing, depending on how you categorize technical writing. It seems much more a survey of problems and discussion piece, with notes about projects making inroads on the problem. It's definitely not a "this is how you solve this problem, and these are the clear steps to do so" type of article. Maybe that's some of the disconnect in how we view it. If I was hoping that this communicated a clear procedure or how to accomplish something, I would be disappointed. I don't think that was their intention.
I came away with some additional understanding of the problem, and thinking there are various nascent techniques to address this problem, none of them entirely sufficient, but that it's being worked on from multiple directions. I'm not sure the article was aiming for more than that.
I'm a highly literate reader and writer of technical topics, and there are a lot of bad technical writers who think they aren't. Except perhaps for the title, which is way too narrow, the article is excellent writing about a technical topic (which is quite different from technical writing)--but then I actually read it, so I know that he doesn't talk about a dichotomy between FP and systems, but rather between single programs and systems, and he explicitly says that his points aren't restricted to FP, but that because FP addresses the single program issues so well, FP programmers are particularly prone to missing the problem.
I feel that people forget that even amateur (Imho) level apps like dance ejay etc exist for 20+ years.
Imho electronic music creation is for a long time not a hard solution, the problem is making something to sound good :) (and good is ofc based on everyone's taste)
My 2 cents, just make the damn meetings 20 minutes or 50 minutes, and start at sharp. I absolutely hate the 5 minutes past, as I am ALWAYS late on them, or 5 minutes early.
Fixed time makes it so much easier to reason than random stuff.
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