Rick

Teacher, developer, and wannabe guitarist.

[Rick @JDevCast]

@devteamdrew @claudeai really cool animations and I learned a bit too. nice!

[Rick @JDevCast]

I can say as a programmer webdev wasn't enjoyable for me. But learning c or go is not only fun but fascinating. Anyone else feel this way?

[Rick @JDevCast]

@amazing_physics B be the answer

[Rick @JDevCast]

@chetaslua any chance of sharing the code?

[Rick @JDevCast]

@obscura_d32874 @imjustnewatai Well said

[Rick @JDevCast]

@Dr_Singularity I had a lot of respect for Terence Tao, but his ego is on show... and other mathematicians too.

[Rick @JDevCast]

@kimmonismus I would argue this is an amazing time for mathematics discovery. So exciting! It'll contribute more to progress than would have been otherwise. Those that oppose it are showing their true colors!

[Rick @JDevCast]

@dev_maims No, no,no. There's only assembly, c and go. Don't argue with me!

[Rick @JDevCast]

@neogoose_btw Wonderful project and really inspiring, so I'm off to create my own tools.

[Rick @JDevCast]

@badbirb @BrianRoemmele No such thing as a spark of creativity. It's derivitive. You've been reading too much fiction.

[Rick @JDevCast]

I'm not in the games industry, but wouldn't using Jev work well for characters in games. An advantage over what is done now? I'm not sure. Just a thought.

[Rick @JDevCast]

@john_bortotti @typesafeai @mutuals_inc I'm not in the games industry, but wouldn't this work for characters in games. An advantage over what is fine now? I'm not sure. Just a thought.

[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.

[Rick @JDevCast]

@manicode all systems Go, and it does!

[Eli Ben-Sasson | Starknet.io @EliBenSasson]

I don't think people realize just how much AI's latest breakthroughs in math have shocked mathematicians, myself included.
The main thing that leaves me and many mathematicians shocked is what AI is solving:

Early on – till about 4 months ago – AI was good at:
(i) solving problems that humans already solved, like Math Olympiad problems
(ii) finding out that a problem in field A was already solved by an obscure work in field B, using a different terminology (or language).

But starting with the shocking work on the Erdos Unit Distance Problem, and up till the recent advance on Navier–Stokes, a totally different AI is seen.
It is innovating.
It's bringing a new set of tools to the problem, and then proceeding to using those tools very delicately and accurately to achieve the result.
It's a bit like how a brain surgeon delicately operates on the exact region that needs fixing.

This is absolutely shocking and unexpected. It raises the question of what exactly constitutes mathematical intuition and brilliance. Many a mathematician have written about it (I love Henri Poincare's treatise "Mathematical Creation"). But we don't understand how mathematicians reach their breakthroughs.

Now, it's the same with AI, we don't understand how it goes about reaching these proofs but the end result is objectively new, and powerful.
This creativity, insight, ingenuity, is what's shocking to me.

[Rick @JDevCast]

@amazing_physics Morning coffee

[Justine Moore @venturetwins]

Mathematicians

[Rick @JDevCast]

@iohanneslucius @IterIntellectus Talk about mindless crap. I've heard this so many times it's so boring. Do something productive with your time.

[Rick @JDevCast]

@mark_k Very much interested.

[Rick @JDevCast]

I got Astra already in Codex as a Plus subscriber. I'm very impressed. Did not expect to get it that quick. And with the resets too, cherry on top! Coding apps like there's no tomorrow!!
@kimmonismus