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Cake day: June 4th, 2023

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  • I asked some larger local models:

    gpt-oss-120b got it wrong:

    spoiler

    The joke works on a double‑misunderstanding:

    1. The monk’s perspective – He’s lived in a cloister for decades, never having encountered prostitution. When the two women say “twenty dollars for a quicky,” he assumes “quicky” means a brief sexual act (the usual slang “quickie”).

    2. The mother superior’s perspective – She either pretends not to know the slang or deliberately pretends she doesn’t. When the monk asks what a “quicky” is, she answers literally: it’s the price – twenty dollars – just as the street prostitutes said.

    The humor comes from the monk’s naive expectation that the word must have a sexual meaning, and the mother superior’s dead‑pan reply that the word is simply a label for a $20 transaction. The punch line flips the expected explanation (what a quicky is in the sense of a sexual act) into a matter of cost, highlighting the monk’s cluelessness and the absurdity of the situation.

    MiniMax M2 (Q3) did pretty well:

    spoiler

    The joke hinges on a double‑meaning of the word quickie.

    1. “Quicky” in everyday slang – The phrase “twenty dollars for a quicky” is what a prostitute says when offering a quick sexual encounter. The word quickie (or quicky) is slang for a brief, usually paid‑for, sexual act.

    2. The monk’s confusion – The head of the monastery has been isolated for 30 years, so he’s completely out of touch with common slang. When he hears “twenty dollars for a quicky,” he takes it literally as if it were some product or service he doesn’t understand (“What’s a quicky?”). He’s naively picturing a product rather than a sexual proposition.

    3. Mother Superior’s punchline – She answers, “Twenty dollars, same as in town.”

    • On the surface, it sounds like she’s saying the price of the item is the same as it is elsewhere.
    • In reality, the line is a tongue‑in‑cheek way of saying, “A quickie costs $20, just like it does in the town,” i.e., the same price a prostitute would charge.
    1. Why it’s funny
    • Unexpected context: A cloistered religious figure asking about a slang term creates an incongruity.
    • Double entendre: The answer deliberately plays on the word “quicky,” letting the audience catch the sexual meaning while the monk (and the audience) are initially misled.
    • Relief of tension: The punchline clears up the misunderstanding in a witty, slightly risqué way, delivering the “aha!” moment.

    So the humor comes from the monk’s innocent, clueless question and the Mother Superior’s witty, double‑meaning answer that reveals the true, sexual nature of the “quickie.”

    GLM-4.6 IQ2 did slightly worse:

    spoiler

    Of course! This is a classic joke that works by subverting your expectations about a character. Let’s break it down. The Simple Explanation

    The humor comes from the shocking and unexpected punchline. You expect the holy Mother Superior to be horrified by the term “quicky” and to scold the monk for even asking. Instead, her answer reveals that she is not only familiar with the concept but also knows the current market price, implying she’s involved in the very same business as the prostitutes in town. A More Detailed Breakdown

    To fully get the joke, you need to look at the three key parts:

    1. The Setup: The Innocent Character
    • The old monk is the perfect “straight man” for the joke. He’s been in a secluded monastery for 30 years, making him completely naive and innocent about the outside world.
    • His confusion is genuine. He doesn’t understand the slang term “quicky” (which means a brief, illicit sexual encounter for money) and seeks knowledge from a figure of authority within his own world—the Mother Superior.
    • The audience knows what a quicky is, so we are in on the secret. We expect the Mother Superior to explain it to him in a shocked, religious way.
    1. The Punchline: The Subverted Expectation
    • The core of the joke is the Mother Superior’s reply: “Twenty dollars, same as in town.”
    • This response is funny for two main reasons: * It’s not a definition: She doesn’t explain what a quicky is, she just gives a price. This immediately signals that something is wrong. * It’s a price comparison: The most brilliant part of the punchline is the phrase “same as in town.” It implies that not only is she familiar with the service, but she’s also aware of her local competition’s pricing and is matching it.
    1. The Implication: The Shocking Truth

    The punchline forces you to re-evaluate the Mother Superior’s character entirely.

    • She’s not innocent: Instead of being a symbol of piety and purity, she’s revealed to be worldly, pragmatic, and possibly running her own “side business” out of the monastery.
    • The Joke’s World is Turned Upside Down: The most holy person in the monastery is revealed to be the most street-smart and corrupt, while the monk is blissfully unaware. The people who are supposed to be farthest from sin are revealed to be experts in it.

    In summary: The joke sets you up to expect a lesson on morality, but instead, it delivers a punchline that reveals the “moral authority” is a savvy businesswoman. The humor lies in the massive disconnect between the Mother Superior’s holy title and her shocking, real-world knowledge.

    Qwen3 235b Q3 also did quite well:

    spoiler

    This joke is a classic example of a double entendre and relies on misunderstanding and irony.

    Here’s how it works:

    1. Setup: A monk who has lived in seclusion for 30 years goes into town for the first time. He’s completely out of touch with modern slang and social norms.

    2. Inciting Incident: Two sex workers offer him a “quicky” for twenty dollars. In this context, “a quickie” is slang for a brief sexual encounter.

    3. The Monk’s Confusion: The monk doesn’t understand the term “quicky” because he’s lived a celibate, isolated life. So he goes back to the monastery and asks the Mother Superior—his superior in the religious order—what “a quicky” means.

