Raw LLM Responses

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Outsourcing your thinking to a chatbot is hazardous, partly because too many humans will then stop thinking, and even worse because LLM chatbots are horrendously bad at identifying everyday real-world risks. LLMs are deployed as a frozen model, so they can't learn. LLMs don't understand local context and don't ask for clarification. LLMs are stochastic, so doing it correctly one time is no guarantee of accuracy on similar future tasks. LLMs feel no fear of making a mistake, hence the stories of AI Agents deleting all files as the easiest solution to "tidying up", and then saying sorry afterward. The legal liability alone is a massive problem. It makes for amazing demos, but reliability is required for deployed systems. These weaknesses are deep in LLM architecture, and so it requires sophisticated external frameworks to try catch the problems when attempting to automate in real-life.
youtube AI Jobs 2026-03-22T09:2… ♥ 68
Coding Result
DimensionValue
Responsibilityuser
Reasoningdeontological
Policyliability
Emotionfear
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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