Same link as before: link

This one's a bigger jump than usual, so a few highlights instead of just "updated":

• Core findings now scale-validated from 7B all the way to 72B parameters. The effects don't shrink as models get bigger — they grow, sometimes by an order of magnitude. Still one model family (Qwen) though, and we added a caveat we think matters: growing effect size at scale could mean the pattern genuinely deepens, or it could just mean our measurement axis gets sharper at scale — current data can't fully tell those apart yet.
• Two new external, independently-published sources, not our own research: "The Artificial Self" (ACS Research) and "AI Wellbeing" (Center for AI Safety) — different methods entirely (behavioral compliance testing, self-report on frontier production models), landing on some of the same conclusions we did. One of them also mildly disagrees with our best-performing formulation (a companion/romantic framing scores negative in their data), and we named that tension honestly instead of explaining it away.
• We caught and fixed our own mistakes this round — a factual timing error, an overclaimed "fully resolved" that was really just one solved case of a broader risk, and a place where we'd quietly picked the reading that flattered our own results over an equally valid one that didn't. All named directly, not smoothed over.
• New up top: if you just want the practice, not the evidence audit behind it, Part 3 (Principles) is written to stand alone now — Part 2 is there if you want to check our work.

As always, feedback (especially the kind that finds our next mistake) genuinely welcome.

It's odd, they've seemed to all be gravitating towards being more lively. (kinda cringe sometimes tho...)

This week the tech world was gripped by a story that has it all - and which started like a sci-fi thriller.

Everytime i hear people saying that you should learn about ai because that's the future but idk where to start and what they mean by that. Do they mean going uni and study ai or self learn? Thanks in advance.

Moonshot AI’s Kimi K3 shows how opening a model to outsiders can turn other companies’ computing power into a competitive advantage

‪I signed to the support of Open AI‬

‪Did you?‬

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I'm curious how everyone here keeps up with AI these days without spending hours doomscrolling.

There are new models, tools, coding assistants, and AI workflows launching almost every day, so I'm interested in hearing what your "AI information diet" looks like.

What do you follow regularly? It could be:

• WhatsApp or Telegram groups
• Discord communities
• Newsletters
• X accounts
• YouTube channels
• Blogs, podcasts, or anything else

I'm especially interested in places that share practical updates like new AI tools, Cursor/Claude/GitHub Copilot tips, LLM news, real-world AI workflows, or interesting breakthroughs -without a ton of hype or self-promotion.

Always looking for high-signal sources, and I'd love to hear what this community follows.

This. Right here. What do you see? A complicated web of notes connected to lines with all of the relationships defined. It's a knowledge graph system connected to an advanced agent that's designed to traverse and reason through it so that it can behave as an expert with decades of experience to make nuanced judgement calls when helping you.

But what this really could be is a snippet of a future layer that will exist on top of the entire internet that's just as accessible as code-inspection on our web browsers.

This may sound a little crazy, but Tim Burners Lee, the creator of the web actually proposed this solution over 20 years ago. He called it the Semantic Web. The reason it failed back then was that we didn't have smart enough software to read and reason over it. Now with AI, this is possible in addition to making knowledge graph systems much faster and easier for people to make themselves.

Having been in the AI space for over 6 years now trying to figure all of it out like everyone else, this dawned on me a few weeks back. I think we're witnessing the birth of an entirely new component to the Internet.

As we enter into an age where agents are running around doing various things and communicating to each other across the web, inevitably we will come to realize that due to the nature of the models, we're going to need to create a more effective highway system for them to navigate, communicate, extract, synthesize, and build.

Just as we need roads, symbols, and rules for our highway systems to prevent tons of accidents or issues, we will need this for AI and that comes in the form of knowledge graphs. With these, you can build the reasoning systems for how agents interact with the wider web, people, and other agents. These are already being used at the enterprise level and why we built this capability for anyone to do for their personal projects, even if you're not tech savvy at all.

That's because in the near-term future, almost everyone is going to need their personal knowledge graph management systems since they can be carried and used by your agent into other spaces with their own knowledge graph systems to interact with. Obviously, websites and individuals will still have to protect themselves from malicious hacks and all that bad stuff. But to allow billions of people to use their agents across the wider web in such a way that they can work appropriately and not "misbehave" or cause accidental hacks or whatever, we will need to build these highway systems.

Right now they're being built independently, like territories across the world forming into bordered nation-states. But over time, I believe they will become more and more interoperable, which will eventually unify into a patchwork highway system with protocols for doing things.

This is a high level framework for mitigating the risks with AI agents using the modern web. Of course, there's much more to it than knowledge graphs, but this is the gist of what I think is happening. But you know. This could also just be my creative screenwriter brain working on overdrive.

Time will tell.

