Espresso + your ANE training work = complete ANE ecosystem?

Hi @maderix,

I've been following your Substack series "Inside the M4 Apple Neural Engine" and your work on ANE training (maderix/ANE). The efficiency numbers and your findings on the convolution vs. matmul throughput differences are impressive.

I'm working on the inference side of the same problem space with **Espresso** (https://github.com/christopherkarani/Espresso), a pure-Swift framework that achieves 519 tok/s on M3 Max for transformer inference using the same private MIL APIs (3.41x over CoreML).

Our experiences complement each other naturally:
- You've mapped ANE training primitives and the full software stack to the IOKit layer
- We've mapped ANE inference kernel fusion patterns (fused RWKV-style decode, lane-packed attention, triplet-layer fusion)

There's a natural joint story here: a combined piece on "The complete ANE developer toolkit — training and inference." The Hacker News thread on your Part 1 shows the community is hungry for this kind of deep work.

Would you be interested in:
1. A joint blog post comparing our benchmark methodologies and results?
2. Cross-referencing findings (your 19 TFLOPS FP16, our 519 tok/s decode path)?
3. Exploring whether Espresso could serve as an inference runtime for models trained via your framework?

This is a genuinely novel space and combining our research could make a strong contribution to the community.

— Chris
https://github.com/christopherkarani/Espresso

The issue names the Espresso repository and the ANE training work but no files, tests, or implementation entry point. Start by reviewing the linked Espresso project and the referenced ANE findings. Done would require an agreed joint article, benchmark comparison, or runtime integration scope; none is defined here.

Written by the indexing model from the issue text.