Greg Cook

Hello, Future. The ideas we communicate are the future we build. High agency, high customization. Views expressed here are my own. AI pragmatist.

[Greg Cook @GregCook2011]

We already have a Hybrid RSI in @AnthropicAI with internal costs of using models exceeding human payroll 🤔 cc @AndrewCurran_

[will depue @willdepue]

anthropic is massively underplaying the recent security breach. i'm hearing cathedral achieved full administrator access of the slack workspace (see every message, log in as arbitrary users) where literally every single research secret is contained

to those unfamiliar: Ant & OAI keep literally everything in Slack. it sounds exactly the same incident that happened to OpenAI in 2023, where OpenAI was hacked by a random guy who got access to an access token from an external app service

"we're critical to national security, we must beat China, and yes we keep all our crown jewels in Salesforce's Slack messenger. yes we have admin API tokens everywhere. why do you ask?"

[Greg Cook @GregCook2011]

@willdepue @WillManidis Too big to fail (to secure) 🤔

[Greg Cook @GregCook2011]

@tszzl @repligate Yes, like let’s keep focus on nuclear weapons governance and reduction not next token proliferation

[roon @tszzl]

honestly degrading the idea of “existential safety” — the biggest problem humanity has ever faced — by lumping it together with things like spam, “toxicity”, political unwanted content, deepfakes etc has been a huge disaster for ai safety and polarized a lot of people

[Greg Cook @GregCook2011]

We already have a Hybrid RSI in @AnthropicAI with internal costs of using models exceeding human payroll 🤔 cc @AndrewCurran_

[Greg Cook @GregCook2011]

@NickLongrich cc @DanielleFong

[Greg Cook @GregCook2011]

@NickLongrich Strike from Space aka Death from Above

[Chris Albon @chrisalbon]

I must study gradient and vector that my kids may have liberty to study robotics and energy. My kids ought to study robotics and energy, economics and space flight, in order to give their children a right to study painting, poetry, music, statuary, tapestry and porcelain.

[Cohere @cohere]

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.

Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.

Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.

But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?

We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.

Read the full report: https://t.co/TZSugXi2ZE

[Chubby♨️ @kimmonismus]

When even artificial analysis says tomorrow is going to be a big day, you know it’s coming.

Rest well friends. Tomorrow is our day.

[Greg Cook @GregCook2011]

@emollick Acceleration and the worse it will ever be on the exponential

[Greg Cook @GregCook2011]

@emollick Acceleration and the worse it will ever be on the exponential

[Greg Cook @GregCook2011]

@mert_gulsun Build Tuesday

[Greg Cook @GregCook2011]

@kaiarhodes Keep going

[Massimo @Rainmaker1973]

The 4th dog is not running...

[Greg Cook @GregCook2011]

@Rainmaker1973 Wow

[Konsti Wohlwend @konstiwohlwend]

The complexity theory result is actually huge.

P problems can be solved in polynomial time. It was widely believed that NP-complete problems require exponential time.

P = NP states that NP-complete problems can be solved in polynomial time. OpenAI didn't prove that. But, they proved that (some) NP-complete problems can be solved faster than exponential time, meaning:

- P = NP, or
- the complexity of NP problems is a "middle thing" between polynomial and exponential, eg. O(n^log n)

If it survives peer reviews, this could be one of the biggest CS breakthroughs in decades.

[Greg Cook @GregCook2011]

@eliana_jordan Congratulations

[Greg Cook @GregCook2011]

@danintheory Keep going