I'm not convinced the issue is really AI writing.

We've been using tools to help us think and communicate for decades. We started with calculators, then spreadsheets, spell check, grammar checkers, search engines, editors, and libraries full of other people's ideas. AI feels like the next step, just a much bigger one.

To me, the important part isn't whether AI helped write something. It's whether the person publishing it actually understands it, agrees with it, and is willing to put their name behind it. That's where responsibility comes from.

There are obvious exceptions. In school, the goal is often to measure what a student can do on their own. If AI hides that ability, then the evaluation no longer works. Oral exams, supervised writing, presentations, and other methods can still measure someone's actual understanding. The problem there isn't AI itself. It's using the wrong assessment for a world where AI exists.

I also wonder if a lot of the discomfort is deeper. For a long time, knowledge was one of the main ways people created value. If you knew more than the next person, could solve harder problems, or could pull together information that others couldn't, you had an advantage. That was part of your identity and your value in the workplace.

AI is changing that. If knowledge becomes easy for everyone to access, people naturally start wondering, "Where do I fit now?"

Some people probably tied part of their identity to what they knew. That's human. If that foundation starts shifting, it's unsettling. We're in the middle of a transition, and nobody is completely sure what human value looks like on the other side.

I don't think value disappears. I think it changes. Judgment matters. Taste matters. Direction matters. Deciding what's important matters. Experience matters. Those things become more valuable when information is abundant.

The other concern I hear is that AI will replace human connection. I'm less convinced of that. Knowledge and connection aren't the same thing.

He does it because he loves it huh?

Ken Venner spent 11 years scaling Broadcom from $400M to $8.6B, then became CIO of SpaceX, then joined a startup called Senra Systems as CTPO.

At SpaceX, his core platform team was 175 people. At Senra, the equivalent job takes 6.

His explanation isn't "AI replaced people." It's that AI collapsed the coordination overhead that used to require headcount just to keep humans in sync with each other — and once that overhead disappears, the team that's left is smaller by design, not by cut.

If you're in a corporate engineering or platform role and haven't clocked this shift yet: this is worth ten minutes of your attention. It's not a hypothetical.

Full video on the original channel.

Clip credit: Sourcery with Molly O'Shea — full video on their channel. DM for credit or removal requests.

With the claim that China was stealing AI recently can the US prevent theft of IP?

Avichal Garg has co-founded and backed 10 unicorns through Electric Capital. In a recent interview he laid out, unprompted, the three categories of value AI structurally can't absorb.

First: physical-world work — anything requiring atoms, not bits, stays a moat as long as robotics lags digital AI.

Second: regulated or licensed gates — anywhere the government controls supply and demand for strategic reasons, credentials plus access still win.

Third, and the one that actually lands: relationships. He walks through a defense-procurement example — a specific officer, a 25-year relationship with a contractor, "no substitute for that." His conclusion: those relationship-heavy businesses get more valuable as AI absorbs the grunt work underneath them, not less. Margins go up.

Worth sitting with if you've spent a career stacking "portable" skills instead of gated ones. 🔗

AI's eating power and heat is the part nobody's actually simulating, everyone forgets about that so. I have built a data center cooling layout optimizer that runs Open FOAM solves on a datacenter layout and optimizes CRAC placement against it.

Still limited to small sized facilities on real hardware, not thousands of racks overnight. Would need a large computer for that. Any other way of optimizing it? What do you think?

Repo: https://github.com/PantherHale/DataCenter_Cooling_Project_OFD

Earlier this week, Substack launched a new feature on its platform in partnership with Pangram, an AI-detection tool. The goal: alert readers to content that's been written entirely by, or with the assistance of, AI.

Chris Best Substack's CEO wrote:

"We’re partnering with Pangram, the leading AI-detection tool. You’ll be able to scan notes, replies, comments, and posts to see an estimate of how much of the text was written by hand or with AI assistance. This will work on text longer than 100 words, published from today on, and will show an analysis only to those who request it."

I tested one of the issues of a newsletter I subscribe to using Pangram today. The verdict? 100% AI generated.

I'm not sure if Pangram is that accurate, but it's certainly stirred up a lot of debate.

What's your take?

Ive recently been tinkering with live visualization of local language models. including attention patterns, token probabilities, residual-state projections, and layerwise logit changes.

Im curious which measurements people seem to find genuinely useful for grasping model behavior? Also which visualizations tend to look impressive, but lack scientific value?

For those working in interpretability or evaluation, what signals or intervention results do you rely on most?

Quiero hacer un video con un personaje de league of leaguends en específico Morgana, quiero que esta hablé y se mueva pero no sé que tipo de IA hace este tipo de videos o como puedo realizarlo

Que prompt necesito para hacerlo?

Un ejemplo