I remember when the US captured Venezuelan president Maduro, and when I posed a prompt related to this, the model said that’s pure fiction. I told it to double check. Still didn’t want to entertain the idea. It only acquiesced when I specifically directed it to check Reuters. I haven’t noticed this problem in months. Model cutoff seems to be less of a problem these days.

It still matters, but in the age of good reasoning, tool use, and web search, this is much less of a problem than it used to be.

For general purpose use this is interesting, but if I'm just using an LLM for coding, does this matter at all? I would hope something like a new java version after a model's publish date can be handled and understood by the model through tool calls and context even if it's not explicitly in the training data, the same way the LLM doesn't have my existing code or the plan to change it baked in from training.

I feel like with web search, exa and fetch built into harnesses this is no longer a problem. Haven't faced this issue in months.

Do people prefer the new flat style LLMs are producing? I don’t mind it as much as the gradient theme they were pumping out previously.

This is one of the things that bothers me about AI.

To me, intelligence or an intelligent entity should be able to learn from its mistakes and learn new things on its own. Having to start from scratch to teach an AI new facts or new skills is not very intelligent IMO.

Qwen-3.8 for me automatically set copyright footer on a website to 2025 and thought Astro 5.x is the latest version which first came out in December 2024

Pre-AI internet data is like pre-war steel

The slop would multiply if we keep feeding it to new models in a loop

I remember running the docker container for ollama and its knowledge cutoff is somewhere in 2023 still. That's unacceptable.

An aside - isn't it nice that have been fewer model releases the past week or so? A brief respite!

Depending on the use case certain models very well remain as or more reliable for certain tasks.

After Trump's last inauguration, ChatGPT would still tell me that Biden was President of the US. I understand that the training cutoff was before Biden dropped out. But it knew, or should have known, the current date and that there had been an election since its last update, but it didn't qualify the answer. When I asked it to search the web, it got it right. The moral I took away was to always ask for the search whenever I ask about current events. I do that so routinely that I wouldn't know if this problem has been fixed. I suppose that failing to update my priors per individual model release is a form of bigotry against a widely hated class.

Depending on the use case certain models

it is depending, i think.

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