But those technologies are layers, and there are reliable things that sometimes bubble across the boundaries — type hints, better code patterns to trigger compiler optimisation, interesting tricks with key column selection — and someone with expertise from that layer below can explain why, and their advice will always work in situations that are sufficiently similar.
You are right about AI personalities. Obvious even with the open weights models. Gemma and Qwen write code and documentation like people from different cultures. Because I guess they are a bit like that.
All "personality traits" within an LLM are entangled. So when you mid-train or post-train on ESL texts, or run RLHF using people from a given culture, you risk bleeding some of the related cultural traits into the LLM itself. A lot of the resulting "personality" is downstream from different AI teams picking different datasets and training signals.
RLAF is more of a "funhouse mirror" distortion - it takes existing traits and twists them, sometimes amplifies them to comical extremes. Weird can become weirder. A verbal tic can become a style signature. Part of the reason why AI writing from GPT-4 era and to now has changed so dramatically.