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.

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piva00 6 hours ago | parent [-]

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.

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hparadiz 6 hours ago | parent [-]

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 4 hours ago \| parent [-]
		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.