Would you mind explaining more.

“Difficult” because of lack of documentation? Or difficult because of purposefully obfuscating things?

>I worked on the Xcode team for years

Why did you guys remove the ability to detach the console and move it to another window?

But not 38 TOPS that Apple claims, with the weak explanation of

> Apple’s “38 TOPS INT8” is computed as 19 TFLOPS FP16 × 2, following the industry convention of counting INT8 operations as 2× the FP16 rate. But the hardware doesn’t actually execute INT8 operations twice as fast.

Why would Apple follow that convention when the hardware explicitly doesn't seems like a more straight-faced lie that I expect from Apple

You're off by a factor of a trillion. It's 6.6 TFLOPS/W.

Humans also write endless amounts of convincing bullshit, and have done since time immemorial. False papers and faked results have been a growing scourge in academia before LLMs were a thing, and that's just counting the intentional fraud - the reproducibility crisis in science, especially medical and psychological science, affects even the best designed and well intentioned of studies.

Humans also make mistakes and assumptions while reverse engineering, so it will always need more engineers to go through the results, test things

Claude likes to hide bad benchmarks from you, so it will show you where you are clearly winning. You even see some weird benchmarks in the article.

> when these neural engines kick in in open source software?

It mostly doesn't because NPUs are bespoke and vendor-specific (which incents neglect by software devs working on open source numerics and ML/AI infrastructure), and the Apple ANE is no exception. Part of this effort is most likely about fixing that for the specific case of the Apple ANE.

This doesn’t seem that weird to me?

* They haven’t said the source isn’t available to them, just that the closed nature of the ANE means they can’t use it in OSS.

* They’ve repeated constantly that it can’t do backprop and isn’t useful for most MLX use cases.

And really, ANE isn’t even that interesting for MLX really; it’s a limited resource power efficient inference engine for smallish edge models. If you want to use it you can use the Apple APIs, which while limited are generally “shaped” like what you’d want to do anyway. Almost every “biggish” CPU has one of these now and Apple don’t want to give away the specifics of theirs (even though it’s been pretty thoroughly RE’d by real REs and re-summarized by Claude, like this article).

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No, GPUs are not what you'd design for neural networks from first principles. They were adopted for that because they offered far more parallelism than general purpose cpus, not because they're ideal. That's why Google et all designed TPUs that have a very different internal structure.

Most TPU designs have been based around systolic arrays, which for matrix ops have a quadratic speedup. A typical design is a 128x128 array of MAC units. You shift weights along one dimension, parameters along the other. It takes 128 cycles to shift a full matrix input in, then 128 cycles to shift the answer back out, but during those 256 cycles you got 16,384 MAC operations done, for a factor of 64 speedup.

The other big appeal of this design is it's way simpler than GPUs. The memory access patterns are predictable, there's no threads or thread divergence, etc. So it can be way more efficient in silicon, not just in area but especially in power efficiency.

There's other ideas for architectures besides this basic systolic array idea. If you want to learn about them, a good place would be the HotChips presentations of the last few years: https://hc2025.hotchips.org and similar domain names for prior years.

What’s the intent of pointing out the presumed provenance in writing, now that LLMs are ubiquitous?

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?

We've got about a year before so many people are interacting with LLMs on a daily basis that its style starts to reverse infect human speech and writing

Also the Prior Art section, which has telltale repetition of useless verbs like "documenting," "providing insight into," and "confirming" on each line. This was definitely AI-written, at least in part.

Yeah, the Inside Macintosh guides were epic.

In principle most if not all inference hardware should be usable for training.

Efficiency is the question.

If "AI" were doing anything more than repeating content from the web without attribution, I might agree with you.

They are also used by ML models that are deeply integrated in macos and ios without you knowing. Like object and text detection in images.

If you strip away the branding, Apple has and continues to ship a ton of algorithms that likely use the ANE and end users can use CoreML to do the same.

Just some things that people will likely take for granted that IIRC Apple have said use the ANE or at least would likely benefit from it: object recognition, subject extraction from images and video, content analysis, ARKit, spam detection, audio transcription.

Apple's OSes run a lot of local ML models for many tasks that aren't branded as Apple Intelligence, and they have done so for many years now.

https://dennisforbes.ca/blog/microblog/2026/02/apple-neural-...

You can convert your own ML models to MLX to use them; Apple Intelligence is not the only application.