We built cybersecurity monitors for Kimi K3 and GLM 5.3 — more accurate, 50x faster, & 50x cheaper than an optimized LLM judge. External red-teaming from @farairesearch found that our monitor greatly reduces universal jailbreaks. 🧵

Often I meet seasoned engineers who want to pivot into AI safety *research*. I think this is a mistake: safety is now a production problem and the world needs great engineers more than ever. I wrote a short post expanding on this:

LLMs are like Schrödinger’s cat: many possible trajectories, but you only see one outcome per run. To really understand models, and debug where they go wrong, you can find the "forking tokens" that lead to different trajectories. Our new research does this 100x more efficiently!

You don’t need to stay to do good research: work that aims to scale to much more intelligent systems can almost always be done on publicly-available models. (And nobody can think creatively in such pressure cookers.) You don’t need to stay to gain influence, because you’re almost definitely too scared to wield that influence in ways which matter (see the tweet on public criticism above). You don’t need to stay to generate scary demos or warn policymakers: we will be swimming in warning shots before long. You don’t need to stay to advance AI capabilities, even if you’re worried about the latest bogeyman your CEO has constructed: “we need to do it to beat the bad guys” is a big part of what makes people the bad guys. You don’t need to be there to implement guardrails or other short-term mitigations: there are plenty of bright young things eager to slot into your place in the machine. You don’t even need to be there to feel rich and powerful. For one, you’re probably already rich (and there’s plenty of money up for grabs elsewhere). But more importantly, the sense of power is almost entirely illusory. Everyone involved has locked themselves into a mindset where they’re incapable of making real choices, except for how fast to barrel down the train tracks that their fear has laid out for them. (More on how that happens in my QT below.) The world is more open-ended and malleable than it’s ever been. A single person who’s thinking clearly and creatively could build things that have never yet been imagined. Or they could just keep spinning in place. At this point AGI company employees owe it to both the world and to themselves to do something better.

Silico managed the complexity of multi-cluster training here, with the reward model running on our cluster and the rollouts and training steps running on Tinker. It even benchmarked the perf and reported this, alongside the code, before launching the big run.

Me: generate a novel 76-residue protein binder BoltzGen: best I can do is ubiquitin. I did a quick analysis of memorization in protein generations using @GoodfireAI’s Silico (now publicly available!). Silico makes it easier to investigate and debug model issues like this 🧵

Silico, the platform for ambitious AI research, is publicly available today. AI is advancing fast. The tools to understand it need to advance even faster. Silico lets you interpret and train your models at frontier scale. Learn more + get access 🧵

> replicate J-space on GLM 5.2 > train a reward model and run RL to reduce hallucinations > show me how this model makes cancer predictions Using our platform Silico is like having a team of AI researchers ready to run experiments like these. Private beta is open now. 🧵 (1/6)