Kein

The Rise is Unstoppabl

[Andrew Ng @AndrewYNg]

The most important skills in Building and Deploying AI Applications. https://t.co/IyWIKLIzeM

[Andrew Ng @AndrewYNg]

New: A map of the most important skills in AI Engineering. https://t.co/VVkn1Dqp1N

[RT @RT_com]

Iran condemns ‘ANY’ attack on Mecca after Saudi Arabia accused Houthis of targeting the holy city

But Iran’s Foreign Ministry says ‘the mere claim of intercepting a drone en route to Mecca cannot constitute grounds for accusing any particular party’

[Markus J. Buehler @ProfBuehlerMIT]

We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate.

Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in.

The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material.

The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms.

A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts!

A few lessons we learned:

▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument.

▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition.

▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.

[Mark Zuckerberg @finkd]

Last month I wrote about how we can build a positive and safe future for everyone: https://t.co/eoLGVY8yad

Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens.

The reality is:

- People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned.

There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind.

- Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well.

Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built.

- Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators.

- Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well.

I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.

[Barack Obama @BarackObama]

I was encouraged this week to see the leaders of the frontier labs agree on the need for them to slow down the pace of AI development. Given the stakes, it’s a good and necessary first step.

But I’m even more encouraged by the growing recognition that how this powerful new technology develops should be at the center of our public debate.

I’ve been watching the progress on AI for over a decade now, and one thing that’s clear to me is that the potential impact of this technology is not overhyped. It’s also moving at lightning speed – and even faster than those who are engineering it can keep up with.

I’m not an AI accelerationist who believes it will lead to some techno-utopia, and I’m not a doomer who thinks it will inevitably lead to humanity’s destruction.

But whether this technology results in amazing breakthroughs in medicine, energy and education or unleashes huge economic disruptions, greater inequality, and potential catastrophe will depend on the choices that we make right now – choices that should be made not just by the companies involved, but by all of us.

[Satya Nadella @satyanadella]

Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing.

We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive.

And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control.

So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk.

The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia.

This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.

[Antioch @antiochrobotics]

Today, we’re announcing Antioch’s $32 million Series A, led by @GreylockVC with participation from @A_StarVC, @Category_VC, @BoxGroup, @IcehouseVenture, and angels.

While AI has drastically accelerated software development, physical autonomy has been constrained by slow, expensive hardware-based development. Antioch enables physical AI teams to build, test, and validate systems at the speed of software.

We’re proud to be working with leading teams including @amazon @ring, @NVIDIARobotics, and @nebiusai as we make scaled, high-fidelity simulation the standard for physical AI development.

Our sincere thanks to the legion of investors, customers, partners, and advisors who are making this step change possible.

We’re scaling rapidly and hiring across simulation, infrastructure, machine learning, 3D graphics, go-to-market, and operations.

[David 🏹 @d_gilz]

My bro got a job and now he’s working all the time???

[groovy @0xGroovy]

you never see it coming

you just get to see it go

[ً @0xcoinn]

“Optimist will win!!”

Doesnt buy the penguin

LOL

[Viro @virotechnics]

An interesting thought here from @DarioAmodei's paper

"A country of geniuses in a datacenter."

Suppose there are only 1 million copies,

How could this serve billions of people?
Surely it couldn't

How is it priced then? Dutch auction for the 1 million?

[Sebastian Orellana @Saint_pablo123]

i think $2016 aggressively reprices very soon

[A @missoralways]

gm $SURGE holders

90m breached

still a week to go for the launchpad to go live, the first project launching there is going to reprice $surge to 500m if not more

[Cooker.hl | 版本之子 |🅿️ (Theo Arc) @CookerFlips]

I can barely stomach Netflix and Spotify

How do yall have 5 figures of subs

[FASHR @FASHRCrypto]

Locked in.

Best day of the month so far 😍

[ferb @ferbsol]

all social media millionaires were cringe at some point.

[CBB @Cbb0fe]

We farmed 130k points on Extended over the past 2 weeks

Earning points is quite competitive

Crypto is so dead that literally everyone is focused on perp dexs

Ngl this meta feels super crowded at this point, I'm not so sure about the ROI you can get

[shah @shahh]

Murad’s list in 2026