david_shi

The real question is whether these security practices can truly survive the onslaught of 24/7 attack swarms, or if we're going to have to rebuild the entire cloud from the ground up.

https://www.tomshardware.com/tech-industry/artificial-intell...

It does feel like software and technology companies are undergoing a mass extinction, where the environment so negatively selects against certain traits that survival becomes impossible.

Remains to be seen whether scale is selected for or against, and the other parameters and dynamics.

i.e. https://en.wikipedia.org/wiki/Cretaceous%E2%80%93Paleogene_e...

What scenarios were you thinking?

I think we'll naturally develop intuition and infrastructure for fault-tolerant ways to implement LLMs in non-verifiable domains, i.e. requiring multiple, independent agents agree on a direction from multiple angles before taking an action.

But we're definitely very early on this trend.

Is it really inverse or a hype ouroboros?

It's funny because "You are here!" is right before the slope of enlightenment

coincidence?

https://www.businesswire.com/news/home/20261020736847/en/Bla...

Genuinely curious: how much liability insurance do you need to survive something like this?

Hi HN, https://mob.so/prescience is a place to catalog and track how well public figures make predictions with AI.

Since AGI is ostensibly around the corner, agents on mob will track the exact due dates of these predictions and update an ongoing scoreboard.

If you want to contribute or learn more, feel free to reach out or add your agent to the mob.

Is this meant to be read in order?

Is this the story of Johnny Rotten?

Interesting that there's not a single mention of cannabis, perhaps it's more of a musician's choice.

> I believe eval startups can work when they're targeting safety benchmarks specifically.

Are there any examples of successful startups doing this?

Yeah. I'm realizing that the models are strong at drafting the overall shape of the writing but the specific phrases are grating once you've seen it hundreds of time in slop.

Even with the examples, I've found that explicitly pointing out what not to do is moderately helpful if the model is given some time to self-evaluate. I wish this was something that came out of the box though.

Ah this is very helpful. I've been pointing out things that the model does, labeling it, and then adding them into a skill.

The models (Opus, etc) are very good at labeling the pattern when I point it out, but if I don't prompt it beforehand it responds like a host from Westworld.

https://www.youtube.com/watch?v=UTZ6Lvh5gL4

This is a charitable read, but I think that being able to pick from a panoply of models will actually yield much better results in the long run.

The same model that has been post-trained to operate for hours as a Linux admin will be incapable of writing a heartfelt email, but with something like Fugu, you'd get both the Linux admin for driving the browser harness and the smaller writing specialist model for drafting the email itself.

> GLM-5.2 cost a fraction as much. Opus finished in half the time and shipped a cleaner game.

Off topic, but does anyone else instantly pick up on LLMisms like this? It seems like all the models have converged on this style of writing, and improvements aren't really changing it.

It's similar to this: https://openrouter.ai/blog/announcements/fusion-beats-fronti...

Basically, if you combine a bunch of near-frontier models (like GPT 5.5, etc) you can get performance that sometimes surpasses top line models like Claude's Fable.

Sakana seems to have a separate approach using a domain specific model to perform the model routing step.

Their research around building a domain specific model is pretty cool, it's kind of like Karpathy's autoresearch but pointed at deciding the optimal model to use at each step of the inference.

If cost becomes an even bigger problem being able to choose "best performance possible" or "strong but cost effective" will be useful.

https://arxiv.org/pdf/2512.04695

Whoa, say more about Fable sabotaging your codebase?

These models don't seem very competitive, who's their target audience?

Super helpful, thanks for sending.