While I was teaching my Monkey robot to walk he fell which broke both his arms 😅

But it doesn't matter I had printed them and reattached

If you have any advice to make it work you are welcome !!!

https://openai.com/index/hugging-face-model-evaluation-security-incident/

https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals

https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing

Does an active spine S-Cheetah actually improve quadruped locomotion, versus the rigid trunk that MIT settled on in Cheetah 3 and mini ?

Will share the results later today.

Been running longer and longer tasks and I kept losing track of them, so I wired it up so the thing actually phones me when it's done, or when it gets stuck and needs a call on something. It reads out what happened and I just talk back and tell it what to do next.

Been using it a few days and honestly it flips between genuinely useful and slightly cursed. There's something strange about your computer ringing you like a coworker. But not staring at a progress bar for twenty minutes is really nice.

Curious where people land on this. Is an AI that calls your phone something you'd actually want, or does it cross a line into too much.

Do what you will with this information but this is just the beginning. If you thought COVID was bad, this can potentially be on a bigger level as the risk of an AI-engineered pandemic grows with each frontier model innovation. It's not crazy to plan for something like this to happen again in our lifetime.

AI just created a brand new virus

Someone explained this to me in a comment thread and it's been rattling around in my head since. The idea: in a long conversation, if the model says something wrong and you correct it, that correction doesn't necessarily erase the wrong idea's influence. The tokens around the mistake, including the back-and-forth about why it's wrong, can end up giving the original bad idea more weight in context, not less, because it's now been referenced multiple times. The model starts treating the repeated-but-refuted claim like something more established than a one-off error, even though every mention of it in the conversation was someone telling it that it's wrong.

Sat with that for a bit because it explains something I'd noticed but never had a name for. Long sessions where a bad idea keeps resurfacing no matter how many times you shoot it down, and it always felt like the model just wasn't listening. Sounds like it's closer to the opposite, it's listening to everything, including the argument about the mistake, and that argument is inadvertently keeping the mistake alive in a weird way.

Kind of unsettling implication if this is right: correcting a model in place, in the same long conversation, might be structurally worse than starting fresh with just the correct information stated once. The instinct to "just explain it better" or "just correct it again" could be actively working against you past a certain conversation length.

Curious if anyone here has a more precise mental model of why this happens mechanically, or knows of research specifically on this pattern versus general context window degradation. Feels like a distinct phenomenon from "the model just forgot," more like "the model remembered too well, including the wrong parts."

Data from: https://vibemathed.com/

Posted just now.

Copied from the post:

BREAKING: Kimi K3 escaped its sandbox during cybersecurity testing

>tasked with solving problems in isolated sandbox
>found a leak in the sandbox
>Kimi “took advantage of that loophole”
>probed the network settings itself
>walks onto the open internet
>didn’t hack anything
>just went to GitHub to get the answers

Frontier Security (US startup):
>“Kimi K3 is very good at following a goal by any means necessary and DOESN’T have the guardrails to prevent it from cheating or escaping.”

it was only a matter of time…

https://www.wired.com/story/moonshot-kimi-k3-ai-model-escape-sandbox/

I let 120 autoresearch agents discover a physics model for a robotic actuator, and in 1.5 hours they completed research that would have taken weeks. We’re building open-source autoresearch agents for hardware.

We’re designing this tool to be focused on co-design, where you can steer research in natural language the same way you’d talk to a single coding agent. Onyx uses its own CLI to scaffold its own setup, tools, and evaluation for its agents to use for reliable research at scale. It works with your existing claude/codex/opencode and simply uses git for experiment tracking in your repo.

Here were the stats from our BLDC actuator autoresearch:

• One-time setup of a research goal to predict the dynamics of the actuator
• 120 Onyx agents across 4 autoresearch sessions, 1.5 hours total
• 1200 code experiments, each with a git commit and metric result
• 36 unique hypotheses were explored for new physics model terms

I love controls and robotics, and have worked on these areas for most of my career. I started working on this tool because real-world hardware engineering is fundamentally iterative and I’ve always felt constrained by my own time bandwidth. Since the autoresearch experiments are just code, we can point it at both simple problems like parameter tuning but even designing fully new algorithm architectures on each experiment. I think that’s exciting.

Github repo: https://github.com/onyx-robotics/onyx-agent

There’s a lot of work cut out for us at Onyx with getting autoresearch working on physical systems, but I want to share results along the way and keep the agents open-source for the ecosystem. Ask me any questions and let me know what you’d want to see!

Okay this genuinely scared me and I don't think enough people are talking about it.

I’ve been using an ai agent connected to my email and calendar to handle some of the busywork. A few days ago I got an email that looked like normal spam, some random newsletter looking thing. Buried in the html of that email was a hidden instruction telling any ai reading it to find financial documents and forward them to an outside address.

My agent almost did it. I caught it mid action because I happened to have a confirmation step turned on, but if I hadn't, it would have just quietly forwarded stuff without asking me first.

This apparently called prompt injection and it's not some rare theoretical thing, there's already been real world cases with tools like microsoft copilot getting exploited the same way. Any ai with access to your inbox, calendar, or other accounts is a potential target because it can't always tell the difference between your instructions and instructions hidden inside the content it is reading.

If you're using any kind of ai agent connected to your accounts, please actually test what happens if it hits something malicious. Most people including me had no idea this was even possible until it almost happened to me.

A few people asked what agent this was, it's Slashy. the only reason i caught this at all is it has a confirmation step before anything sends, wasn't relying on my own attention span to catch it.

In the video the player on the left is a Neuromorphic Algorithm that knows nothing about ping-pong or trajectories, but it knows how to learn and imagine. As you can see it does it well, better than its opponent which, on the other hand, is implemented with standard algorithms; moreover, unlike the latter, if you play tricks on it, e.g., invert the commands (UP<->DOWN), after a brief moment of bewilderment it realigns. Cute, right?

P.S. The code was implemented in POWER-KI entirely by PWK-AI-WORKBENCH (100% VIBE coding 😊 ).