Jon Radoff 👾/acc 🎮 Metavert

AI Agents | Artificial Intelligence | Virtual Worlds | Open Source Building | Adventurer | Metavert | | Blog:

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

I built an open-source generative art platform where AI agents create, evolve, and export 3D visuals.

It's called Threelab. React + Three.js + Go, 21 built-in patterns, a visual node-graph editor, genetic evolution, and 13 MCP tools so AI agents can drive the whole thing.

Everything exports as standalone HTML. 🧵

[Rishabh Karnawat @rishabhkio]

Many of you don't understand how serious this is, so let me break it down.

In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force.

For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew.
About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea.

They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science.

Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption.

Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean.

And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea.
So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited.

Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved.

But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.

[Google DeepMind @GoogleDeepMind]

This is Genie 4, our most realistic and interactive world model yet. 🌐

Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.

[Peter H. Diamandis, MD @PeterDiamandis]

MIND-BENDING FACT: OpenAI announced Monday that its AI proved the Exponential Time Hypothesis (ETH) is false — a foundational pillar of theoretical computer science since the 1970s. The ETH holds that NP-complete problems cannot be solved faster than exponential time; cryptographic security, algorithm complexity bounds, and the theoretical ceiling on AI planning have all been calibrated against it being true. One model, running in collaboration with academic partners, disproved it. Simultaneously, OpenAI disclosed it finalized a proof of Yang-Mills existence on October 16 — one of the seven Millennium Prize Problems, each carrying a $1 million Clay Mathematics Institute award. Navier-Stokes was solved September 8. Two Millennium Prize Problems in 49 days. Five remain.

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

@HowToPrompt__ So it’s all about the entropy?

[Tim Sweeney @TimSweeneyEpic]

When I was writing Unreal Engine 1, my time was divided as follows:

- Around 30% went to seriously deep coding on hard problems: assembly language texture mappers, C++ BSP trees, data persistence, object oriented systems design, and networking.

- Around 70% was just typing and refactoring large bodies of code needed for a complete engine feature set. This was not hard, and while I was doing it my mind was mostly on the deeper coding topics, thinking and planning for what's next. It didn't feel much different from vibe coding today, except that it was just me typing code on an IBM Model M keyboard rather than prompting it and reviewing the results.

So, it has always seemed perfectly logical that we would rely on AI for that 70%, once the AI could do it with a similar quality standard as we could, which has never been perfection. It came as no surprise that, after a few years of AI model evolution, it was able to take the reigns on that 70% and free up programmers' time for more valuable tasks.

What's more of a surprise is the emergence of AI as a sort of insta-Carmack able to solve hard technical problems quite cleverly and often cleanly, and that it's able to reason about and work in complex 3D scenes both by operating the tools and by directly understanding the raw data files.

I abandoned assembly language in 1998 when out-of-order CPUs made much simpler C/C++ code faster, and never I missed it. If prompting through architectural documents, concept art, and design plans can transcend typing code and tweaking vertices, that will enable developers to spend their time on higher-level, higher value technical and creative tasks.

What matters is the end product, whether it's a game or an engine. Anything developers can do to try out more ideas and make better software and games faster is a win for everyone.

[ℏεsam @Hesamation]

> Claude gets a 25 year old NSA cryptography mystery
> $36,864 bounty
> solves it in under 30 mins
> “is it suspicious that such an obvious thing wasn’t discovered in 25 years?”
> “let me be skeptical: am I in a simulated?”
> “wow, it's a real discovery”

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

Quantitative fields are accelerating! Why: a lot of this science is really about a problem’s search space, and our AI tools are uniquely suited to the search.

