After dissing Anthropic for limiting Mythos, OpenAI restricts access to Cyber

There's a story to tell in that: 1) Google has a transformer-based AI that hallucinates too much to release 2) OpenAI replicates the tech then YOLOs it 3) Everyone says: look how Google is getting left behind! Google thinks: the second mouse gets the cheese. 4) Google gets the cheese, OpenAI is absorbed by Microsoft or just disappears (or both).

China’s DeepSeek prices new V4 AI model at 97% below OpenAI’s GPT-5.5

Did somebody say that Elon is stealthly funding: Seven lawsuits filed against OpenAI by families of Canada mass-shooting victims

As always, when the going get's tough, the tough ultimately resort to lawsuits.

think about it in the form of who can pay. theyre at b2b. and swiftly moving to government.

People keep on mentioning gpt2, but it's worth recalling that back in 2019 it was basically the first model that was capable of zero-shot generation of coherent multi-paragraph text. Having it write security exploits like Mythos wasn't even on the radar. Rather, the concerns were about misuse and societal implications, which in retrospect were pretty prescient: https://openai.com/index/gpt-2-6-month-follow-up/

It's just "thinking past the sale" which they've been doing forever.

i.e. "I'm so worried that our capped for-profit structure will limit your returns when we make over 1 Trillion in profit".

Given the USG has asked Anthropic not to release Mythos I'd wager it's more than a marketing stunt.

I doubt it. By not releasing it, Chinese companies will be unable to break TOS and use it to acquire high quality training data...which, I suspect, is how they've kept pace

If deepseek is anything to go by they are still significantly behind.

Ominous phrasing.

Good but not necessarily better that was is already pay-as-you-go available today. ref. https://www.flyingpenguin.com/the-boy-that-cried-mythos-veri...

This AISLE benchmark is interesting in this matter: https://aisle.com/blog/ai-cybersecurity-after-mythos-the-jag...

And the recently discovered Copy Fail by Xint code is another proof that the gating is overblown: https://xint.io/blog/copy-fail-linux-distributions

> demonstrably

I'm not entirely up to date on each week's LLM hype train/scandal but last I heard there was no public access to it or public-trusted 3rd parties that can review model's capabilities

You'll have to define what you mean by AGI

No, because AGI is a fantasy.

Has there been a single positive post about Altman?

You missed literally every single post/article about the guy?

Altman played no small part in the current price of RAM. He told everyone he would buy 40% of all the RAM, causing shortages and a huge increase in price, just to take it back a few months later. So yeah, he is a bad guy now.

People don't become bad guys just because they lie. The consequences of their actions (and their lies) matter more. Take Elon Musk for instance, he has always been a recognized liar, even when he was a good guy. What changed? Before, he was famous for making the electric car people actually wanted to drive, and cool rockets. Then came the politics: supporting the party most of his fans disliked, being responsible for many government job losses, in particular in the field of environmental preservation (ironic for a supporter of "green" energy), etc...

> charge gullible companies $$$$$

The following companies are participating in Project Glasswing (to get out in front what vulnerabilities Mythos is able to find and exploit at scale):

AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft, NVIDIA, Palo Alto Networks.

Do you think they are all in that gullible category?

https://www.anthropic.com/glasswing

And government contracts.

They are selling an impossible product.

If you make an LLM more safe, you are going to shift the weight for defensive actions as well.

There’s no physical way to assign weights to have one and not the other.

Sounds like most corporate deep dives I've attended tbh

I've been experimenting with running a few models for local inference, some of them get "stuck" in a repeat loop of trying the same thing endlessly, its weird. Others are really good. If they can ever handle about 400k tokens (maybe less, but from experience with Claude after the 1 million token increase this seemed to be a good sweet spot) without going batcrap crazy I'll be impressed, mostly because I would like them to read more of the codebase instead of just making assumptions. Although I've been building a custom harness, and I'm just about to start working on the tool building features for the harness. I already have a system similar to what Beads does but I didn't like some things about Beads so I made my own to track tasks, so context window doesnt need to be super massive for task tracking.

Gemma4:e4b is crazy good and quite usable on 10 years old midrange hardware.

Not sure about the security capabilities and haven't tested it all that well, as I usually just use hosted models, but I do find myself using it and it's been quite successful for parsing unstructured data, writing small focused scripts and translations.

The fact that I retain control of the data itself makes it incredibly useful, as I work in an environment where I can't just paste internal stuff into Codex.

But since it's run locally on a toaster testing it is out of scope for me. It takes a fairly long time to do anything.

Local models are 6-12 months behind the “frontier” models. This mean anthropic, openai, and google don’t have a moat, they’re on a treadmill running to stay ahead. Treadmills don’t justify their valuation.