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What spec of Framework Desktop do you run this on?
There is only one and for this model you need the one with 128GiB RAM.
[0] https://frame.work/products/desktop-diy-amd-aimax300/configu...
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My only issue with AI is that its so verbose. What we should encourage are people to simplify with AI. There was that literal 9 page rant by the other day and people had no idea what he was trying to say (too technical language, a poor writer, and seemingly unedited). That could have benefited greatly.
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You would be correct. Ran the article through GPTZero, 100% AI.
These detectors are a scam falsely flagging non-native English speakers: https://plagiarismcheckerai.app/ai-detector-false-positives-... At this point relying on their judgement is beyond folly.
ESET is blocking this site saying: Threat found This web page may contain dangerous content that can provide remote access to an infected device, leak sensitive data from the device or harm the targeted device. Threat: JS/Agent.RDW trojan
Unfortunately Apple appears to be blocking the use of these llms within apps on their app store. I've been trying to ship an app that contains local llms and have hit a brick wall with issue 2.5.2
I think Apple will become increasingly draconian about LLMs. Very soon people won't need to buy many of their apps. They can just make them. This threatens Apple's entire business model.
Apple doesn’t care about revenue from a random TODO app.
Strangely, it is super fast on my 16 Plus, but with longer messages it can slow down a LOT, and not because of thermal throttling. I wish I could see some diagnostic data.
does anyone know of a decent but low memory or low parameter count multilingual model (as many languages as possible), that can faithfully produce the detailed IPA transcription given a word in a sentence in some language? I want to test a hypothesis for "uploading" neural network knowledge to a user's brain, by a reaction-speed game.
Espeak-ng. You don't need a neural network. Traditional NLP is far better at this task. The keyword you're looking for is "phoenemizer"
I'm surprised traditional NLP being better than ML models for this task, can you point me to a benchmark analysis pointing out that non-neural Espeak-ng is better than ML models?
Also, I asked for a neural model for another reason as well, I still want semantic knowledge present, I want more than pronunciation, but before I use myself as a test subject, I want to make sure I get the proper pronunciation in case the highly speculative "uploading game" works... I don't want to early systematically mis-train myself on pronunciation...
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But… why would I put the effort into getting an llm to make me an app when a there’s an existing app that I don’t have to maintain? I don’t want to have to make every app I use?
There's a huge difference between local apps that cost one time 3-10$ and apps that ask for a subscription between 5 to 20$ per month. the first category will remain and might become more popular as quality increases, the second category will be oblitereated as the value isn't there, even if all the buyers are rich. The second group takes up a much larger part of the pie than the first though, so apple's revenue will…
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Its more impressive when Apple does it because they are so far behind. I remember being excited when Apple got widgets because then I could add my 'Next Alarm time' to my home screen. Made my company work phone usable on trips. I wonder when they are going to get NVIDIA cards or CUDA? Then they can actually run LLMs and not just trick people into buying it under the 30 year old idea of 'Unified Memory'.
It's kinda funny that macOS supported CUDA when it was a tech demo, but then ideologically objects to it once it's a $3 trillion business. They've had to be dragged kicking and screaming away from the NPU model only to admit that GPGPU tech was the right choice.
'Cool demo' -> Doesnt convert to tangible things.
Wont attempt to compete with companies better than them, but go their own route. "oh look it consumes low power!" (Things no one cared about).
They are the Nintendo of tech.
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Wouldn't trust AI to run TODO, especially weak models. They can hallucinate tasks, forget to remind etc.
LLMs are stateless. But given an actual database of task-shaped items and some work, I could see the potential. With a canonical source of truth, and set input/output expectations, the potential blast radius is quite small.