Could you explain what feelings you have about AI, and how you see these feelings as opposing. If you are just uncertain about AI (holding no opposing views), I think you would want to research more. Maybe if we knew more about your teams at your work, and what is being developed. You could honestly just spend an hour a week working on standardizing coding agent availability and licensing/subscriptions, and leave it at that. Either they weren't looking for someone who was a machine learning engineer, or whoever promoted you is clueless to what AI actually means.

Thank you so so much for pointing out ROCmFP4. I have been tinkering with my RDNA 3 framework on llama. I was struggling with ROCm llama.cpp and have been using vulcan in the meantime. I know there’s some issues on the llama.cpp github to try and fix my issue (UMA stuff), but haven’t come across this specific project. Gonna try it out

… if you have the ram (with fast enough bandwidth)

MoE models are pretty magic on my laptops 32gb ram. 24 tok/sec on DDR5-5600 using Gemma 4 26B-A4B is so much faster than a dense model

UMich research cited in the article estimates a carbon dioxide emissions per year (using constant mileage typical per year) break even time between EV and ICE SUVs to be 1.6-1.9 years.

Promising. But, is carbon dioxide emissions the only environmental impact? Humanitarian impact (e.g. rare metal extraction labor conditions)? I’m not an environmentalist researcher/academic. I wonder how accurately just one emissions prediction portrays the full picture.

Fair enough. Let me quickly go through the one-liner, command-by command

# Joined by `&&`, bash runs these commands in sequence (as if run individually in shell), but exits/stops execution early if any command fails (return nonzero)
TMP_DEB=$(mktemp --suffix=.deb) && curl -sSL "https://support.brother.com/g/b/downloadend.aspx?c=us&lang=en&prod=hll2465dw_us&os=128&dlid=dlf106036_000&flang=4&type3=10283" -o "$TMP_DEB" && sudo apt install -y "$TMP_DEB" && rm -f "$TMP_DEB"  

# Going command by command:

# First, we create a local variable in the shell, named `TMP_DEB`
# We assign the value to `$(...)`. This stores the string output (to stdout) of running the command `mktemp ...` to `TMP_DEB`
# `mktemp` creates a temporary file and prints its name, which uses the name template `tmp.XXXXXXXXXX`
# `--suffix=.deb` flag appends `.deb` to the name template
TMP_DEB=$(mktemp --suffix=.deb)

# At this point, we've created a temporary file, and saved the name to a variable in bash
# Next, we download the file using curl. `-s` makes output silent, `-S` shows errors in output, and `-L` follows redirects
# note the url doesn't end in `.deb`, implying that we will be redirected by the web server to the file path. without -`L` curl will download a page that stores the redirection response from the web server, not the .deb package
# `-o "$TMP_FILE"` forces curl to store the downloaded file to the tmp file we created
# note the quotes around the variable expansion. `$TMP_FILE` would also resolve the string stored in the variable, but we use quotes to avoid string globbing (google this)
curl -sSL "https://support.brother.com/..."

# Next, we install the package with apt
# note: we use the string stored in the variable `TMP_DEB`, the filepath to the temp file we created, and downloaded the deb package
# `-y` flag skips the confirmation question "install package [y/n]: `
sudo apt install -y "$TMP_DEB"

# Finally, to clean up we delete the tmp file
rm -f "$TMP_DEB"

I certainly wasn’t trying to “encourage” anything. I agree, blindly trusting commands is dangerous.

In this context I present a specific explanation of how the install works. This adds to the novice’s knowledge, and allows them to begin to understand what my one-liner does.

I think that without the context of instructions on how to do it manually, yes, you could make the case i’m enabling beginners to form/reinforce bad habits.

That printer probably supports AirPrint, which Mint supports without any extra tinkering. Connect the printer to your network, and try going through linux mint and adding the printer through the settings. If it doesn’t show up, then you can try using drivers (install using below command) and then re-adding the printer

Install by pasting this into your terminal. Enter your password when prompted.

TMP_DEB=$(mktemp --suffix=.deb) && curl -sSL "https://support.brother.com/g/b/downloadend.aspx?c=us&lang=en&prod=hll2465dw_us&os=128&dlid=dlf106036_000&flang=4&type3=10283" -o "$TMP_DEB" && sudo apt install -y "$TMP_DEB" && rm -f "$TMP_DEB"  

Explanation if you want to learn:

• Brother offers drivers online
• Download the “linux printer driver (.deb package)”
• Then, to install onto your system, use your package manager and tell it to install the package you downloaded sudo apt install ./Downloads/package_name.deb

This is:

• yet another ploy by predatory, profit-hungry AI companies to get people to fall into LLM dependence and need to pay for a subscription after the free trial ends
• potentially going to degrade code quality of the countless open source repos many depend on for security and stability
• not the solution to help open source maintainers — pay them for their work with actual money, not “free” trials and credits to proprietary software

Yes, you do know the boundaries of AI. It is purely matrix multiplication: its output distribution is just as intelligible as the distribution of rolls of a dice. We receive a probability distribution for the next token given a sequence of tokens. This is demonstrable; search for softmax online.

To fairly equate a dice roll event to a model prompt event we must understand the technicalities. To say you have a 20 sided die, is equivalent to saying you have a specific model’s architecture and value of every parameter, in the context of qualifying event determinism.

If you can assume your die is fair, and 20 sided, that is an equivalent assumption about a model as to saying it’s llama-3.1-8B-instruct. That is, you do know the specific model weights, corresponding to a functional relationship between input and output which is deterministic. That is, if you know the model weights, which is equivalent to knowing whether a die is fair and n-sided, you can deterministically predict the output of a model as you can deterministically predict which number on a die will land

You’re making specific, technical errors about the mathematical basis of language modeling, and equating things fallaciously to a similar deterministic event.

Despite this, your intuition is right: we can’t perceptually predict the output of a model as we can’t perceptually predict what number will result from a die roll

Language modeling is equivalent to a dice roll (given a perfect random number generator). Setting the temperature to 0 removes all randomness from the output, meaning the model always selects the highest probability next word, and the model becomes 100% deterministic. That is, the output of a model is entirely predictable given temperature = 0, you know the model weights, and the seed/prompt.

These technicalities aside, it’s true for both a dice roll event and a specific model/prompt event that, practically speaking, the outputs are treated as probabilistic despite being mathematically/technically deterministic: a human can’t predict with 100% accuracy the output of a die despite the theory (classical mechanics of die positioning, force, velocity, friction, …) proving determinism

How it currently exists, yes in most cases it is trained on stolen cognitive labor. Do you think this is inherent to the technology itself, however? Consider a model trained on entirely public domain data, or non-copyleft liscence not requiring attribution. E.g., talkie

Totally agree that we need strict regulation.

If only we lived in a society where people could be freely able to produce cognitive labor while also being guaranteed a dignified life with universal basic services and income, regardless of what they produce. Then, like with piracy, LLM training, in my opinion, could be trained on anything without harming original authors.

i honestly believe it isn’t that everyone here is only pitchforks and cheerleading. i agree “fuck AI” on the surface, semantically is a gross oversimplification without nuance; but rhetorically this really means “fuck AI corporations and their cronies”.

this community isn’t strictly fuck AI from a technology standpoint, but from the environmental and socioeconomic standpoint.

the “fanboys” refers to are supporters of the massive corporations pushing their slop and enshittification, which i hope you despise as much as the rest of us