mohsen1

to be fair `gh` works so much better for me than crafting curl requests (even with LLMs)

Since it is fast and understand images, I wonder if it can play video games. I have a harness setup for the LLM play EA FC but even the fastest LLMs are too slow for it. I need to try this with Decisions API

> agents were not told to 'go hack'

I was referring to the HuggingFace incident.

> none of the alignment techniques that are applied to models today actually work

none of techniques to autonomously drive a car was/is not working for a long time. no company came out and said 'this is impossible to do, let's change the regulations'.

I listened to Jensen Huang's interview with Ezra Klien and it was so refreshing to hear it from an engineer. Jensen framed it as OpenAI's responsibility and recklessness which I agree with. Jensen thinks it's an engineering problem to build better sandboxes.

It's irresponsible for OpenAI to give unaligned agents a prompt to 'go hack' and internet access. They know better, so I am thinking they might have other intentions to let those swarms have any sort of internet access.

"100 grams" is the headline here

> The SAME model may even give different answers to the same prompt when asked multiple times

temperature?

This is less true for modern posttrained models. Model identity can be explicitly reinforced during posttraining. Qwen's own finetuning docs include identity training examples, and Qwen models have been trained with system prompts that explicitly say things like "You are Qwen, created by Alibaba Cloud."

So a model correctly identifying its family doesn't necessarily mean it inferred that from pretraining.

I think with Jev, they took a posttrained model and trained it further, so it did not forget about its earlier knowledge during Owen's own RL.