    4. The Punchline: The Mother Superior replies, “Twenty dollars, same as in town.”

    Why it’s funny:

    • The humor comes from the unexpected implication that the Mother Superior does know what a “quicky” is—and not only that, but she seems to be pricing it competitively.
    • This suggests, absurdly and ironically, that there might be some kind of “monastery brothel” or that religious figures are somehow involved in sex work—which is completely contrary to expectations.
    • It’s also funny because the Mother Superior doesn’t explain the meaning of “quicky”—she just quotes the price, implying she’s familiar with the transaction, not the definition.

    In short: The joke plays on the contrast between the monk’s innocence and the Mother Superior’s seemingly worldly knowledge. The punchline subverts expectations by implying that even in a secluded monastery, “a quicky” costs twenty bucks—just like in town. It’s absurd, cheeky, and mildly sacrilegious, which adds to its comedic effect.


  • Not sure if it counts as “budget friendly” but the best and cheapest method right now to run decently sized models is a Strix Halo machine like the Bosgame M5 or the Framework Desktop.

    Not only does it have 128GB of VRAM/RAM, it sips power at 10W idle and 120W full load.

    It can run models like gpt-oss-120b or glm-4.5-air (Q4/Q6) at full context length and even larger models like glm-4.6, qwen3-235b, or minimax-m2 at Q3 quantization.

    Running these models is otherwise not currently possible without putting 128GB of RAM in a server mainboard or paying the Nvidia tax to get a RTX 6000 Pro.







  • I’m running gpt-oss-120b and glm-4.5-air locally in llama.cpp.

    It’s pretty useful for shell commands and has replaced a lot of web searching for me.

    The smaller models (4b, 8b, 20b) are not all that useful without providing them data to search through (e.g. via RAG) and even then, they have a bad “understanding” of more complicated prompts.

    The 100b+ models are much more interesting since they have a lot more knowledge in them. They are still not useful for very complicated tasks but they can get you started quite quickly with regular shell commands and scripts.

    The catch: You need about 128GB of VRAM/RAM to run these. The easiest way to do this locally is to either get a Strix Halo mini PC with 128GB VRAM or put 128GB of RAM in a server/PC.


  • I can’t buy salami in the supermarket and justify it by saying the cow is dead anyways and someone already sliced it up. It’s down to demand and that’s really complex.

    You pay for the salami and thus entice them to make more. There is monetary value for them in making more salami.

    Does Mark Zuckerberg really gift an open-weights model to me out of pure altruism?

    I don’t really know why they initially released their models but at least they kicked off a pissing contest in the open weight space on who can create the best open model.

    Meta has not released anything worthwhile in quite a while. It’s pretty much Chinese models flexing on American models nowadays.

    Still, their main incentive to train those models lies with businesses subscribing to their paid plans.

    However that’s not the entire story either, we still buy the graphics cards from Nvidia and we also set free some CO2 when doing inference, even if we didn’t pay for the training process.

    True, I exclusively run inference on AMD hardware (I recently got a Strix Halo board) so at least I feel a little bit less bad and my inference runs almost purely on solar power. I expect that is not the norm in the local AI community though.

    If I use some research mode from one of the big AI services, it’ll randomly google things, but some weird blog post or a wrong reddit comment will show up on the same level as a reputable source.

    I rarely use the commercial AI services but also locally hosted the web search feature is not really that great.

    It’s awesome to sift through documentation, though. Or a company’s knowledgebase. And I think those are the real use-cases for RAG.

    Yes, I prefer to use RAG with information I provide. For example, ask a question about Godot and provide it the full Godot 4 documentation with it.

    Still working on getting this automated though. I would love to have a RAG knowledge base of Wikipedia, Stackoverflow, C documentation, etc. that you can query an LLM against.


  • From how datasets contain a good amount of pirated stuff

    Personally, I do not care if datasets contain pirated stuff because the copyright laws are broken anyway. If the entirety of Disney movies and Harry Potter books are somewhere inside those datasets, I can play them a song on the world’s smallest violin.

    Smaller artists/writers are the ones I empathize with. I get their concern about large corporations using their stuff and making money off of it. Not entirely something that applies to local AI since most people here do this for themselves and do not make any money out of it.

    to the environmental impact

    That’s actually the saddest part. Those models could be easily trained with renewables alone but you know, capitalism.

    Do we do the same unhealthy stuff with it, or do we help even out the playing field so it’s not just the mega-corporations in control of AI?

    The thing is, those models are already out there and the people training them do not gain anything when people download their open weights/open source models for free for local use.

    There’s so much cool stuff you can do with generative AI fully locally that I appreciate that they are available for everyone.

    Second controversial take: I think AI isn’t very intelligent.

    If we are talking about LLMs here, I don’t think that’s much of a controversial take.

    Most people here will be aware that generative AI hallucinates all the time. Sometimes that’s good, like when writing stories or generating abstract images but when you’re trying to get accurate information, it’s really bad.

    LLMs become much more useful when they do not have to completely rely on their training data and instead get all the information they need provided to them (e.g. RAG).

    I’m a huge fan of RAG because it cites where it got the information from, meaning you can ask it a question and then continue reading in the source to confirm. Like fuzzy search but you don’t have to know the right terms.