Raw LLM Responses

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One thing that the current systems lack that might be needed to truly automate white-collar work, is continual learning, i.e. learning new things that weren't there in the pre-training or reinforcement learning phase. The models are static. Sure, you can have some kind of knowledge-base and update it via your agentic scaffolding, have the LLM read parts of it, but this is much less like learning and more like reading through notes that someone else has written down, which is more limited. But why do we need continual learning? Well, the world can have randomness that you can't account for at training time. For example, when claude played Pokemon, it was struggling to find it's way through relatively simple caves, because it couldn't learn the layout (i.e. like a human making a mental map of it), so it would keep going in circles. I think a lot of jobs are like that, in a more abstract way. For example, a large codebase is kind of an abstract space that you create a mental map of over time & even the whole process of how things are handled in a company, is also like that. So, while AI will ofc make us do sub-tasks faster, I think that it will still require a more flexible human mind in the loop.
youtube AI Jobs 2026-02-24T15:5… ♥ 7
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-27T06:24:53.388235
Raw LLM Response
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