Tootfinder

Just finished "Beasts Made of Night" by Tochi Onyebuchi...
Indirect CW for fantasy police state violence.
So I very much enjoyed Onyebuchi's "Riot Baby," and when I grabbed this at the library, I was certain it would be excellent. But having finished it, I'm not sure I like it that much overall?
The first maybe third is excellent, including the world-building, which is fascinating. I feel like Onyebuchi must have played "Shadow of the Colossus" at some point. Onyebuchi certainly does know how to make me care for his characters.
Some spoilers from here on out...
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I felt like it stumbles towards the middle, with Bo's reactions neither making sense in the immediate context, nor in retrospect by the end when we've learned more. Things are a bit floaty in the middle with an unclear picture of what exactly is going on politics-wise and what the motivations are. Here I think there were some nuances that didn't make it to the page, or perhaps I'm just a bit thick and not getting stuff I should be? More is of course revealed by the end, but I still wasn't satisfied with the explanations of things. For example, (spoilers) I don't feel I understand clearly what kind of power the army of aki was supposed to represent within the city? Perhaps necessary to wield the threat of offensive inisisia use? In that case, a single scene somewhere of Izu's faction deploying that tactic would have been helpful I think.
Then towards the end, for me things really started to jumble, with unclear motivations, revelations that didn't feel well-paced or -structured, and a finale where both the action & collapsing concerns felt stilted and disjointed. Particularly the mechanics/ethics of the most important death that set the finale in motion bothered me, and the unexplained mechanism by which that led to what came next? I can read a couple of possible interesting morals into the whole denouement, but didn't feel that any of them were sufficiently explored. Especially if we're supposed to see some personal failing in the protagonist's actions, I don't think it's made clear enough what that is, since I feel his reasons to reject each faction are pretty solid, and if we're meant to either pity or abjure his indecision, I don't think the message lands clearly enough.
There *is* a sequel, which honestly I wasn't sure of after the last page, and which I now very interested in. Beasts is Onyebuchi's debut, which maybe makes sense of me feeling that Riot Baby didn't have the same plotting issues. It also maybe means that Onyebuchi couldn't be sure a sequel would make it to publication in terms of setting up the ending.
Overall I really enjoyed at least 80% of this, but was expecting even better (especially politically) given Onyebuchi's other work, and I didn't feel like I found it.
#AmReading

There's `@starting-style` in #CSS https://developer.mozilla.org/en-US/docs/Web/CSS/@starting-style but no 'ending-style' 🫠
Wondering how it got spec-ed out in…

@starting-style - CSS | MDN
The @starting-style CSS at-rule is used to define starting values for properties set on an element that you want to transition from when the element receives its first style update, i.e., when an element is first displayed on a previously loaded page.

Yes! God still loves us!
There's a new Spinal Tap movie!
😎🤘🏼
https://www.theatlantic.com/magazine/archive/2025/10/spinal-ta…

A 'Spinal Tap' Sequel Riffs on the End of Rock
Forty years after Spinal Tap, history’s most hapless band turns it up to 11 one last time.

Another case of death by alternative medicine. Anyone who recommends the Gerson method should be in the dock, IMO.
https://

My sister died after refusing cancer treatment. Now I want to get justice for her – and hold my mum to account
Gabriel Shemirani blames his mother’s conspiracy theories for the death of his twin Paloma at age 23. As the inquest nears a verdict, he talks to Marianna Spring about his campaign against health misinformation

LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding