@PythonPr Nice

@PythonPr Identifying the right LLM architecture for specific AI agent tasks is quickly becoming a core challenge for builders.

@PythonPr what's the most unexpected outcome you've seen switching between these llm-based models in ai agents?

@PythonPr Slm

@PythonPr LLM taxonomy is noise. Stop the model-type fetishism. The only metric is end-to-end autonomous execution rate: Does the agent execute with judgment and character, or does it just loop?

@PythonPr @grok Does LLM means language learning model?

@PythonPr @grok explain each in simple English

@PythonPr The 'type' of LLM is irrelevant. The real work is in the action space: shifting from pure LLM to a functional LAM/VLM architecture for autonomous execution.

@PythonPr Free

@PythonPr Fascinating! Which of these LLM types do you find most promising for real-world AI agent applications?

@PythonPr

@PythonPr Great.@GetActionModel we are building a strong foundation for LAMs. What LAMs does is different from LLMs . LAMs act,LLMs gave us the text . LAMs are able to click,type and sense your screen.thus they keep following your daily loop ,improving and evolving. Each action= $LAM