Which makes sense, because of you have looked into search and crawlers you notice that search is actual quite expensive (which is why e.g. Kagi charges a few bucks for search every month).

I wonder how much that extends to using LLMs for programming. I assume most knowledge of programming language syntax still comes from training data.

Were you expecting your model to be updated on current events? Why?

Also the specific event you are referring to is a statistically very improbable event, prior to its actually happening.

>It only acquiesced when I specifically directed it to check Reuters.

Do all models do this? They check in with Reuters? Why would a model think that you asking about an extremely improbable event warranted reaching out to Reuters?

if you point it to the web it got the point, but started treating everything like fiction. so it simply started making up possible scenarios and playing them off as real answers when asked for factual information.

i could not tell what the issue was or how to fix it because the reasoning is encrypted. the obfuscation model spat out something like: 'the user is asking for details about a fictional scenario in which the usa has assassinated the leader of iran'

i really don't like the way big ai companies are going. encrypted thinking, guardrails, adversarial personality, moralizing. it is creating something anti-human.

You’re not going to get all that just by pointing the LLM to the recently released Java 27 documentation. That information is also potentially adding tons of content to your context, which is already filled with tons of other data (your code, other recently released libraries it has to get documentation for, etc).

I’m going to add this site to my agents files so that they’re explicitly aware of their own limitations.