Just for reference, a few years back, (ex-Microsoft) David Plummer had this historical dive into the (MIPS) origin of the blue color, and how Windows is not blue anymore: https://youtu.be/KgqJJECQQH0?t=780

Likely due to being a prototype. Production laptops from Tuxedo tend to have the “TUX” penguin in a circle logo on the Super key by default. They also have been offering custom engraved keyboard (even with the entire keyboard engraved from scratch to the customer’s specifications) as added service, so probably there will be suppliers or production facility to change the Super key.

By the way, there was one YouTube channel that ended up ordering a laptop with Windings engraving from them: https://youtu.be/nidnvlt6lzw?t=186

How does this analogy work at all? LoRA is chosen by the modifier to be low ranked to accommodate some desktop/workstation memory constraint, not because the other weights are “very hard” to modify if you happens to have the necessary compute and I/O. The development in LoRA is also largely directed by storage reduction (hence not too many layers modified) and preservation of the generalizability (since training generalizable models is hard). The Kronecker product versions, in particular, has been first developed in the context of federated learning, and not for desktop/workstation fine-tuning (also LoRA is fully capable of modifying all weights, it is rather a technique to do it in a correlated fashion to reduce the size of the gradient update). And much development of LoRA happened in the context of otherwise fully open datasets (e.g. LAION), that are just not manageable in desktop/workstation settings.

This narrow perspective of “source” is taking away the actual usefulness of compute/training here. Datasets from e.g. LAION to Common Crawl have been available for some time, along with training code (sometimes independently reproduced) for the Imagen diffusion model or GPT. It is only when e.g. GPT-J came along that somebody invested into the compute (including how to scale it to their specific cluster) that the result became useful.

This is a very shallow analogy. Fine-tuning is rather the standard technical approach to reduce compute, even if you have access to the code and all training data. Hence there has always been a rich and established ecosystem for fine-tuning, regardless of “source.” Patching closed-source binaries is not the standard approach, since compilation is far less computational intensive than today’s large scale training.

Java byte codes are a far fetched example. JVM does assume a specific architecture that is particular to the CPU-dominant world when it was developed, and Java byte codes cannot be trivially executed (efficiently) on a GPU or FPGA, for instance.

And by the way, the issue of weight portability is far more relevant than the forced comparison to (simple) code can accomplish. Usually today’s large scale training code is very unique to a particular cluster (or TPU, WSE), as opposed to the resulting weight. Even if you got hold of somebody’s training code, you often have to reinvent the wheel to scale it to your own particular compute hardware, interconnect, I/O pipeline, etc… This is not commodity open source on your home PC or workstation.

The situation is somewhat different and nuanced. With weights there are tools for fine-tuning, LoRA/LoHa, PEFT, etc., which presents a different situation as with binaries for programs. You can see that despite e.g. LLaMA being “compiled”, others can significantly use it to make models that surpass the previous iteration (see e.g. recently WizardLM 2 in relation to LLaMA 2). Weights are also to a much larger degree architecturally independent than binaries (you can usually cross train/inference on GPU, Google TPU, Cerebras WSE, etc. with the same weights).

There is even a sentence in README.md that makes it explicit:

The source files in this repo are for historical reference and will be kept static, so please don’t send Pull Requests suggesting any modifications to the source files […]

He was criticized also because the girls were not in danger of becoming infected. See e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6724388/ :

The Chinese episode has also generated other issues. Several notes demonstrate that this was an experiment and not a therapeutic intervention (even He Jiankui called it a ‘clinical trial’). The babies were not at risk of being born with HIV, given that sperm washing had been used so that only non-infected genetic material was used. Further, even though one of the parents (or both) was infected, it did not mean the children were more prone to becoming infected. The risk of becoming infected by the parents’ virus was very low (Cowgill et al., 2008). In sum, there was no curative purpose, nor even the intention to prevent a pressing risk. Finally, the interventions were different for each twin. In one case, the two copies of CCR5 were modified, whereas in the other only one copy was modified. This meant that one twin could still become infected, although the evolution of the disease would probably be slower. The purpose of the scientific team was apparently to monitor the evolution of both babies and the differences in how they reacted to their different genetic modifications. This note also raised the issue of parents’ informed consent regarding human experimentation, which follows a much stricter regimen than consent for therapeutic procedures.

