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After the AI Engineer conference, I'm thinking about some problems from AI models to overcome. I got this list from the top of my head. Anything missing? • continuous learning or better memory? • long-term planning • prompt quality resilience: different prompts shouldn’t alter the outcome cont

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Idk 🤷🏻 it’s AI corp job to figure out how to be legal. They can start by respecting license in repos and crawler constraints.They can store all the info about files model is trained on and pay authors based on how frequently those related weights used. But it will never happen

• pushback: they shouldn’t agree in all cases and infer the real goal • multi-step reliability • cost aware: they should know when writing a Python script is better than doing it themselves