True but one benefit of those guys is that they actually define what they mean in a formal way. "Programmers" generally don't. There is in fact some benefit in having consistent names for things, or if not at least a culture in which concepts have unambiguous definitions which are mandated.

You can engineer a waveguide if you understand the nonlinear theory they propose. There's no heat exchange involved, which is easy to get confused on because the writing in the article does not really understand "optical thermodynamics".

>if the routing is dynamically changeable

At this point probably not, it requires a finely engineered waveguide which has a well-defined "ground state"

>it works in reverse, eg light coming in can be routed to one of several output ports

In theory it works in reverse, as everything in this system is time-reversible (i.e., the "optical thermodynamics" is just an analogy and not real thermodynamics, which would break time reversibility). This is demonstrated via a simulation in the SI, but experimentally they did not achieve this (it may be difficult, I am not an experimentalist so cannot comment).

Apply directly... to what? IMO it is weird to learn theory (like linear algebra) expressly for practical reasons: surely one could just pick up a book on those practical applications and learn the theory along the way? And if in this process, you end up really needing the theory then certainly there is no substitute for learning the theory no matter how dense it is.

For example, linear algebra is very important to learning quantum mechanics. But if someone wanted to learn linear algebra for this reason they should read quantum mechanics textbooks, not linear algebra textbooks.

Having said that, let me raise some objections:

1. Omitting the multi-layer perceptron is a major oversight. We have backpropagation here, but not forward propagation, so to speak.

2. Omitting kernel machines is a moderate oversight. I know they're not "hot" anymore but they are very mathematically important to the field.

3. The equation for forward diffusion is really boring... it's not that important that you can take structured data and add noise incrementally until it's all noise. What's important is that in some sense you can (conditionally) reverse it. In other words, you should put the reverse diffusion equation which of course is considerably more sophisticated.

An interesting perspective is Terry Tao's on local vs. global errors (https://terrytao.wordpress.com/advice-on-writing-papers/on-l...). A typo like this, even if propagated, is a local error which at worst makes it very annoying to Ctrl-F papers or do literature review. Local errors deserve to be corrected, but in practice their importance to science as a field is small.

Ultimately Ned is in the right about empathy and communication online. But as an educator it would have been nice to hear, even briefly, why he thought Pyon's point was unnecessarily technical and pedantic? Instead he just says "I've worked for decades and didn't know it". No one is too experienced to learn.

EDIT: I just skimmed the original comment section between Pyon and Ned and it seems that Ned is rather diplomatic and intellectually engages with Pyon's critique. Why is this level of analysis completely missing from the follow-up blogpost? I admit to not grasping the technical details or importance, personally, it would be nice to hear a summarized analysis...

Not really, if you ever listen to CS undergrads or people in non-traditional schooling (bootcamps, etc.) talk about software engineering this opinion is essentially ubiquitous. People interested in ML are less likely to hold this exact opinion, but they will hold qualitatively identical ones ("do you really need multivariable calculus/linear algebra to do ML?"). It is precisely because people (primarily Americans) are scared to learn mathematics that they rationalize away this fear by saying the necessary mathematics must not be essential, and indeed it is true that many people get away without knowing it.

The practical question is whether you think it's ok to continue propagating a rather crude and misunderstanding-prone idea about Big O. My stance is that we shouldn't: engineers are not business people or clients, they should understand what's happening not rely on misleading cartoon pictures of what's happening. I do not think you need a full-year collegiate course in calculus to get this understanding, but certainly you cannot get it if you fully obscure the calculus behind the idea (like this and uncountable numbers of blogpost explainers do).

This is like saying in the 70s that we know only the US is capable of sending a man to the moon. Just because the reasoning developed in a particular context means very little about what the bare minimum requirements for that reasoning are.

Overall I am not a fan of this blogpost. It's telling how long the author gets hung up on a paper making "broad philosophical claims about reasoning", based on what reads to me as fairly typical scientific writing style. It's also telling how highly cherry-picked the quotes they criticize from the paper are. Here is some fuller context:

>An expanding body of analyses reveals that LLMs tend to rely on surface-level semantics and cluesrather than logical procedures (Chen et al., 2025b; Kambhampati, 2024; Lanham et al., 2023; Stechly et al., 2024). LLMs construct superficial chains of logic based on learned token associations, often failing on tasks that deviate from commonsense heuristics or familiar templates (Tang et al., 2023). In the reasoning process, performance degrades sharply when irrelevant clauses are introduced, which indicates that models cannot grasp the underlying logic (Mirzadeh et al., 2024)

>Minor and semantically irrelevant perturbations such as distractor phrases or altered symbolic forms can cause significant performance drops in state-of-the-art models (Mirzadeh et al., 2024; Tang et al., 2023). Models often incorporate such irrelevant details into their reasoning, revealing a lack of sensitivity to salient information. Other studies show that models prioritize the surface form of reasoning over logical soundness; in some cases, longer but flawed reasoning paths yield better final answers than shorter, correct ones (Bentham et al., 2024). Similarly, performance does not scale with problem complexity as expected—models may overthink easy problems and give up on harder ones (Shojaee et al., 2025). Another critical concern is the faithfulness of the reasoning process. Intervention-based studies reveal that final answers often remain unchanged even when intermediate steps are falsified or omitted (Lanham et al., 2023), a phenomenon dubbed the illusion of transparency (Bentham et al., 2024; Chen et al., 2025b).

You don't need to be a philosopher to realize that these problems seem quite distinct from the problems with human reasoning. For example, "final answers remain unchanged even when intermediate steps are falsified or omitted"... can humans do this?