There is this idea of self-learning in AI. Am I smarter today than I was yesterday by learning from yesterday's mistakes? Does anyone have any practical suggestions on this self-learning? I am seeing this tension. If the instruction is non-deterministic, then there are issues like prompt hacking. Besides the vulnerability aspect, there have also been numerous cases of AI agents not doing specifically what was asked. This "may or may not happen" aspect to any plain English instruction given to an AI agent. It's a request not a command. The way to solve this is to convert the instruction from plain English to some coding language and run that piece of code. Usually python works. But moving to python kills the self-improvement loop. All self-improvement options I have seen (SkillOpt for example) work on improving instructions in plain English. A request can improve over time but a command is frozen in the time it was created. Wonder how people see this tension of being more efficient today (python code) vs being smarter tomorrow (Prose in a skill)?

Anyone tried grok-bot?

Hi everyone, I'm having a career conversation with my manager in a few weeks. The point of the conversation is to discuss the direction I want to take my career as I continue to work at the company. At the moment, I am working as a data engineer but I want to pivot more towards the AI and ML space. I want to focus more on Full-Stack AI Engineering and in the future, AI Architecture. I have also been interested in MLOps but don't have much experience in the cloud or Kubernetes yet so I don't know if it makes sense to bring that up. Anyway, I was wondering if anyone has made similar transitions and if you would be open to sharing any tips or suggestions to how I should steer the conversation with my manager so that it is productive and effective.