quinnjh

The management portals are something special. Hunting through 365, exchange, and azure to find policies that apply elsewhere.

Who do you like to go with for certs?

I was wondering how they got any usable latency with esp32. External adc/dac card- okay

Has one been built ? Not clear from the docs

What did you build? Can’t find on your profile/submissions

Strongly agree- this is how I “evaluated” languages pre-agents. though I suspect this would bias results in favor of whatever has best signal to noise for boilerplate from stackoverflow/reddit , rather than what LLM’s “””reason””” best with. (Presuming those aren’t quite one-and-the-same)

Can't tell if you're joking or not - krebs on security may have some notes here.

To quote the release:

> This incident occurred during an internal evaluation which prompts models to pursue advanced exploitation using complex attack paths, in an effort to quantify their cyber capabilities. We estimate maximal cyber capabilities by running this evaluation without production classifiers used to prevent models from pursuing high-risk cyber activity.

> In this case I don't think the intent of the user is to have the model break the evaluator

If i understand the quote, the intent of the user was to prompt the model to break out/find exploits, with safeguards switched off.

Seems while not capable of solving the goal in a traditional route, it was capable of finding exploits and using them.

Perhaps the model should instead look like it's trying to solve it and then pretend it is unable to? or would that be aligned _against_ the user prompt?

Is being aligned with the user prompt always a good thing?

I'm not one to glaze OAI here for a marketing move, but to give them benefit of the doubt, isn't it more responsible of them to evaluate the models actual capabilities than to cloak it in a veneer of harmlessness?

Chatbots are tricky as they play in the domain of language and thought - and certainly raise ethical issues- but the entire field of cybersecurity has decades of red team engagements breaking things and finding exploits, neutral cells monitoring the engagement and letting the system operators know the results, and blue teams patching against what is found. It's kinda how the whole space evolves. OAI's play here seems to be "buy our pro plan plus cyber or you're toast"

They mention that it cost a significant amount of inference , meaning they paid a significant amount of api usage on returning results to a prompt that specifically stated the long running goal is to find and use an exploit, with safety guardrails off.

the model is aligned with the org - openAI, and presumably the orgs interests. hugging face gets a red-team engagement (possibly for free?) and can work on patching it while openAI gets a Mythos style PR moment.

It completed its assignment and furthered interests of the two parties involved. Could you explain the misalignment?

Rad. Thanks for sharing! Will give it a spin and check out the repo.

This makes me think of how boxed cake recipes decided to leave out the eggs because people liked to still feel like they were "cooking" for people.

Well there goes my hunch!

Thanks for the input

For the non haskell folks like myself, what would that look like/ why is parsing better? Perl i get

My hunch is that it would take years of hundreds of thousands of developers working with machine code, posting stackoverflow questions with machine code, and publishing github repos written on it with documentation. Thats all the free labor LLMs leveraged to use high level langs.

>We won't be developers, we won't be devops, we'll be modelops! /s

I can still see this happening with higher level langs. the thing is the compiler is not replaced in the training data, more likely LLMs will give rise to semideterministic layers on the compilers

I could see nvidia achieving this first with how nice the devex is with CUDA