Other critical articles (e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524470/) have also cited in particular https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779710/, which states in the result section:

No HIV transmission occurred in 11,585 cycles of assisted reproduction using washed semen among 3,994 women (95% confidence interval [CI] = 0–0.0001). Among the subset of HIV-infected men without plasma viral suppression at the time of semen washing, no HIV seroconversions occurred among 1,023 women following 2,863 cycles of assisted reproduction using washed semen (95%CI= 0–0.0006). Studies that measured HIV transmission to infants reported no cases of vertical transmission (0/1,026, 95% CI= 0–0.0029). Overall, 56.3% (2,357/4,184, 95%CI=54.8%–57.8%) of couples achieved a clinical pregnancy using washed semen.

https://youtu.be/FaOSCASqLsE?t=235

In the beginning, only privileged ones will be allowed to run in pass-through mode. But goal/roadmap calls for all FUSE filesystems eventually to have this near-native performance.

Well, if you have a constructive suggestion which site to link instead regarding kernel developments, I am all ears:

• Not sure that raw commits are readable or have sufficient context for non kernel development readers here
• LWN, particularly timely/kernel development news there, has gone mostly paywall, and there will be (legitimate) complaint if I link articles needing a LWN subscription

Not sure what called for this blatant personal attack. My post history speaks for itself, quite in comparison to yours. And Phoronix is well-known Linux website, and its test suite is in fact even referenced in various regression tests/patches in LKML (also not sure what/if any kind of kernel development you have done).

German news outlets reported that there were certain days, when this person received as many as three vaccinations within the same day. https://archive.ph/pqwVK (in German, original pay-walled)

My understanding is that it allows you to play planar video from a website, but not (yet?) side-loaded videos that are spherical/hemispherical. And the latter is what these people really wanted for this application.

My motivation was the “dead wrong expecting someone to step up like adults in the room” part.

Retention, or the lack thereof, when cold-stored.

In term of SD or standard NAND, not even Nintendo does that. Nintendo builds Macronix XtraROM in their Game Card, which is some proprietary Flash memory with claimed 20 year cold storage retention. And they introduced the 64 GB version only after a lengthy delay, in 2020. So it seems that the (lack of) cold storage performance of standard NAND Flash is viewed by some in the industry as not ready for prime time. Macronix discussed it many years back in a DigiTimes article: https://www.digitimes.com/news/a20120713PR201.html.

And Sony and Microsoft are both still building Blu-ray-based consoles.

Yes. If you mean “CLI” as for e.g. pacman install, it is a GUI (Electron) application, so I expect will install straight from e.g. KDE Discover and then run without you touching the shell.

Installing podman-compose with the immutable filesystem is fairly straight forward, since it is just a single Python file (https://github.com/containers/podman-compose/blob/devel/podman_compose.py), which you can basically install anywhere in your path. You can also first bootstrap pip (python3 get-pip.py --user with get-pip.py from https://github.com/pypa/get-pip) and then do pip3 install --user podman-compose.

There might be several misunderstandings:

• Docker Desktop ≠ Docker Engine, and I think what you (and several in this thread) are thinking is actually Docker Engine. Docker Desktop ultimately includes a Docker Engine inside, but it does not appear you need that virtual machine (e.g. running non-Linux code). See: https://docs.docker.com/desktop/faqs/linuxfaqs/#what-is-the-difference-between-docker-desktop-for-linux-and-docker-engine
• Docker Desktop is based on KVM, which already works with Flatpak. So this is not something new. For example, GNOME Boxes is available as Flatpak and provides a way to run KVM guests in SteamOS.
• Starting with version 3.5 (the current stable) SteamOS already includes Podman with the default installation. And running the daemon-y Docker Engine “bare metal” is not going to be any easier with the immutable filesystem. While Docker Desktop solves this by using KVM, it adds another layer with performance loss, vs. just running Podman containers.

So what you want is already available, and no Docker Desktop is actually needed.

https://fosstodon.org/@jimsalter/110441094176772187

Yes. But one should also note that only a limited range of Intel GPU support SR-IOV.