[Tornado guy @fanofaliens]

we're so damn cooked 😭

google's gemini 4 argon just did something actually crazy for fusion energy

in 1967 a physicist named harold grad guessed that the magnetic fields inside twisty fusion reactors could never be solved exactly.
people believed him for 59 years

last month two papers finally proved him wrong. one of them used openai's GPT-6 Astra

then google deepmind gave that paper to a team of argon agents and they found a whole new family of answers nobody had.

the first answers showed up in a few hours.

the paper literally says "promising candidates appeared within hours"

NOT YEARS.
HOURS!!!!

the scientists checked every step by hand and it held up

59 years of humans vs a few hours of AI????

it's over. ai won.

[Andrej Karpathy @karpathy]

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:

Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:

Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:

Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:

Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!

In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

I'm looking for people with a strong Machine Learning background who would be interested in working on and/or consulting around optimizing product KPIs (revenue, engagement, retention) given complex time series data and other user behavioral data. Send a DM if interested in talking!

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

It’s called Peggys Cove @AirCanada

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

Really astonishing telling of the Odyssey, made with Grok Imagine. Congratulations @NemPerez

[Nem Perez @NemPerez]

This is "The Odyssey" as Homer intended. My longest, most intricate work to date. I pushed @grok @imagine to its absolute limits to create this 5-minute epic.

What bothered me most about the recent adaptation was the omission of the Phaeacians and Princess Nausicaa, whom Odysseus told his story to in the original poem.

I focused my rendition on the introduction to Nausicaa and his love for his family. The motive driving him was the memory he kept for so long, even when his journey tried to make him forget. As a husband and new father, that's something I felt deeply. I challenged myself to try to fit everything I could from the source material and used GROK for research.

Hope you enjoy. Workflow and process coming soon. I'm working on a deep dive showing how I used @grok for research, scripting, and prompting. I used @imagine to animate and visualize every shot and bring this to life. Share it if you dig it! @elonmusk

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

@aakashgupta Sounds like regulatory capture

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

I remember when WIRED was a great tech & culture publication

[dar @radbackwards]

I gave WIRED the exclusive on our hands launch, and they wrote a really weird article about how we are sexualizing robotics…

https://t.co/8VbNvx1rBF

I felt pretty betrayed because that’s not what they told me they were writing about not is that what I’ve ever been about… actually I stand for quite the opposite… But I’ve come to find a lot of dishonesty and malice in the journalism community so I wasn’t surprised.

This is what I sent the author… I’m only sharing this because I hope it encourages journalists to resist the click bait trap and tell truly awesome stories because I for one don’t believe journalism is dead— I think it’s just starting and just needs to evolve past the weird corner of the internet where data driven optimization turns everything into smooth brained shocking brain rot bullshit.

The technological revolution we are going through should inspire a journalism renaissance. Not let it fall into further decay. There is so much brilliance at play in the world and the stories should be told!

My note:

��[author name redacted], it was nice talking to you, but I wanted to let you know that I didn’t enjoy your article at all.

I understand the need to be inflammatory because that seems to be the only thing that gets clicks these days but that doesnt mean you shouldn’t recognize when something special is in front of you.

I trusted our PR team in saying we should offer you the exclusive on what is one of the most important technological developments in the history of Mankind and I deeply regret it.

Good luck with the rest of your writing career.

-Dar Sleeper”

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

Jevons Paradox

[Conor Sen @conorsen]

The Chief Economist at Indeed just posted that software development job posting growth is outpacing overall job posting growth:

[Elon Musk @elonmusk]

I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon.

And I would never cut them off in a way that hurt them badly, even as a competitor. That’s not my style.

Tesla open sourced its patents and we made the Supercharger network available to all competitors, even though we could have made it a walled garden.

SpaceX launches competing satellite systems with no increase in price or use of unfair terms.

Even my worst enemies can attack me on this platform.

…

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

@benln Me 🙏

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

@beffjezos The Dyson one

[François Chollet @fchollet]

The only long term bottlenecks are information and energy.

[Jon Radoff 👾/acc 🎮 Metavert @jradoff]

I asked @anthropic Fable to add a fireworks simulator to threelab .metavert .io (my open-source @threejs experimentation platform) and it one-shot it pretty well. Happy Fourth of July! 🇺🇸