I can speak, read, and write Taiwanese Mandarin (which is likely relatively underrepresented in the training sets and, which is, in my practical experience, materially different in its usage.)
The authoritative answer for this question would best come from the millions (or tens of millions) of Chinese-speakers who are currently using LLMs to write software.
However, it is my suspicion that you would see no advantages using any language other than English. While there is a certain token-level density to written texts, it seems the benefits of this (and the more recent discussion around “caveman talk”) are quite limited.
Furthermore, consider that the vast majority of textbooks, technical documentation, blog posts, StackOverflow answers, &c. are originally in English. Historically, where these have been translated to Chinese, the translations have often been of very poor quality (and the terminology and phraseology is often incomprehensible unless you also understand some English.) I would suspect that this makes up the overwhelming majority of the training sets for these models.
That said, my experience using the most recent models, is that they are surprisingly language-agnostic in a way that surpasses readily-available human capability. For example, I can prompt the LLM to translate English into something that uses German grammar, Chinese vocabulary, and Japanese characters, and I'll get an output that is worse than what a human expert could do… but where am I going to find a multilingual expert?
(Of course, I have so far only ever been impressed that a model could generate an output but never impressed with the output it did generate. Everything—translations, prose, code—seems universally sloppy and bland and muddy.)
So what I would anticipate the biggest benefit for a Chinese-speaker today… is that if they are disinterested in working internationally, they have significantly less dependency on learning English.
It's also very easy to complain about how employees have résumé-focus in their approach to their work: “why should I bother to learn some internal-only tooling that I'll never use anywhere else (for a task that I don't really even care that much about…)?”
But, to borrow a line from Warren VanderBurgh's ‘Children of the Magenta’: “(in the industry) we created you like this.”
Another key flaw of precomposed automations for rigidly-defined work-flows is that they usually exist in precisely the circumstances that give rise to their own subversion. (I might even go so far as to suggest that the circumstances are the cause of both the mistake and the maladaptive behaviours that address the mistake…)
Ultimately, deep stacks of tightly-integrated components forming a precomposed automation that enacts some work-flow—“vertical integration” as the post frames it—is obvious enough that it seems every big company tries it… only to fail in basically the same ways every time.
This kind of rigid, singular view of operational workflows based on precomposed automations not only constantly break but also inevitably introduce huge inefficiences.
I have first-hand experience across five distinct AMD 7840U and AMD 8840U devices that near-perfect, out-of-the-box Linux-support (with stock kernels and no dodgy kernel flags!) is possible. This includes support for S0ix suspend.
I don't doubt it when people recount their bad experiences with AMD devices; however, my experience should serve as an existence proof that it's not a universal experience.
In the case of each device mentioned in the comment above, I followed a standard installation procedure from an Arch installer USB. I use only stock kernels: linux, linux-lts, and linux-zen. For almost all of the devices, the only kernel flags I pass are for enabling hibernate or handling FDE. (In one or two cases, the devices have portrait displays that have been installed for use in landscape-orientation. These need an `fbcon=rotate:…` kernel flag.)
In all but one case (the OneXPlayer X1 Ryzen) everything (except fingerprint readers) works flawlessly. In the case of the OneXPlayer X1 Ryzen, there is an intermittent issue with hang on suspend, but that may have gone away with a recent kernel update. If not, I'll probably come back to this blog post and see what I can do…
It can be really hit-or-miss, and it can be really hard to debug errors like in the post.
A lot of workarounds that are suggested for various issues are also not really viable. Some of the workarounds involve turning off different power-saving modes; however, the point of enabling sleep is often to increase the amount of usable time between charges, and turning off these power-saving modes can often dramatically shorten battery life.
But getting sleep to work (even S0ix!) is not impossible.
I have a bunch of handheld AMD 7840U and AMD 8840U devices that I have installed Arch Linux on: GPD Win Max 2, GPD Win Mini, GPD Win 4, Minisforum V3, OneXPlayer X1 Ryzen. These devices were not designed with Linux support in mind. I would be very surprised if the companies that made them ever tested them with Linux. Yet with just a small amount of work (generally fiddling with `/proc/acpi/wakeup` and `/sys/devices/*/*/*/power/wakeup` to disable sources of spurious wakeups,) I have gotten essentially flawless S0ix support (… on all but the newest OneXPlayer X1 Ryzen.)
(In general, out-of-the-box stock Linux kernel support on these devices is fantastic. Touchscreens work, pen input works, wifi and Bluetooth work well. The only gap I've seen is fingerprint reader support.)
I suspect that given how small these manufacturers are (and how small their production batches must be,) there's much less extreme-customization and tight-integration of components. This is visibly evident in the form-factors of these devices, which many millimeters thicker than they might otherwise be. (Of course, these devices are primarily advertised to a gaming audience who are eager to avoid the thermal-throttling that happens with ultra-thin devices like Surface Pro…) I partially suspect that the lack of extreme-customization, the lack of tight-integration, and the smaller production batches means that the manufacturers make much more conservative choices in components. Maybe this explains the exceptional Linux support?
The authoritative answer for this question would best come from the millions (or tens of millions) of Chinese-speakers who are currently using LLMs to write software.
However, it is my suspicion that you would see no advantages using any language other than English. While there is a certain token-level density to written texts, it seems the benefits of this (and the more recent discussion around “caveman talk”) are quite limited.
Furthermore, consider that the vast majority of textbooks, technical documentation, blog posts, StackOverflow answers, &c. are originally in English. Historically, where these have been translated to Chinese, the translations have often been of very poor quality (and the terminology and phraseology is often incomprehensible unless you also understand some English.) I would suspect that this makes up the overwhelming majority of the training sets for these models.
That said, my experience using the most recent models, is that they are surprisingly language-agnostic in a way that surpasses readily-available human capability. For example, I can prompt the LLM to translate English into something that uses German grammar, Chinese vocabulary, and Japanese characters, and I'll get an output that is worse than what a human expert could do… but where am I going to find a multilingual expert?
(Of course, I have so far only ever been impressed that a model could generate an output but never impressed with the output it did generate. Everything—translations, prose, code—seems universally sloppy and bland and muddy.)
So what I would anticipate the biggest benefit for a Chinese-speaker today… is that if they are disinterested in working internationally, they have significantly less dependency on learning English.