Scene_Cast2

One issue I've had with swapping fans for Noctuas is the airflow matching. The louder fans aren't louder just because they're lower quality (which they definitely are), but also because (almost always in my experience) they force more air through.

That unit from the article sounds very temperature sensitive. I'd be wary of throwing a precision unit like that out of spec and out of calibration.

FWIW I've noctua-swapped so many things including an oscilloscope.

Really interesting.

On the other hand, this only applies to a limited set of workloads (training is often memory bandwidth bound for example).

I also wonder how this affects power consumption.

I found that surprisingly many k-pop songs do microtonal shenanigans. Nowhere near most, but it's there. Even something as popular as Boombayah (check the 4th note, it's not an on-tune semitone).

There's also more niche stuff like Flying Microtonal Banana.

A quality ECE program will have you working lower level still - all the way from ALUs down to the electrons & holes in semiconductors.

That's what I had lying around when I started the build. PTM is now my go-to recommendation.

This video seems to be about LM on untreated copper (which I thought wasn't really advised).

Now, I personally have had two builds with LM. One with LM as the material for a CPU re-lid, and another for a direct-die application. Neither touched aluminum or untreated copper.

The re-lidded CPU is still going strong, no issues. But with the direct die application, I found the performance to significantly degrade after two years. Apparently there's a pump-out effect.

I ended up switching to PTM. Same temps as LM, but zero degradation so far.

Not all of the builds seem live. Pixel 9 Pro has 24.0, but Pixel 9 has 23.2, for example.

I ended up going with a Synthetic subscription (they host some open-weight models including K3 and GLM 5.3). They're the only subscription that I found that's simultaneously cheaper than token rates, and is extremely transparent about usage and pricing.

The libraries, yes, but the code you write is portable (at least until you get into squeezing the last few percent and switch to Triton / Helion in case of GPU, and even those are decently portable).

There's also Halide, where you write the algo but the framework gets you the scheduling and SIMD.

What about numpy, numba, and torch.compile?

The official page still downloads 1.23.2. I wonder when this will go live, and if the old app will auto-update.

How? I don't see it. (I'm familiar with the ML side, not the combinatorics side.)

How many tokens are you getting, roughly?

I found LLMs circa 4 months ago to be decent at textbook-style electronics, but they choke at anything nontrivial. They approach things like someone that knows the undergrad and grad textbooks, but has absolutely zero experience. No ability to answer questions such as "is this adequate", "do I need a LPF here", "how do I kill resonance without increasing impedance", etc.

I'm guessing that this is due to a lack of RL and data.

Why does it work for Red Bull?