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Search results for tag #llama

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[?]Lisa S Baker Art » 🌐
@LisaSBaker@mastodon.sdf.org

Obsessed with this one 😍☕ The cozy stripes, the soft neutral tones, and that calm little coffee moment… such a vibe 🖤✨ Coffee Llama - Available here: 1-lisas-baker.pixels.com/featu

Coffee Llama - No Text by Lisa S Baker.

A calm and elegant llama stands poised in a bold striped sweater dress, savoring a steaming cup of coffee. Soft brushstrokes and warm neutral hues give this whimsical painting a modern yet relaxed vibe. The llama’s peaceful expression and upright posture lend a quiet confidence, making this a standout piece for lovers of both caffeine and character.

Alt...Coffee Llama - No Text by Lisa S Baker. A calm and elegant llama stands poised in a bold striped sweater dress, savoring a steaming cup of coffee. Soft brushstrokes and warm neutral hues give this whimsical painting a modern yet relaxed vibe. The llama’s peaceful expression and upright posture lend a quiet confidence, making this a standout piece for lovers of both caffeine and character.

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    [?]DrBob, 🧠 Mechanic » 🌐
    @drrjv@vmst.io

    😎 Run local AI models on your iPhone

    AnywAIr, which is a play on the word “anywhere”, is a nifty little iOS app that lets you play with AI models – regardless of if you have an internet connection. It offers custom themes, a plethora of tools and games, and all of the local AI models you could want to mess with.

    9to5mac.com/2025/12/20/indie-a

    Text Shot: People often talk about the power of Apple silicon when it comes to local AI models, and AnywAIr gives you a glimpse of what’s possible with your iPhone.
First and foremost, the app supports both MLX and Llama models. This gives you loads of flexibility for which model you pick, and AnywAIr supports all of the notable offerings, including Qwen 2.5, Gemma 3, Llama 3.2 and more.
At the end of the day, these are still going to be small models that can run on an iPhone with 8-12GB of memory. That said, they’re still rather knowledgable, and have one large advantage: privacy. If you’re worried about AI companies being able to access your chats (or train based on your conversations), local LLMs have a huge advantage.

    Alt...Text Shot: People often talk about the power of Apple silicon when it comes to local AI models, and AnywAIr gives you a glimpse of what’s possible with your iPhone. First and foremost, the app supports both MLX and Llama models. This gives you loads of flexibility for which model you pick, and AnywAIr supports all of the notable offerings, including Qwen 2.5, Gemma 3, Llama 3.2 and more. At the end of the day, these are still going to be small models that can run on an iPhone with 8-12GB of memory. That said, they’re still rather knowledgable, and have one large advantage: privacy. If you’re worried about AI companies being able to access your chats (or train based on your conversations), local LLMs have a huge advantage.

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      [?]C. » 🌐
      @cazabon@mindly.social

      @catsalad

      @jerry

      New tie? Or is it a scarf?

        [?]Bytes Europe » 🌐
        @byteseu@pubeurope.com

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        [?]Tina LeCour » 🌐
        @ArtThatMakesYouSmile@mastodon.online

        A Llama In The Pink Parlor for Monday, have a good one!
        Availalble here..tina-lecour.pixels.com/feature

        This is a vintage shabby chic style canvas of an adorablel llama sitting in a pink chair decorated with roses against a pink and white batik vintage background.

        Alt...This is a vintage shabby chic style canvas of an adorablel llama sitting in a pink chair decorated with roses against a pink and white batik vintage background.

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          [?]Sharon Cummings Art (Official) » 🌐
          @SharonCummingsArt@socel.net

          12 ★ 6 ↺
          planetscape boosted

          [?]Anthony » 🌐
          @abucci@buc.ci

          This misguided trend has resulted, in our opinion, in an unfortunate state of affairs: an insistence on building NLP systems using ‘large language models’ (LLM) that require massive computing power in a futile attempt at trying to approximate the infinite object we call natural language by trying to memorize massive amounts of data. In our opinion this pseudo-scientific method is not only a waste of time and resources, but it is corrupting a generation of young scientists by luring them into thinking that language is just data – a path that will only lead to disappointments and, worse yet, to hampering any real progress in natural language understanding (NLU). Instead, we argue that it is time to re-think our approach to NLU work since we are convinced that the ‘big data’ approach to NLU is not only psychologically, cognitively, and even computationally implausible, but, and as we will show here, this blind data-driven approach to NLU is also theoretically and technically flawed.
          From Machine Learning Won't Solve Natural Language Understanding, https://thegradient.pub/machine-learning-wont-solve-the-natural-language-understanding-challenge/


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            [?]Anthony » 🌐
            @abucci@buc.ci

            Lately I've been thinking of the much-hyped generative AI products of the last 2 years as anti-technology.

