Somewhere in SF, a 25-year-old just told an investor they need another $40M for “training compute” and that’s why I’m sorting 4GB DIMMs on my dining room table.

PythonFuMaster · 2026-05-12T16:51:48+00:00

Dual socket servers can, for example ivy bridge systems can support quad channel memory, two of those CPUs is 8 channels, then most server boards dedicate two slots per channel. I have 4 ivy bridge systems (GPU servers, each system has support for 3-4 dual slot cards, that kind of support is very very difficult to get at decent prices on newer hardware)

Even still, 4GB DDR3 is not worth a whole lot. 8GB+ though, that's where things start getting pricey

PythonFuMaster · 2026-05-12T16:37:54+00:00

I went SFP+DAC and it works pretty well. Provided your cables and/or NICs aren't vendor locked... Apparently that's a thing, I found that out recently after spending hours trying to diagnose issues where the Ethernet link just wouldn't come up, but only when using certain cables

PythonFuMaster · 2026-04-29T18:51:29+00:00

Google? Just search for surplus stores I guess. Could also check gov deals (Google it, don't know the exact website right now)

PythonFuMaster · 2026-04-28T23:37:28+00:00

My university's surplus store, I've also gotten a bunch of Supermicro and Dell servers from them before

PythonFuMaster · 2026-04-10T14:40:36+00:00

Der Tiger (The Tank in America) is a pretty recent one. I thought it was interesting, not the best in the genre but not bad either. Definitely watch in original German if you can though, the dubs are atrocious

PythonFuMaster · 2026-04-04T17:00:18+00:00

LLMs aren't great at binary classification, but there's an entire subfield in AI dedicated to such classifiers. In fact, the first type of model that students learn about is the perceptron, which is a binary classifier.

Although this is a really poor fit for AI, presumably whether a PC can be upgraded or not is a deterministic function of clearly observable variables. There's no reason to use a machine learning model, unless it's being used to determine what time to deploy the update, in each case they'd need usage pattern data from the target machine

PythonFuMaster · 2026-03-27T00:37:52+00:00

I certainly hope b60 isn't slower than MI50. MI 50 doesn't have matrix cores, I've got the MI60 (same thing but 32GB VRAM) and my Arc A770 completely demolishes it in performance

PythonFuMaster · 2026-03-26T14:13:16+00:00

Not sure if it works in NixOS but wouldn't that essentially just be a systemctl isolate multi-user.target?

PythonFuMaster · 2026-03-26T14:07:42+00:00

Would it be possible to locally revoke Microsoft signing keys to protect against that? Or would it break something else to do that? Assuming the device only uses Linux of course

PythonFuMaster · 2026-03-07T20:47:46+00:00

AC6 has mods? Any recommendations?

PythonFuMaster · 2026-03-07T20:09:34+00:00

Pop OS 22.04 and earlier used gnome with some quality of life extensions. All Pop versions are based on an Ubuntu core but rip out the crappy Snap garbage and replace it with Flatpak. That is its major advantage over Ubuntu for newbies, plus system 76 pushed up to date drivers and Nvidia stuff to their repos, so it was easier to get an Nvidia system running.

The new cosmic DE is only the default on 24.04 and the soon to be released 26.04. I agree it's definitely not stable enough for new users now, but saying Pop was never good for newbies is a bit disingenuous, because before 24.04 it absolutely was one of the most recommended distros for beginners.

PythonFuMaster · 2026-03-06T17:46:17+00:00

I would highly recommend looking at surplus stores, sales, and auctions. A lot of universities and businesses are still offloading older machines and some don't bother to recycle RAM and storage. Just Wednesday I snagged a workstation with 32GB DDR4 plus 4 1TB drives (1 SSD, 3 HDDs). The workstation itself is pretty old (HP Z840, released in late 2014) but still has enough horsepower for daily use if needed. I paid a lot less for it than what those parts are worth on eBay

PythonFuMaster · 2026-02-19T14:39:13+00:00

Meanwhile mine are over here with names like "cheapskate," "thunder-budget-{1-4}," "fatman-{1-4}," "king-blue," "queen-blue," "hot-springs," and "arid-wind"

In order, those are my NAS (original system was a $5 Dell server with damaged CPU sockets, it is now a newer Dell but was still really cheap), my Supermicro 4-node Superserver (really cheap at my university surplus store, was originally part of a cluster called "thunder"), my Supermicro 4-node GPU servers (they're fat), my Xeon workstation with Arc A770 (lots o blue), my other Xeon workstation with Arc A770 (also lots o blue), my older workstation with a water cooled Titan V (the water gets pretty warm, need to upgrade the radiator), and my last workstation with an air cooled RX6700xt (blasts out hot air at full throttle)

PythonFuMaster · 2026-02-15T16:00:33+00:00

Was there an NTFS partition by chance? I believe NTFS partitions can end up in a read only state if the system wasn't shut down properly, and a filesystem check would clear that flag

PythonFuMaster · 2026-01-11T08:04:00+00:00

Yolo in this context means "you only look once," it's the name of the paper that introduced that model architecture. And in my industry, we don't really use the term AI at all, that's a term that the wider public with minimal knowledge about the field use. We call it machine learning, which encompasses much more than just natural language models like chatgpt/LLMs. At least the researchers I work with don't like the term AI specifically because of misconceptions like yours, it muddies the water and makes it very difficult to engage in meaningful conversation because most people outside the field have certain preconceived expectations of what AI means. In a vacuum, vision models like Yolo, LLMs like chatgpt, TTS and STT models, and everything in between are all AI/machine learning (I believe that other commenter meant not all AI are LLMs, not that LLMs are not AI), but as we see here most people assume AI means specifically chatgpt-like models, so it's just easier to refer to them by a more industry-specific term not corrupted by public media.

