I'm happy supporting iOS, macOS and web.
2,774 karma · joined January 20, 2009
I'm happy supporting iOS, macOS and web.
"When you’re a carpenter making a beautiful chest of drawers, you’re not going to use a piece of plywood on the back, even though it faces the wall and nobody will ever see it. You’ll know it’s there, so you’re going to use a beautiful piece of wood on the back. For you to sleep well at night, the aesthetic, the quality, has to be carried all the way through."
This is classic Steve Jobs. Sheer force of will.
https://en.wikipedia.org/wiki/Normalized_compression_distanc...
https://docs.swift.org/swift-book/documentation/the-swift-pr...
Vibe-coded website.
Both bundles contain quantized versions of the CohereLabs/cohere-transcribe-03-2026 model. The model in JEXXA.dmg is MLX int8 g64 affine quantized. JEXXA-Small.dmg has the same model but MLX int4 g32 affine quantized. There's also an int8 quantized WeSpeaker ECAPA-TDNN speaker-verification ONNX encoder. No acknowledgments for both models (doesn't the Apache 2.0 license require it? WeSpeaker's cc-by-4.0 license certainly does). Both bundles also contain full-blown Python 3.12.8 runtimes with about a dozen packages installed.
Apps are unsandboxed menubar apps and also contain PostHog analytics and Supabase auth. So, I wouldn't run it on any of my machines.
A few engineering hygiene issues like a .pytest_cache directory, Python code, .DS_Store files, etc.
Given the above, I suspect the "I am training better models." is just marketing speak.
nb: The demo link without LinkedIn tracking slop: https://www.linkedin.com/posts/sankyde_jexxa-demo-httpsjexxa...
I suppose the next logical thing to do would be to train a language model to generate random flags, call it Artificial Flag Intelligence (AFI) and raise a $10m pre-seed at $100m post. :P
> Does the M4 and later ANE expose any additional capabilities, or is it just a higher-performance iteration of the same thing?
IIUC, M4 introduced a fast path for INT8 weights and activations (w8a8). M5 Ultra, M6 and A20 have two ANEs.
> As an aside, the introduction to this article seems to conflate the ANE with the Neural Accelerators (NAX)
Yeah, that part is true. NAX cores are matmult accelerators, closer to tensor cores in NVIDIA GPUs.
[1]: https://magic.dev/blog/100m-token-context-windows (also linked to in their blogpost)
One minor wrinkle in Alpöge's narrative, he refuses to deny that he hasn't used any non-public Anthropic models for his "independent" research.
Who will watch the watchmen?