            That may sound odd, since these are clearly technological artifacts in the way we've come to understand technology, and they're being produced by what's commonly called the tech sector. However, there are at least two ways in which these artifacts differ markedly from what we usually (used to?) think of as "technology":

            (1) They tend to have a deskilling effect. The English word "technology" ultimately derives from the Greek word "tekhnē", which can be interpreted as meaning skill or craft. It's very much about a human being's ability to perform a task. Yet much of generative AI is aimed at removing human beings from a task, or minimizing our involvement. In that sense generative AI is very much anti-tekhnē

            (2) They tend to lie squarely in what Albert Borgmann called "the device paradigm". L.M. Sacasas has several nice takes on Borgmann's distinction between devices and focal things. See https://theconvivialsociety.substack.com/p/why-an-easier-life-is-not-necessarily and also https://theconvivialsociety.substack.com/p/the-stuff-of-a-well-lived-life (and of course, read Borgmann himself!). Simply put, devices tend to hide their inner workings under a simplified "interface"; a device is a device “if it has been rendered instantaneous, ubiquitous, safe, and easy”, if it hides the means in favor of the ends. By contrast, focal objects tend to invite you into fully experiencing the focal practices they enable, to experience the means and the ends. In particular, they tend not to be easy: you have to engage with and learn to use them. Guitars are an interesting example of focal objects. To be (I hope not overly) simplistic, devices dumb you down while focal objects train you up. Devices are anti-tekhnē, and to the extent that current generative AI is created and deployed in the device paradigm, it is too.

            None of this means generative AI has to be anti-tekhnē. I do admit though that I struggle to see how to make it less device-y, at least as it's currently made and used (I do have a few half-formed thoughts along these lines but nothing worth sharing).


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              [?]Anthony » 🌐
              @abucci@buc.ci

              If I had the time, energy, and education to pull it off, I'd do some scholarship and writing elaborating on this juxtaposition:

              - Statistics, as a field of study, gained significant energy and support from eugenicists with the purpose of "scientizing" their prejudices. Some of the major early thinkers in modern statistics, like Galton, Pearson, and Fisher, were eugenicists out loud; see https://nautil.us/how-eugenics-shaped-statistics-238014/
              - Large language models and diffusion models rely on certain kinds of statistical methods, but discard any notion of confidence interval or validation that's grounded in reality. For instance, the LLM inside GPT outputs a probability distribution over the tokens (words) that could follow the input prompt. However, there is no way to even make sense of a probability distribution like this in real-world terms, let alone measure anything about how well it matches reality. See for instance https://aclanthology.org/2020.acl-main.463.pdf and Michael Reddy's The conduit metaphor: A case of frame conflict in our language about language

              Early on in this latest AI hype cycle I wrote a note to myself that this style of AI is necessarily biased. In other words, the bias coming out isn't primarily a function of biased input data (though of course that's a problem too). That'd be a kind of contingent bias that could be addressed. Rather, the bias these systems exhibit is a function of how the things are structured at their core, and no amount of data curating can overcome it. I can't prove this, so let's call it a hypothesis, but I believe it.


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                [?]Anthony » 🌐
                @abucci@buc.ci

                Lately I've been thinking of recent generative AI as the equivalent of fast food for the mind.


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                  [?]Anthony » 🌐
                  @abucci@buc.ci

                  The information oil spill caused by continues to claim casualties: https://www.404media.co/bards-and-sages-closing-ai-generated-writing/
                  In a notice posted to the [Bards And Sages Publishing] site, founder ​Julie Ann Dawson wrote that effective March 6, she was winding down operations to focus on her health and “day job” that’s separate from the press. “All of these issues impacted my decision. However, I also have to confess to what may have been the final straws. AI...and authors behaving badly,” she wrote.
                  Closure announcement: https://www.bardsandsages.com/closure-announcement.html