You still need to learn the names of models, understand their use cases, concepts like MoE, then you have different architectures like diffusion vs transformers, agents etc.
And then you have GenAI like flux and all the open source projects.
I think it's beneficial to get all of that and then keeping an eye on it to catch the moment when it becomes relevant for you and not being surprised and too late.
> You still need to learn the names of models, understand their use cases, concepts like MoE, then you have different architectures like diffusion vs transformers, agents etc
Why? When you think you might need something just search for it. There are too many models with incremental improvements
If you want to see how close to a non-ordinal 123456 a random generator can get, you also need to look for stuff like 923456 or 123956, etc.
Also, would 223456 be considered a closer match compared to 323456? (It shouldn't in my opinion because, again, these are non-ordinal strings).