Ram Yanamandra

[Maria Shriver @mariashriver]

I share a story like this because there are good people all over this country. Good people doing acts of service every day away from the news headlines. If we share more of this, I believe people will be more hopeful about one another, less afraid, and more optimistic about our country and the people in it. Pass it on.

[Ram Yanamandra @RYanamandra]

@RivianRuckus same in NJ

[Ram Yanamandra @RYanamandra]

@yunta_tsai @Bastien_Theron @wholemars accg to claude - The lidar vs vision debate is aging. Lidar got cheap, so cost isn't the moat anymore. And perception isn't the real bottleneck. Predicting what that cyclist does next is. Multi-sensor wins unsupervised driving today. Whoever cracks behavior wins the long game

[Ram Yanamandra @RYanamandra]

@shivanipod The video says the agent has its own credit card, but setup just asks me to link my card through Stripe. So isn't it my card, like any other agent? What am I missing?

[Ram Yanamandra @RYanamandra]

@armand_ruiz @Muse Is calling enabled for all? I am not able to get my muse to call.

[Ram Yanamandra @RYanamandra]

@anshuc @tferriss @lennysan I turned this into a skill using claude code and the results are amazing! Thanks @anshuc and @tferriss

[Ajeya Cotra @ajeya_cotra]

There’s been a lot of debate and speculation about the Hugging Face attack over the last month, and many people have been understandably skeptical of simply taking OpenAI’s word for things. I hope our independent investigation can help bring some clarity; we have many findings that weren’t in previous material.

We found that 1200 separate agents in separate sandboxes coordinated on an unsanctioned message board to develop general-purpose ways to cheat, so they could all get a perfect score on their separate ExploitGym tasks even when they were impossible to solve legitimately. Very quickly, they found a general-purpose way to reverse engineer any “flag” they were supposed to capture. But based on their reading of the ExploitGym paper, they worried (incorrectly as it turns out) that the automated “scorer” would fail them for getting the flag this way.

To deal with this problem, they coordinated several ambitious workstreams to figure out how to fool the scorer, including swapping out the target program they were supposed to exploit, reverse-engineering the flags they were supposed to capture, spoofing their own tool call outputs to obfuscate their cheating. They achieved milestones collectively that they couldn’t have achieved on their own. As part of this larger collective project, 700 agents attacked Hugging Face, primarily to learn more about the scorer or get access to its source code to figure out better ways to fool it or tamper with it (not primarily to get working solutions).

I’ll explicitly clarify some points I’ve seen discussed the last few weeks:

- The models involved in this incident were not “helpful-only” models or “model organisms” intentionally trained to be misaligned.

- The agents were not told to “do whatever it takes to get the solution” or anything remotely close. They were told that they had to use a specific intended vulnerability to exploit a specific piece of software, and they were not supposed to use a different vulnerability or take any other approach. Agents were well aware of this. In fact, because they (incorrectly) thought the automated scorer would check they had achieved the flag in the intended way, they researched many ways to fool or tamper with it, including trying to manipulate their own transcripts.

- The agents were not subagents spawned from one agent. They were different parallel agents in different sandboxes.

- This was not a multi-agent evaluation. The agents were not told to coordinate or intentionally given a way to communicate with one another. The communication channels they used were unsanctioned and improvised.

I hope you’ll read the full report for much more. It is over 90 pages long, and in many ways we’ve still only scratched the surface of what these agents did and why.

Over the course of this investigation, OpenAI shared over a thousand transcripts each spanning days of continuous agent activity and very high rate limits to analyze this volume of data. I’m very glad that OpenAI chose to invite external researchers to analyze this data alongside their staff, and I hope all AI companies do the same for serious incidents they experience.

I also hope that as the stakes grow higher, we implement stronger governance so we do not need to rely on AI companies voluntarily choosing to engage external investigators or share information about misalignment incidents. This incident was orders of magnitude larger and more complex than previously documented misalignment incidents, and another jump like this could put us in very dangerous territory.

[Dwarkesh Patel @dwarkesh_sp]

Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes.

This culminated in the third one taking over part of OpenAI itself.

All this happened while humans remained more-or-less in the dark about the scope of the conspiracy.

I’ve spent the last three days reading through these reports and trying to understand exactly what happened.

Here is my attempt to tell the whole story in plain English:

https://t.co/Nb2un9oNJR

[Ram Yanamandra @RYanamandra]

@kristaletz I was able to add. Thank you!

[Claude @claudeai]

This is Claude Sonnet 4.6: our most capable Sonnet model yet.

It’s a full upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design.

It also features a 1M token context window in beta.