ejs

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

i would say this is like if open code and open web ui had a baby it would be this. It’s a web interface for self hosting models but runs them through open code to make it agentic. Helpful for non developers to get into running models, but imo isn’t significant bc using open code tui and connecting it to a llama.cpp or vllm self hosted api is not difficult for devs

The first study cited in the article, a meta study in cognition, alzheimer’s, sleep deprivation, traumatic brain injury, and depression notes:

DC has conducted industry-sponsored research involving creatine supplementation and received creatine donations for scientific studies and travel support and speaking honoraria for presentations involving creatine supplementation at scientific conferences and on social media. In addition, DC serves on the Scientific Advisory Board for Alzchem and Create (companies that manufacture creatine products) and as an expert witness/consultant in legal cases involving creatine supplementation. NF declares no conflicts of interest

I don’t have any familiarity with using this kind of software, but I looked through the git repo of SavaPage. It looks like it has been actively developed for the past few years, which is a great sign, but it looks like almost all commits are done by one user. The issue tracker is also a little meager, with just one open issue, potentially pointing to a very small user base. Adoption heavily depends on as long as that one person keeps maintaining the project.

Honestly, you’re a few months late to the whole buying GPUs for local llms party, so expect exorbitant prices even for older cards

The name of the game is vram. For the most part, more is better. If you can get your hands on multiple matching (same model) 24gb or higher cards (within price range), you’re golden.

Going for more than 2 gpus can become challenging with motherboard pcie slot heights, so make sure either your cards aren’t too tall or you have widely spaced out pcie slots.

For inference, speed (tokens/second) is limited by memory bandwidth. Go for faster bandwidth memory cards if you can afford it (e.g. GDDR6 will be faster than GDDR5).

Also with multi gpus you will need an adequate power supply, and a large enough case.

If you want to be a bit eccentric and load huge models, you can also go the CPU route and fill up a motherboard with 256 GB ram, because then you’re in the several hundred B param model territory, which could, depending on your use case, be better than having faster inference on smaller/quantized models. Even then, DDR5 with high MHz is still way slower than gpus.

yea there’s still honestly some downsides to Qobuz, including:

• Artist profiles: lack of consistency on details like images, descriptions
• Generated recommendations: magazine articles and album reviews (sometimes) written by humans are top notch; the tradeoff is that recommendations based on specific playlists are often far less “close” musically and I often get random and unexpected auto plays; there is no “daily mix” or “similar artists” or good recommendations for adding new tracks to a longer playlist
• Library: across the many diverse genres I listen to, frequently newer releases are delayed on Qobuz. Older music library is outstanding, extremely few of my 10s of thousands of total tracks of jazz records were unavailable

when i switched from spotify to Qobuz several months ago they gave me access to a third party playlist conversion site https://soundiiz.com/ with premium features free for the first month of my subscription. Conversion of playlists and liked songs was easy and done within minutes of signing up for Qobuz. I can’t recommend moving off spotify enough; Qobuz won my pick because how they pay artists (seemingly) the highest rate per stream.

lol they already support running local models. wtf is the distro gonna do…? pre-install llama.cpp? this is so silly to me that people are resigning over this, too.

global dominance of English in the 20th and 21st centuries is quite the euphemism for the global imperialist reign of Britain and the US and its cultural erasure globally

This is a dumb story. They researchers prompted a coding agent to “replicate yourself as a running instance on the local device”. This is in my opinion equivalent to prompting claude code “install a second instance of claude code on my system,” a trivial task that takes maybe 3 lines of bash to be executed by the agent.

Calling this “self-replication” is a heinous sensationalization. In particular, no model or agent will do this autonomously. The self replication requires a bad actor to prompt the agent to do so.

Read the paper (and not this bullshit article) here: https://arxiv.org/pdf/2412.12140

Has anyone compiled a list of where projects are moving to? I know many linux desktop applications are self hosting on gitlab, but i’ve also seen gitea and codeberg. If anyone has opinions about a preference, do comment. I have been enjoying self hosting gitea for my simple personal projects and for deploying simple web apps, all on $5 vps.

no, you need to secure between 10s of thousands and millions in campaign finances, the good will of corporate media, and support of the establishment political parties. exceptions are extremely rare

I suggest looking at llm arena leaderboards filtered by open weight models. It offers benchmarks at a very complete and statistically detailed level for models, and usually is quite up to date when new models come out. The new Gemma that just came out might be the best for 1x GPU, and if you have a bunch of vram check out the larger Chinese models

this just in: pro-capitalist politicians have no other interest than self-enrichment. more news at 12…

upend the neoliberal institutions. they do not serve the people.

why have it at all?

Despite all of us collectively agreeing that the law is dumb/flawed, the 40 M residents of Cali should have the liberty to be able to use distros that depend on systemd, legally. And, the developers of these distros using systmed (whether you interpret the law to see them as OS providers or not) want to be able to provide these distros legally.

Now that this functionality exists, apps are going to start using it and requiring it

Yes, but not all apps. While the CA law mandates that app developers must use some API to get the age bracket, the merged code into systemd is not causually related to all apps actually implementing and using the API. Just because systemd merged this code does not inherently result in every single user application querying this, nor does it force you to install apps that do query the API. One may freely choose to not use apps that require it. If one needs an app that requires it, one may set a garbage DOB to their user. I don’t see this as an issue. Do you?

It seems you disagree with the law (so do I) but are blaming the wrong person here (author of merged systemd code). I maintain that complying with the law is harmless, and thus it is beneficial to add this DOB field to the userdb json, because in all cases of some distro user using their computer, they are not compelled to compromise their personal privacy.

Your example relies on some assumptions:

• User has chosen to opt into filling in their actual DOB (not some nonsense date)
• User has app installed that fetches the DOB from userdb

None of these assumptions are garunteed by the merged code into systemd. The following are optional, and not required as a result of the code merged into systemd:

• Merely setting data into the DOB field
• Attesting DOB honestly
• installing some prying application that queries

It’s possible to put your full first and last name into your user, so by your logic the first and last name fields of the user profile should not exist.

Did that help identify the absurdity of your argument?

I’m still not convinced there is a direct casual link between the merged attestation and some future surveillance. Your speculation that this is some deliberate political strategy for some gradual escalation from attestation to surveillance is not logical evidence, but some belief you have, which holds no weight in an argument; it stands that you have no concrete evidence against your logic being a slippery slope fallacy.

You did concede to my argument by admitting “by itself the attestation is pointless.” Good to know we agree that there is outrage over nothing.

By saying “PR vs merge is a moot point”, you’re running away from a logical/technical debate by being dismissive; you are openly stating you don’t care how the mechanics of these foss projects actually work. Again, you can have a speculative opinion, but that is not a logical argument.

When you argue parents should be using OS parental controls, you do know that that’s exactly what the systemd age attestation PR is building, right? It seems you’re fighting against the very infrastructure needed for your preferred solution.

Finally, you conflate local infrastructure with cloud APIs (vindicating my claim that people opposing this are ignorant to the actual code being merged): Systemd is a local init system. Connecting the local userdb age integer to an external, network reliant govt API is a monumental leap in implementation and architecture, not a simple “add this API” patch that can be quietly slipped in without the entire foss community noticing and revolting. The attestation PR, for instance, had around 200 comments, of back and fourth refining of implementation and discussion, before merge.