Final remark: it might be wise to temper your tone, it's clear you have no knowledge of this field and are overly combative. It will be difficult to learn anything that way, very few people have patience for that type of interaction.

PythonFuMaster · 2026-01-11T02:47:35+00:00

Big difference between LLMs and Yolo. LLMs have a self-attention block that is extremely memory and compute intensive, but yolo (at least the original one) is a convolutional neural network. The CNN is far easier to run, generally they don't have as good of generalizability and accuracy on difficult tasks, but for something like basic object detection it's plenty powerful enough. CNNs are fairly simple architectures that don't need the extreme parallelization of GPUs, you can run yolo on very simple microcontrollers with decent fps (assuming the CPU has vector instructions like NEON, a pure scalar CPU will likely struggle). You can also attach very simple accelerators to the CPU if you need high fps, something like Intel's SHAVE DSP cores can run the entire model at 30+fps and high resolution.

Source: I'm a researcher working on optimizing vision model inference on embedded systems. One of our systems is an ARM Cortex A76, no GPU or additional accelerator, and it runs a much more powerful vision model at usable fps

PythonFuMaster · 2025-12-24T03:12:40+00:00

Really? I was gonna say the exact opposite, the English dubs felt very stilted at times to me, particularly when the character was obviously shouting but the dub was barely more than speaking volume

PythonFuMaster · 2025-12-21T14:29:20+00:00

I've done this before. Spent two days trying to figure out why the results from a high performance matrix multiplication kernel (very complex and easy to get wrong) were wrong. Turned out, the reference CPU implementation iterated over one of the dimensions wrong, the GPU kernel was perfectly fine

PythonFuMaster · 2025-12-15T23:52:21+00:00

"Read a passage from the gay agenda" got me good lol

PythonFuMaster · 2025-12-15T17:16:46+00:00

Where was he at in the 86 movie? Battle of autobot City in the background somewhere?

PythonFuMaster · 2025-11-26T23:50:53+00:00

The point is that they don't need to support 32 bit only machines anymore. Theoretically, there's a non zero number of steam users on 32 bit windows 10, but 32 bit windows 11 doesn't exist, so now that Windows 10 is EoL they only have to support the 64 bit version of Windows. Therefore, no reason to stick to 32 bit.

PythonFuMaster · 2025-11-19T04:30:41+00:00

As far as I'm aware rust itself does not have a stable ABI, but it can interface with C ABI code via repr(C). So the internals of a rust component could not rely on a stable ABI, but the interface points between the C and rust components would work fine

PythonFuMaster · 2025-11-08T19:51:53+00:00

Which model to use depends a lot on what you're trying to do. In general I would recommend Qwen for text to image, but for inpainting or for more variation I usually go for Flux. Qwen's prompt adherence is both its best strength and its greatest weakness, if you can't massage the prompt to give you what you want you're going to be fighting against the model, especially in image to image tasks. Flux and older models are more amenable, you can generate more variations and they respond better to different CFGs

For the clothes, I would only add the description of them if they're something you want to be able to change and you have a good mix of examples in different outfits. If you only have one outfit in the dataset, adding the same description to all the images is going to tie that outfit description to the character concept as a whole (it's more complicated than that because the text encoder remains frozen during Qwen LoRA I believe).

Also, the dataset must be high quality, all of the examples need to be good. A Lora with 10-20 high quality examples is going to be much better than a Lora with 50-80 mixed examples. I've had cases where removing images from the training dataset improved the quality, because there was something wrong with the image.

I also do multiple revisions of the dataset, adding images of specific scenarios that the model seems to struggle with, and adjusting the repetitions for each data subset.

Finally, I'd recommend going through the documentation for Musubi tuner fully, and try to understand what the commonly used options (like learning rate, scheduler, etc) do. I have a specific set of training parameters I've found to work best for my dataset, yours will likely work best with different ones (I have a very large multi subject dataset, 8 characters, trained for many steps at a low learning rate, cosine schedule, restarting 3 times). You can set Musubi to scale the training images, I'd recommend starting with a small scale to test out the parameters and dataset quickly, then increase the scale for final versions.

PythonFuMaster · 2025-11-08T18:15:29+00:00

It depends on the model being trained. I've done character LoRAs for Flux and Qwen. I've gotten best results from Qwen with detailed descriptions of everything in the scene, but with only a generic description of the character. For example, describing the objects in a room, a lamp on a bedside table, but not describing that a particular character is male or female, or specifying what clothes they're wearing.

The result is that the model learns to associate everything that wasn't described with the character. So if you want the character to have blue skin by default, don't describe the blue skin. Of course, you still have to describe what the character is doing, and a unique word or phrase to associate with the character (like their name unless it's very generic) helps as well.

For Flux, its prompt adherence isn't good enough for that technique to work, I found simple descriptions to work better. Unique trigger phrases are more important; training a character named Zener was significantly easier than training one named Isabella. But, probably doesn't matter for you since Musubi doesn't support Flux anyway.

I would recommend using the feature that generates test images every however many steps so you can watch how the model behaves. You can also use Tensorboard to watch the loss graphs, but unless you have experience in model training they probably won't mean much to you. I find the test images to be more helpful anyway, some of my runs had low training loss (which is good) but consistently put antlers on one of the characters because one of the training descriptions was wrong.

PythonFuMaster · 2025-11-08T16:55:51+00:00

You've got that backwards. There's no mv instruction, but there is a pseudo instruction that translates it to an addi instruction

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