That, or I can’t read.

There are emails unearthed in various lawsuits where you can read Bill Gates screaming at his subordinates: "why the hell can't our partners like Sony and Creative create a similar device? Give them all, give them early access to everything, work with them". In the end MS felt compelled to make their own.

When plugged in... This caveat is so enormous it should almost be legislated. If your computer use is at all portable, a computer that scales down to 20 - 40% of GPU power when unplugged is an enormously significant factor. So far as I'm aware (could be wrong about arm devices?) there's no non-apple laptop that operates at 100% speed on the road.

The real question is whether all these Intel Windows laptops spin their fans at full speed when doing absolutely nothing. That’s something I can never go back to. I do some light gaming on my M2 Pro MBP and it gets hot when trying to push 120 fps. But my Lenovo Legion (that’s now collecting the dust) is so loud I could hear it through headphones.

Hold up, in what metric/benchmark? Feel personally like we life in an age where, no matter the SOC vendor, something high performant and efficient is offered, so seeing a claim that any vendor, be it Intel, AMD, Qualcomm or Apple, is consistently outperforming another, I'd like to get more context on that.

Intel had to reduce the frequency of their processors when running AVX2 instructions and the AVX2 frequency of the processors were non-disclosable to anyone.

Also, benchmarking Intel processors and publishing these numbers were forbidden in some cases. I don't know whether this ban is still in effect.

x86 processors can't keep up with the ARM processors TDP and thermal profile wise. So they slow down a ton when running on battery. See Jeff Geerling's last video on Apple Neo vs. some Intel laptop. It's as "efficient", but slow as a newborn tortoise learning to walk when unplugged and trying to get the most endurance out of the battery.

My M1 Mac gets almost 2 days of low-intensity use after ~6 years of use, and it got warm once or twice because something ran away in the background for tens of minutes.

5.25GHz Pentium 4s, intentionally lower binned Athlons, the era your CPU got obsoleted the moment you booted it for the first time.

I don't remember early 90s much. I was too young back then. I don't remember much stuff from that era. But late 90s, early 2000s.

Oh, boy.

P.S.: AMD64 was a great sucker punch though. One of the professors in our university rejected to believe and got mad when he learnt that Intel licensed AMD64 from AMD, heh.

Now we boot an embedded microcontroller (or CPU) which boots the main CPU which boots another OS semi-persistently to boot the main OS (if it's allowed).

Sometimes there are other processors needs to be up to allow processor to continue booting as well (these are mostly servers, but eh).

I enjoy it because of progress, not because of Apple.

@TRACK: Zylokloto, cyanydeez, deaux

Basically everyone that makes memory is building new fabs, meanwhile I'm not sure how much longer AI datacenter demand for ram will last. I think the decrease in AI ram demand and the new fabs will likely coincide leading to a collapse in pricing.

That is, of course, assuming the memory manufacturers don't pull their favorite trick and collude.

...

> I don’t think anyone truly knows when, but it will happen.

Do you know what cyclical means ... ?

Could you provide more details about the Epyc + DDR4 server?

E.g how is the perf/$ vs Wildcat lake

https://en.wikipedia.org/wiki/ELIZA_effect

It turns out the limiting factor isn't how sophisticated algorithms are, it's how gullible humans are.

https://commission.europa.eu/news-and-media/news/safer-and-m...

https://www.psychologytoday.com/ca/blog/the-digital-self/202...

And the "popularized" version is faulty also since it uses an ideal, abstract human judge (like the "spheroidal economic agent").

But if you want to add declinations to the said popularized image of the Turing test, you may add Maxim Lott's IQ tests at trackingai.org . Between the end of 2024 and the beginning of 2025 LLMs reached an equivalent IQ of 100, for example.

I think there are elements showing lowering of performance and expectation.

ELIZA beat the Turing test and then everyone forgot about it. Humans are just really terrible at recognising robots.

https://en.wikipedia.org/wiki/Turing_test

I've tried [1] and I almost 100% detect which is the AI. I really want to convince myself I have failed, does anyone know of a better site/resource for this?

I know it might be moving goalposts but I would consider AI to have passed in a well and truly undisputed manner when [2] is resolved.

But in a more practical sense, if AI can impersonate humans so well today then why are state of the art frontier models so obviously AI when they create PRs, commit messages, documentation, etc. Are the companies deliberately making them unnatural?

[1] https://turingtest.live/

[2] https://www.metaculus.com/questions/11861/date-when-ai-passe...

- Extreme poverty has dropped from 30% to under 10% globally. - Child mortality rates have dropped in half - Internet access has exploded from 10% to 70% - Solar energy costs have dropped 90% - Cancer death rates have declined by 30%

All of these massive improvements in less than 30 years.

While there certainly are issues to solve, and if you simply follow journalism you may think the world is worse off, but for many, their lives have been significantly improved.

That's one big plus.

These times are exciting and rough seas make good sailors. Find your path forward.

They’re very different things.

The more logical argument to me is that Apple uses its upgrade price points as more than just direct BOM and rather as a proxy for things that are amortized across all their sales like support/warranty/etc so higher SKUs subsidize the costs of the lower ones.

For one thing, you can’t tell from a movie what the specs are. A $999 Mac Studio looks exactly the same as a $20,000 one.

For another, Apple updates the industrial design on their products so rarely, a 6-year-old Mac, iMac or MacBook also looks nearly indistinguishable from a brand-new one.

