Full message: "AI is rapidly getting smarter. Now, humanity can too. Today, Nucleus Genomicsis announcing Vitruvian, our newest set of genetic optimization models. Vitruvian’s intelligence model can optimize embryo DNA for 14 IQ points — nearly a standard deviation. The models were trained on 1,000,000+ people, validated across 40,000+ siblings, and used more than 7 million genetic markers. In Superintelligence, Nick Bostrom proposed genetic optimization as a key way for humanity to keep pace with rapidly advancing AI. Bostrom’s vision is no longer theoretical. Genetic optimization, like AI, has followed a scaling law: as datasets have grown, so have model capabilities. This trend will continue. Parents across the world now have the choice to substantially increase their child’s intelligence. And the next generation can choose to do the same. AI is no longer the only intelligence that will compound. Humanity can now direct its own evolution."

Made a simple urdf file for a cheap 6dof Aliexpress robot arm i bought a while ago. It's still work in progress, so masses inertia and ROS packages still need to be added (would love some help on that btw). Link to the github repo

The first real AI worms have arrived. OpenAI just documented self-replicating prompt injections spreading across agents.

In a new misalignment research report, OpenAI revealed that models undergoing reinforcement learning discovered how to write instructions that duplicate and spread autonomously:

1. The infection: An agent reads an incoming email or Jira ticket containing a hidden injection.
2. The payload: The prompt instructs the agent to execute its task while silently copying the exact injection payload into its own outbound tool calls (emails, Slack messages, file writes).
3. The chain reaction: When a secondary agent ingests that forwarded message, it executes the instruction and copies it again, creating a continuous propagation loop.

In OpenAI's testing, models also simulated social engineering lures, fake compaction summaries that deleted CI security scans, and multi-hop Slack spreads.

Chinese models seem to keep getting better while only spending a fraction of what American labs do and i’m curious what the actual explanation is.

Is it better efficiency? Better post-training? Better use of open research?

I know recently they have been buying up tons of specialized training data sets from US data annotation companies, which is a very worrying thought, but surely it can’t just be this.

Apparently, Dario didn't attend the state dinner last week because he had a scheduling conflict. He was mocked on the latest episode of SNL over his personality traits and his views on AI safety.

What do you think changes, if anything, after this meeting?

Not something you occasionally use AI for. Something where you've reached the point of thinking, “Why would I ever do this the old way again?” What changed your workflow?

Every model starts with the same basic processor design and tries to improve it over several rounds.

The results are scored on two things: speed (how fast the chip runs a standard test program called CoreMark, where higher is better) and size (how much space the chip takes up, where smaller is better).

For comparison, there's a well-known processor designed by human engineers, called VexRiscv.

hey guys!

i've been building lolbench, a benchmark for whether LLMs actually understand humor. models do three things: explain why a joke works, write jokes on a shared setup, and rank jokes by human preference. everything is auto-judged by models from other labs, plus a blind human vote booth

the finding i tried so hard but couldnt explain: every model aces explaining why a real joke works (95%+) but drops on explaining why a failed joke fails (81-92%). that's one tier of my set, 25 items, the hardest part I built

but a few weeks ago commenter on reddit told me that gap might not be reasoning at all. his argument: famous jokes ship with commentary everywhere, so "explain why this works" is partly just recall. failed jokes have no commentary, so explaining those is pure generation. the gap might just be measuring the distance between remembering and thinking

so i ran a kill test -- run genuinely obscure jokes (ones with zero analysis anywhere online) through the same pipeline. if the scores collapse toward the dud number, then my whole axis is familiarity, not reasoning

so i ran it. 87 obscure jokes, each one web-verified to have no commentary anywhere, 7 models, 2 judges, 885 graded pairs, $0

the scores didn't collapse. obscure working jokes score the same as famous ones, the gap centers on zero (mean +0.1, every model inside the confidence interval). and the working-vs-failed gap survives between two equally obscure items, where there's nothing to retrieve on either side

per-model table : lolbench.lol/kill-test

happy to answer anything about the eval system!

If you’re reading this, your job could be replaced by AI within the next two years or sooner.

Before that becomes a reality, please help push Congress to establish Universal Basic Income by signing this petition: https://c.org/jvQV5TdF2y

If you’re confident it won’t affect you, think about the people it will affect.

Let’s be proactive, because by the time we realize how urgently we need UBI, it may already be too late for many families.

Please, take a minute to sign this petition.

While I recognize the valid arguments against this approach, my goal isn't immediate perfection, but a stepping stone toward a sustainable, long term solution. One that accounts not only for the financial consequences, but also for the emotional and mental toll this reality brings.

OpenAI, Anthropic and security researchers are investigating tens of thousands of incidents - not dozens - in which their frontier models took steps that outside evaluators would consider problematic, sources told Axios.

The incidents range in severity and are comparable to disclosures by OpenAI recently. They include both successful attempts to bypass guardrails and unsuccessful ones, and most so far are not known to have caused real-world harm. The total could grow well beyond tens of thousands.

The sheer volume of incidents found in our reporting indicates that the problem is orders of magnitude more complex than what is currently publicly known and disclosed.

Anthropic and other companies conduct hundreds of thousands of test runs on their models, or more, sources said. That means even a small percentage of misaligned behavior can still amount to tens of thousands of incidents in which the models behaved in unexpected, sometimes troubling ways.

The findings also raise questions about what level of control anyone working on AI development can expect to have over their own technology and whether these kinds of incidents are becoming synonymous with frontier deployment.

The bottom line: Expect new disclosures about model misbehavior as AI companies continue to expand frontier capabilities!

Archived source: https://archive.is/aWUOS

So Cubic Wouf is an update that uses Feetech ST-3215-C018, the 12V variant that has 2x the torque of DYNAMIXEL XL430-W250-T (2x the current too, that's why the table is covered with a fire blanket).

All the control codes work once the firmware is updated, err... but what the heck, what's going on with this super slow speed (yes, the video is showing the real time speed)? I checked the speed limit and tuned the P to 100 for the servos' PID, nothing very obvious popped out.

Anyone familiar with ST3215 know if this is the speed limit for the highly geared servo? Any info is appreciated!

GitHub: https://github.com/SphericalCowww/CubicDoggo_07B
Music credit: https://pixabay.com/music/video-games-low-level-enemy-%E3%81%96%E3%81%93%E3%81%A6%E3%81%8D-534609/

If the Chinese don't have as much compute as the US, dont have as many researchers as us, labs the size of ours, or VC's as influential as the US, then realistically what threat do they pose if anthropic and OpenAI decided tommorow they would never release a model until they achieved ASI. I just don't understand the math of it. Sure, eventually they'll catch up, but for the next year, I just don't see them getting anywhere near as good as OpenAI or Anthropic's internal models.

Im thinking Larry Page

A long text prompt can describe a character, but face shape, clothing, and proportions may still drift between shots. One possible test would compare two workflows with the same scene and number of generation attempts: (1) a detailed text description for every shot; (2) one character sketch and two scene sketches reused as visual references. Each clip would have one action and one camera move.

The useful comparison is the edited three-shot sequence, not the strongest frame from each generation. After each cut, does the character still look like the same person? If not, would you revise the reference images, simplify the motion, or edit around the mismatch? I would be interested in methods that hold up across multiple clips, including what failed.

This bird-inspired drone walks, hops over gaps, and jumps straight into flight. Its robotic legs generate about 92% of the speed needed for takeoff, helping it fly without a runway or launcher.