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rynn

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9 years into transformers and only a couple years into highly useful LLMs I think the jury is still out. It certainly seems possible that some day we'll have the equivalent of an EDR or firewall, as we do for viruses and network security.

Not perfect, but good enough that we continue to use the software and networks that are open enough that they require them.

What is keeping Google/Amazon/Microsoft from licensing Groq’s tech?

Nothing, but they likely can't implement it as well as they could had they bought Groq first.

gpt-5.2 codex isn't available in the API yet.

If you want to be picky they could've compared it against gpt-5 pro gpt-5.2 gpt-5.1 gpt-5.1-codex-max gpt-5.2 pro

all depending on when they ran benchmarks (unless, of course, they are simply copying OAI's marketing).

At some point it's enough to give OAI a fair shot and let OAI come out with their own PR, which they doubtlessly will.

What are you working on that you’ve had such great success with gpt-oss?

I didn’t try it long because I got frustrated waiting for it to spit out wrong answers.

But I’m open to trying again.

It will be like the rest of computing, some things will move to the edge and others stay on the cloud.

Best choice will depend on use cases.

I recognize this is a hard concept to understand for folks on this site, but the average joe signing up for a VPN doesn't even remotely understand what they are doing and why.

Really this is the answer to half of the comments on this thread.

Is there any real-life situation in which this matters, though?

You’d be shocked at the number of people in regulated industries that thinks a VPN inherently makes them more secure. If you think your traffic exits in the US and it exits in Canada — or really anywhere that isn’t the US — that can cause problems with compliance, and possibly data domicile promises made to clients and regulators.

At minimum, not being able to rely on the provider that you are routing your client’s data through is a big deal.

It’s easy to underestimate the amount of testing “just” prompt/context engineering takes to get above average results.

And then you need to see what variations work best with different models.

My POCs for personal AI projects take time to get this right. It’s not like the API calls are the hard portion of the software.

Azure AI Foundry says:

How was the model trained? The gpt-oss models were pretrained primarily using synthetic data along with some heavily filtered real code. The models were then post-trained using distillation (RLKD against o3/hailmary) and berry. For more details about the model training process, please refer to the documentation.

Meaning the author was spot on.

Current guidance is that AI-generated works are not copyrightable:

'If a work’s traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it 26 For example, when an AI technology receives solely a prompt 27 from a human and produces complex written, visual, or musical works in response, the “traditional elements of authorship” are determined and executed by the technology—not the human user. Based on the Office’s understanding of the generative AI technologies currently available, users do not exercise ultimate creative control over how such systems interpret prompts and generate material.'

https://copyright.gov/ai/ai_policy_guidance.pdf

There is some nuance to the document, but prompt-only works seem pretty clear.