andrewgleave

I like to think of technology as packaged-up knowledge intended to solve a particular problem.

The glib epigrams and aphorisms it shoehorns in to all prose is by far the worst regression of any model I can recall in terms of simply generating simple, clear output. I cannot think of another model that's gotten worse at writing plainly while being explicitly instructed to do so. Infuriating.

I've switched mostly to Sol and if I have to use Opus, the first task once the code is written is to ask Sol to strip and re-write (from the code as ref) all documentation Opus wrote.

Yes. The irritating epigram / aphorism style it now uses is such a regression compared to previous Anthropic models. Probably is the case that this is due to watermarking - though hardly subtle if it is.

My Lindy alarm has gone off!

Looks like MLX is not a supported backend in Ollama so the numbers for the Mac could be significantly higher in some cases.

It would be interesting to swap out Ollama for LM Studio and use their built-in MLX support and see the difference.

Theory building is the secret sauce, and all variants of "this is how to use AI effectively" I've seen are inferior to the epistemologically sound theory Naur outlines in his paper.

Feynman said, "The first principle is that you must not fool yourself - and you are the easiest person to fool" when talking about science, but it also applies to the properties of LLM output.

Yes. Juniors have a lack of knowledge about how to build coherent mental models of problems whose solution will ultimately be implemented in code, whereas seasoned engineers do.

Seniors can make this explicit to models and use them to automate "the code they would have written," whereas a junior doesn’t know what they would have written nor how they would have solved it absent a LLM.

Same applies to all fields: LLMs can be either huge leverage on top of existing knowledge or a crutch for a lack of understanding.