ComputerGuru

Yes, here I'm referring to actual ECC, not the on-die ECC. All DDR5 chips have on-die ECC as a prerequisite just to reach error levels comparable to standard non-ECC DDR4, but from a hardware analysis perspective, even with the on-die ECC, DDR5 still needs ECC more than DDR4 (which already needed it).

Of course they disallow it. In saying they’re not thick enough to not realize how it looks to publicize their anti-distillation efforts. You must not have seen the tirades and screeds Anthropic posts in comparison, then.

It’s intentional product segmentation. Just like Intel not shipping ECC for client SKUs when DDR5 design makes it really necessary.

Read this last night. I was completely thrown off when I read this line:

> We expected the engine to return an error for any prompt it could not process, and our long-context tests returned successful responses, so we did not believe this to be an issue

Surely “we expected and error but got a successful response” merits investigating rather than moving on?

My interest was piqued and I tried to read on… Holy mother of all that is slop, I couldn’t even get to the text because of all the interactive slop I had to scroll through.

This is incorrect. You cannot identify the presence of text watermarks if you do not have access to the models so that you can spot where expected tokens diverge from actual tokens based off the weights. That does not preclude Pangram from being able to reliably identify LLM-generated text from models whose outputs they’ve trained their own detection model upon.

I feel like OpenAI hasn’t been as (publicly) hypocritical and petty about their distillation spats as Anthropic has been?

People in HN live in a bubble. Plenty of real world use cannot use subscription plans (not even talking about enterprise agentic use) and is absolutely at the mercy of the cost per task metric.

We agonize over cost per task to the extreme for services/functionality built atop of APIs and can absolutely guarantee you that it is the real world metric that counts most right after “is the model good enough for the task at hand,” and furthermore that, yes, in fact, it is quite often that we see certain models/providers easily using 3x or more tokens on average for the same tasks (or even the exact same task) than a supposedly similarly matched model from another provider. Once you become intimately familiar with the thinking traces from specific models, you can preempt this to some extent with manipulations to the system prompt (essentially heading off entire thinking branches) but the nuking of reasoning traces in the name of preventing against distillation attacks has made this harder.

Pretty sure my Samsung plasma TV from 2011 is safe. It was all downhill from there, just competing over which display looks better with its saturation and brightness boosted all the way up with the floor models all next to each other.

Sorry for the twitter, link; I couldn't find this reported elsewhere.

Apparently only supported under darwin and linux for arm64 and x64? At least via npm.

So you’re saying.. they are that bad, actually?