alt Hacker NewsI'm curious if the knowledge cutoff is important, when the interface (Gemini app) can search online for recent information. Is there a big advantage to having everything internal?
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You don't need everything internal, but having some idea of recent events is useful. If you ask it to implement some local AI there's a decent chance it will try to use qwen 2.5 without wondering if anything better came out since
Search grounding is expensive, you can't force the model to do it either. I use Gemini a lot and it often replies with out-dated data. The more detailed the information you're asking, the more likely it is to be wrong.
very important actually. just try to generate code for fresher frameworks/libraries. gemini sucks so bad in real work usage, everything it suggests are outdated and mostly useless.
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Not directly - but latest research advancements, cleaner / richer datasets, etc. still require fresh base models. Not everything can be fixed through post training alone (e.g. why GPT-5.5 "Spud" was such a big jump, and also why GPT-6 "Astra" is now supposedly another big leap). Ofc model size etc also plays a role, but my (admittedly limited) understanding is that new base models _can_ also lead to big jumps even keeping parameter counts constant.