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I think the problem is that the companies mostly don't make money, period. They may have better unit economics on underused subscriptions, but I don't see a wor

I think the problem is that the companies mostly don't make money, period. They may have better unit economics on underused subscriptions, but I don't see a world in which OAI/Anthropic don't heavily tighten the screws in the future. Right now it's silly to default to frontier models, but it won't bankrupt your company. I believe in the short-medium term future, we'll need to be more deliberate about model choices. In the long-term, of course, tech costs tend to plummet. Is there a future where in 15 years, my Apple Watch locally runs an Opus 4.8-class model? Maybe. And that would obviate this whole discussion.

hparadiz 4mo ago
[flagged]
- piva00 4mo ago
  You can save it to your favourites, no need to comment at all if it's not going to add to the conversation.
  - hparadiz 4mo ago
    Okay here is my adding to the conversation: The current discourse about LLMs in coding especially is based on the cheapest type of inference: text. This technology was designed for images which is a much more computationally expensive task than text. If it's already profitable to use this technology for multimedia like images and videos then using it on a text based inference for code is less then 1% as computationally expensive. Furthermore in the aggregate over time the computational expensive of text based inference precipitates negatively. In other words using it to write code will inevitably become a throwaway computational task like decompressing a jpeg. And yes decompressing jpegs would lag your 386 in the early 90s.
    - piva00 4mo ago
      What? LLMs were designed for text, it's in their name "large language model". Only with specialised encoders like vision transformers they were able to process images as well but you're absolutely wrong about the original design intent. In the end you just added misinformation, just save the comment to your favourites and set a reminder to check it again in a few years like you wanted.
      - hparadiz 4mo ago
        The first technological breakthroughs were with face and red eye detection in 2003. Then object detection between 2008-2012. Text models didn't become useful until about 2016. Please watch the first course of Dr Fei Fei Li's lectures on the subject.
        piva00 4mo ago
        If we want to keep tracing the lineage of AI we'll have to go all the way back to Markov chains from the 70s. You said LLMs were designed for images which is absolutely incorrect.

hparadiz 4mo ago

[flagged]

piva00 4mo ago
You can save it to your favourites, no need to comment at all if it's not going to add to the conversation.
- hparadiz 4mo ago
  Okay here is my adding to the conversation: The current discourse about LLMs in coding especially is based on the cheapest type of inference: text. This technology was designed for images which is a much more computationally expensive task than text. If it's already profitable to use this technology for multimedia like images and videos then using it on a text based inference for code is less then 1% as computationally expensive. Furthermore in the aggregate over time the computational expensive of text based inference precipitates negatively. In other words using it to write code will inevitably become a throwaway computational task like decompressing a jpeg. And yes decompressing jpegs would lag your 386 in the early 90s.
  - piva00 4mo ago
    What? LLMs were designed for text, it's in their name "large language model". Only with specialised encoders like vision transformers they were able to process images as well but you're absolutely wrong about the original design intent. In the end you just added misinformation, just save the comment to your favourites and set a reminder to check it again in a few years like you wanted.
    - hparadiz 4mo ago
      The first technological breakthroughs were with face and red eye detection in 2003. Then object detection between 2008-2012. Text models didn't become useful until about 2016. Please watch the first course of Dr Fei Fei Li's lectures on the subject.
      - piva00 4mo ago
        If we want to keep tracing the lineage of AI we'll have to go all the way back to Markov chains from the 70s. You said LLMs were designed for images which is absolutely incorrect.