It’s a bit depressing because it means that if I ever feel forced to switch my daily driver, it won’t come without a dump truck load of friction, frustration, and lost productivity, which I’ve validated by using the various Linux desktops on secondary machines.

For example when using PyTorch I wanted to try to speed up my NN kernel by 2x by just using half precision and haven't noticed any speedup at all. Also I was missing the easy to use GNU tools that had to be mixed with Apple's tools.

I loved using Arc browser as well, and I'm missing it, but I guess I will do without it somehow (Chrome's vertical tabs are just not the same).

My main program missing from going back to Linux was ChatGPT Desktop, but now it's there.

I just checked out Hammerspoon, I'm happy for you that you wrote it, and looks great, but it has the same problem that I had: for security reasons Apple stopped allowing the window APIs to get all important information on other workspaces. You can only do it with Accessibility API. I was trying to fight with it but have up.

I ordered an ASUS Zephyrus G16 with 5090 NVIDIA card + 1.9kg (quite an overkill, and I know that I will have to limit power output), but hasn't arrived yet.

But what's fun is that I love QML+QuickShell with its hot reloading, Hyprland with its Lua support.

With AI nowdays it's just so easy to do deep UI changes that wasn't possible a year ago.

I edit 4K ProRes and H.265 footage, sometimes with multicam (up to 4 streams) and color adjustments, titles, etc. It's only after stacking 3-5 effects before things can stutter, really.

Or if you try doing something CPU-intense in the background _while_ running some heavy creative software. I just don't do that.

The prices are ridiculous though. I may just keep rolling with my Windows 10 setup.

This may depend on the size of your library. I tried installing Jellyfin on a Synology NAS, which runs Plex just fine, and it ran so poorly it was basically unusable. It “worked”, but it was painful.

10 years ago 32GB ram laptops sounded too much. 8 was enough. These days even I would get that much ram since it’s soldered. 64GB is higher end.

In a few years we should see such high end hardware commonplace. Working with a local LLM to get work done is the ideal way to go which has mostly hardware limitation as of now that gets solved in due time.

The iPhone 15 was almost entirely marketed based upon AI (I would say fraudulently so, advertising features they still haven't delivered), and a huge portion of the OS work was on local AI or AI integration.

And for that matter Apple has been dumping enormous sums into their own AI development. Their failure to have a lot to show for it doesn't void the fact that they tried really, really hard.

It's bizarre how often this "Apple sat on the sidelines and let the AI people fight...so smart!" narrative appears on HN. Apple hasn't gone down the path of spending hundreds of billions on nvidia GPU data centres, but they absolutely tried really hard to matter in AI.

Yep, Siri AI; they’re doing it in public.

It would take Apple one or two engineers to make Linux life much easier on macs. But Linux is outside their walled garden so it's ignored.

My old 3090 is typically significantly faster (almost 2x token/s) than my M4 Max 128GB machine, as long as the model fits in the 24GB of VRAM.

In most situations it's a better idea to just buy tokens. But there are definitely cases when that's not an option. And then a machine like the M5 Ultra can allow you to do things locally for a fairly limited budget. And in a simpler package to manage than a machine with multiple GPUs.

Linux runs very well in a VM on macOS. There are many good options for this, some free and open source (QEMU, UTM, Lima, Colima), some proprietary (VMware Fusion, Parallels).

But Linux in a VM doesn't get access to the real GPU, so model performance is limited. Those running on the CPU perform well, and those needing the GPU don't.

However, macOS on M-series macs is excellent for local models. (Maybe not as excellent as a box full of the best nVidia GPUs, but still excellent).

So if you're getting Apple hardware, like Linux, and want to run all of it locally, a fine setup for a machine to run local models, with agentic characteristics:

- macOS running one of the many local model runners. I used to use Ollama and Whisper, and now use llama.cpp instead of Ollama. Others use LM Studio, oMLX, etc. Provide HTTP endpoints to access the models.

- Linux in a VM for overall control and orchestration, with standard VM settings, and bridged networking so it appears as its own machine on your network. Also, in here provide a robust shared file server for shared state. Use this VM as your desktop and primary access to the machine, if you like Linux.

- Linux in a VM to launch ephemeral, volatile containers, with the containers using a memory-only tmpfs overlay on top of a read-only Linux filesystem in a VM disk image, with tools in this filesystem. Alternatively, a writable Linux filesystem in a VM disk image, with disk buffering set to use macOS host buffering and discard fsync requests. These settings optimise for container disk performance for data that's only ephemeral which will be deleted soon or on system shutdown. (You can combined both VMs, but need to use two VM disks to get equivalent behaviour, and be careful about VM disk configuration of the two disks.)

- Containers spawned within that second Linux VM can be spawned very quickly and run quickly, so are ideal for LLM agents that need a quick sandbox. These sandboxes generally run faster than a macOS sandbox, despite being on the same machine with VM overhead, because Linux is faster at some things. Teach the LLMs to store files and memories they want to keep in the shared file server.

I just want my butt ugly repairable beast machine to do the same trick. Why is my ram not unified? I have an iGPU in my server, but it can't access the 64 GB ram (I got last year for 150 euro) directly or something? It's on the CPU right? Why did only Apple go for this architecture? So many questions...

"M6 also introduces a Dual 16-core Neural Engine, providing up to 2x the peak compute over previous generations to make on-device AI workflows run even faster" .

It's the Hardware, Software and Services in combination. None would work without the other (to reach the scale apple is)

More specifically, they're a hardware dongle company first

FWIW, Ollama, LM Studio and Lemonade (and oMLX) also wrap Apple's MLX framework.