- The key insight - [CoreML] doesn't XXX. It YYY.

With that being said, this is a highly informative article that I enjoyed thoroughly! :)

The article links to their own Github repo: https://github.com/maderix/ANE

Is it like one of those “Morning” nods, where two people cross paths and acknowledge that it is in fact morning? Or is there an unstated preference being communicated?

Is there any real concern behind LLMs writing a piece, or is the concern that the human didn’t actually guide it? In other words, is the spirit of such comments really about LLM writing, or is it about human diligence?

That begs another question: does LLM writing expose anything about the diligence of the human, outside of when it’s plainly incorrect? If an LLM generates a boringly correct report - what does that tell us about the human behind that LLM?

People seem to be going around pointing out that people talk like parrots, when in reality it's parrots talk like people.

Did you develop your own whole language at any point to describe the entire world? No, you, me, and society mimic what is around us.

Humans have the advantage, at least at this point, of being a continuous learning device so we adapt and change with the language use around us.

It's not my subject, but it reads as a list of things. There's little exposition.

Here is why you are correct:

- I see what you did there.

- You are always right.

> hollance/neural-engine — Matthijs Hollemans’ comprehensive community documentation of ANE behavior, performance characteristics, and supported operations. The single best existing resource on ANE.

> mdaiter/ane — Early reverse engineering with working Python and Objective-C samples, documenting the ANECompiler framework and IOKit dispatch.

> eiln/ane — A reverse-engineered Linux driver for ANE (Asahi Linux project), providing insight into the kernel-level interface.

> apple/ml-ane-transformers — Apple’s own reference implementation of transformers optimized for ANE, confirming design patterns like channel-first layout and 1×1 conv preference.

"Here’s the fascinating part:", "And one delightful discovery: "

Personally I find the AI-isms take away from the voice of the author. What does the author find interesting? What was their motivation? It's all lost in a sea of hubris and platitudes.

There's almost certainly a positive side - technical people who aren't so good at communication can now write punchy deep-tech blogs. But what's lost is the unique human voice that is normally in every piece of writing. It's like every blog is rewritten by a committee of copywriters before it's published. Bleurgh.

- "- Name - {Noun with modifiers} {comma} {verb-ing with modifiers}."

- "- Name - {Noun with modifiers} {comma} {verb-ing with modifiers}."

The phrasing is the same, which I notice sometimes happens in my own notes, but it's most noticeable when an LLM is asked to summarize items. An LLM written job description (without major prompting) for a resume comes out the same way, in my experience. It's the simplest full-sentence grammar for describing what something is, and then what something does.

If we used the developer's descriptions (from the github repo) to populate the info, it would look like this:

- hollance/neural-engine - Everything we actually know about the Apple Neural Engine (ANE)

- mdaiter/ane - Reverse engineered the Apple Neural Engine, with working Python and Objective C samples

- apple/ml-ane-transformers - eiln/ane - Reverse engineered Linux driver for the Apple Neural Engine (ANE).

- apple/ml-ane-transformers - Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE)

IMO It may not be as information-packed as the LLM list, but it is more interesting to read. I can tell, or at least think I can tell, that different individuals wrote each description, and it's what they wanted me to know most about their project.

If I were making a list of software during research (that would eventually turn into a report), the particular details I write down in the moment would be different, depending on the solution I'm looking for or features it has or doesn't have, will add or won't add. I don't try to summarize "the Whole Project" in one clean bullet point, i (or my readers) can re-read the repo for that, or glean it from surrounding context (presuming enough surrounding context was written). But unless I made an effort later to normalize the list, the grammar, length, and subpoints would vary from the form-identifiable "LLM Concise Summary." It's more work for me to write to a standard, and even more work to consciously pick one.

EDIT: Upon re-reading the article, I noticed the "Prior Art" section is written in past-tense, as I would expect. But the list is in present tense. I feel like it jumps from "narrative" to "technical details list" back to "narrative". And the list is 70% of the section! I wouldn't mind reading a whole paragraph describing each project, what worked, what didn't, what they could use and what they couldn't, in the past tense, if it were interestingly-written. Something that tells me the author dove into the previous projects, experimented with them, or if they interacted with the developers. Or something interesting the author noticed while surveying the "prior art". but "interestingly-written" isn't really the LLMs goal, nor its ability. It's maximal information transfers with minimal word count. So the result is a list that smells like the author merely read the repo readme and wrote a summary for the masses in a technical report.

tl;dr The list is just "a list", and that makes it not interesting to read. If it was not interesting to read it was probably not interesting to write, which I take as an LLM writing it.

Doesn't run inside IDEone due to the external download link, but you can copy&paste the code over

If I were to describe this to a nontechnical person, I would say:

LLMs are big stacks of layers of "understanders" that each teach the next guy something.

Imagine you are making a large language model that has 4 layers. Each layer will talk to it's immediate neighbor.

The first layer will get the bare minimum, in the LLM's of today, that's groups of letters that are common to come up together, called "tokens". This layer will try to derive a bit of meaning to tell the next layer, such as grouping of letters into words.

The next layer may be a little bit more semantic, for example interpreting that the word "hot" immediately followed by the word "dog" maps to a phrase "hot dog".

The layer after that, becoming a bit more intelligent given it's predecessors have already had some chances at smaller interpretations may now try to group words into bigger blobs, such as "i want a hot dog" as one combined phrase rather than a set of separated concepts.

The final layer may do something even more intelligent afterward, like realize that this is a quote in a book.

The point is that each layer tries to add a little meaning for the next layer.

I want to stress this: the layers do not actually correspond to specific concepts the way I just expressed, the point is that each layer adds a bit more "semantic meaning" for the next layer.

He's got a big Twitter following so people assume somethings going on or important, but he just isn't.

Biggest thing he did in his career was feed Elon's Full Self Driving delusion for years and years and years.

Note, then, how long he lasted at OpenAI, and how much time he spends on code golf.

If you're angry to read this, please, take a minute and let me know the last time you saw something from him that didn't involve A) code golf B) coining phrases.

Most are, but not all.