• Gaming is indeed a big weak point for M series chips. Some games run, but it’s a pretty bad experience overall. Personally I’m insulated from this because I have a gaming PC I can use.
• I’ve not heard this at all recently. This sounds like it was probably true in late 2020 but afaik creative workflows are quite flushed out at this point.
• No idea what you’re talking about here. Tools like PyTorch fully support metal for training and inference. llama.cpp fully supports Apple silicon. Apple’s shared memory model gives their GPUs access to huge pools of memory compared to even high end discreet GPUs, and this allows working with models otherwise not possible in a laptop form factor. No other laptop GPU is getting shipped with up to 96GB of memory.
• Really? This seems like a generally easy thing to fix, what projects (that ran on x86 macOS) are known to be bothersome?
• This is a barrier to the Asahi Linux folks, and I hope someday the situation improves for them. Otherwise, it’s irrelevant since nearly everything for macOS is gonna be built through Xcode. It’s the system toolchain.

I don’t know exactly what software you use for work, but for simple cases docker desktop uses binfmt-misc to enable Rosetta and qemu-user for containers. This actually makes it really easy to build and test for a bunch of different architectures, x86 but also ppc64le, mips, etc. With x86-64 specifically you get Rosetta for very high performance. I know tools like gdb don’t work right in this environment, but thats not usually part of a typical ci/cd system anyways.

Lol! No.

Gaming: has just jumped up in a huge way in the last couple of months. The software that’s come out recently is amazing. Just run full windows games on your macbook pro? Sure.

Creators: huh? Never heard of this before. Everything seems to run amazingly well.

GPU: except for the local ai stuff that is being done all the time? What? Diffusion Bee doesn’t exist now?

Cheaper: well, yeah. Heh. You get that one, for sure. But if money is no object, then… 🤷