> It is possible to amplify a model - for example ensembling a bunch of predictions, using reversible tasks like translation to check if you get back to the ori

> It is possible to amplify a model - for example ensembling a bunch of predictions, using reversible tasks like translation to check if you get back to the original, applying code execution and math verification, using a search engine or using narrow models. Basically augmented LLMs are smarter than plain LLMs. Yes, these are great extensions. It's part of where I like thinking of these things as human as they are all good answers to "how would you do X?" I'd search for answers, I'd run code and see what the error message was, etc. I'd talk through problems, try and list things too. It's not a perfect analogy, but I think people do things like ask them to effectively whiteboard a solution then complain that some API doesn't exist - sure that's not the preferred outcome but it's not how we expect even good devs to write code. We explicitly complain about times we're forced to do that in interviews. I'm investigating giving them access to autocomplete while they're writing code, I need to learn how to write a client for a language server though.