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?
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
I have always wondered if the neural engine could be used for training - pretty excited for part 3 of this to see if the juice is actually worth the squeeze
> Throughout this series, “we” refers to maderix (human) and Claude Opus 4.6 (by Anthropic) working as a pair. The reverse engineering, benchmarking, and training code were developed collaboratively
Sure, "collaboratively." Why would I ever trust a vibe coded analysis? How do I, a non expert in this niche, know that Opus isn't pulling a fast one on both of us? LLMs write convincing bullshit that even fools experts. Have you manually verified each fact in this piece? I doubt it. Thanks for the disclaimer, it saved me from having to read it.
Benchmarks all in part 2. Training progress in part 3(upcoming)
Also I think AI human collaboration is important for goal management.
Sure LLMs bullshit all the time, but that's the role of the human to create good goals and gating criteria to what constitutes as good.
The recent news is that Apple is supposedly replacing the Core ML framework with an updated version that will make it easier to integrate third party LLMs into your apps.
> the company is also planning a few other software-based AI upgrades, including a new framework called Core AI. The idea is to replace the long-existing Core ML with something a bit more modern.
I scoffed, thinking "more modern? It's pretty recent, right?" and then I realized it's coming up on 10 years old and in AI years that's like 70 years, isn't it.
I wonder to what extent this is a branding exercise; the framework that will replace Core ML could have just as easily been called "Core ML", except the current hotness is "AI" and not "ML".
* 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).
Reverse Engineering with AI is only going to get better. I have seen some crazy things friends of mine have done with Claude alone. Let's just says SaaS isn't the only industry that could one day suffer.
I remember the good old days when Apple was desperate for developers and produced great documentation and there were a lot of great 3rd-party books too. You can't just give out awards in hopes that someone will make that great app.
For me, what AI brings is augmented humans. Just as we don't calculate on paper anymore, what is the reason of doing things by hand when a machine in X times better.
Want to code by hand, as artisans of old? Suit yourself.
Much of this information we already knew the very basics of from documentation of the M1/M2 ANE as accessed via bare-metal from Asahi Linux, but it's nice to see confirmation and it being explored in further depth. Note that according to OP Parts 1/2 for very large matmuls CoreML adds little to no overhead compared to the lower-level interface, so there seems to be plenty of scope for supporting ANE for prefill in local AI frameworks. Decode is generally memory-bandwidth limited unless context is very large, and the ANE requires special handling (converting from matmul to 1x1 convolution as described here is wasteful of memory bandwidth, as is potentially dequantizing to INT8/FP16 in memory) so it's less of a clear win.
Can someone help me understand when these neural engines kick in in open source software?
I typically use python ML libraries like lightgbm, sklearn, xgboost etc.
I also use numpy for large correlation matrices, covariance etc.
Are these operations accelerated? Is there a simple way to benchmark?
I see a lot of benchmarks on what look like C functions, but today in my jobs I rely on higher level libraries. I don't know if they perform any better on apple HW, and unless they have a flag like use_ane I'm inclined to think they do better.
Of course chatgpt suggested I benchmark an Intel Mac vs. newer apple silicon. Thanks chatgpt, there's a reason people still hate AI.
Is it really worth having separate GPU and NE? Seems redundant and weird compared to what Nvidia is doing, i.e. "GPUs are good NEs", or is that not really true?
I went digging down the rabbit hole over the last 6 hours on what compute around training can be extracted from M4/M5 Neural Engine chips:
- was able to offload
@karpathy's NanoGpt training run(partially) on Apple Neural Engine.
- moved the Classifier & Softmax layers directly onto the ANE - Classifier is 10x faster, and Softmax is 34x faster
- fixed memory exhaustion: original repo had an ARC memory leak that capped training at ~119 compile loads per process.
- patched the C-bridge, allowing continuous, stable training
Why does apple want to make this hardware hard to access?
What actual benefits do they get?
I guess they can have their own models run faster than the competition on their hardware? But they don't even really have anything that consumers use on the ANE as far as I can tell and local LLMs are taking off on macs and could really benefit from this
Really impressive reverse engineering work. I’m curious how much of the Neural Engine’s instruction set is undocumented versus inferred experimentally. Also wondering how Apple balances power efficiency vs peak throughput in the M4 compared to previous generations.
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[ 0.28 ms ] story [ 63.7 ms ] thread- 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?
The big takeaway isn't reverse engineering the ANE per se, but what Manjeet could do with his software engineering skills when accelerated by AI.
This is a good example of the present state of software engineering. Not future state - present state.
6.6 FLOPS/W, plus the ability to completely turn off when not in use, so 0W at idle.
> 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
thanks
Efficiency is the question.
Sure, "collaboratively." Why would I ever trust a vibe coded analysis? How do I, a non expert in this niche, know that Opus isn't pulling a fast one on both of us? LLMs write convincing bullshit that even fools experts. Have you manually verified each fact in this piece? I doubt it. Thanks for the disclaimer, it saved me from having to read it.
I don’t understand the mindset, I really don’t. Why are humans held to such a lower standard?
I just wanted to say that you’ve done an excellent job and am looking forward to the 3rd installment.
Why did you guys remove the ability to detach the console and move it to another window?
“Difficult” because of lack of documentation? Or difficult because of purposefully obfuscating things?
> the company is also planning a few other software-based AI upgrades, including a new framework called Core AI. The idea is to replace the long-existing Core ML with something a bit more modern.
https://www.bloomberg.com/news/newsletters/2026-03-01/apple-...
I wonder to what extent this is a branding exercise; the framework that will replace Core ML could have just as easily been called "Core ML", except the current hotness is "AI" and not "ML".
* 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).
This, a thousand times this.
For me, what AI brings is augmented humans. Just as we don't calculate on paper anymore, what is the reason of doing things by hand when a machine in X times better.
Want to code by hand, as artisans of old? Suit yourself.
I, for one, love the smell of burning chrome.
I typically use python ML libraries like lightgbm, sklearn, xgboost etc.
I also use numpy for large correlation matrices, covariance etc.
Are these operations accelerated? Is there a simple way to benchmark?
I see a lot of benchmarks on what look like C functions, but today in my jobs I rely on higher level libraries. I don't know if they perform any better on apple HW, and unless they have a flag like use_ane I'm inclined to think they do better.
Of course chatgpt suggested I benchmark an Intel Mac vs. newer apple silicon. Thanks chatgpt, there's a reason people still hate AI.
Repo - https://github.com/vipuldivyanshu92/ANEgpt
What actual benefits do they get?
I guess they can have their own models run faster than the competition on their hardware? But they don't even really have anything that consumers use on the ANE as far as I can tell and local LLMs are taking off on macs and could really benefit from this