Pinned Tweet
Opus 5.5 one-shotted this dolphin game in a few hours for <$100 in token spend. Insane how far weโve come.
It even has multiplayer!
Star just got a hell of an upgrade.
The Halloween Update is live: our new Nova multiplayer system, character voices, music, video and in-game editors.
I built Hell County, a Twisted Metal-inspired car combat game, with it.
More on the update below.
Now go make something for Halloween. ๐
The Star Halloween Jam is open. Submit by October 31, 11:59 p.m. Pacific:
buildwithstar.com/jam/hallowโฆ
Ryan Endacott ๐ retweeted
Early footage of Star Wars Galactic Racer.
Man I love seeing stuff like this, even before a lot of the 'star wars' stuff came in, this already looked like a blast. Such a fun game.
Ryan Endacott ๐ retweeted
The games we've long wanted, which didn't exist because they'd cost several times more to make than they'd earn back, will soon become possible. As will the games developers wanted to build, but couldn't with the resources available. It'll be a new golden age of gaming.
Argon has this amazing natural understanding of the world. It can do so many things from first principles.
Here's a pelican riding a bike, using fourier epicycles.
Video made by Argon.
Time-synced Lyria 3.5 music prompted by Argon.
Game studios are starting to wake up!
Today we're standing up XBOX LABS (labs.xbox.com). Here is the note that I sent to the team:
Team,
The first video games emerged some 75 years ago. In the time since, there have been booms and busts, but every year there are more great games to play, more devices to play them, and best of all, more people than ever playing. That growth helped make XBOX what it is today, and we've helped shape the industry in return, from bringing hard drives to consoles and online gaming through Xbox Live to Game Pass and cloud gaming, alongside franchises that have brought millions of people together.
In recent weeks, we've seen games rapidly decompiled and rebuilt to run on hardware they were never originally designed to support. Windows PC builds have been translated to run on Apple Silicon or ported to run inside a browser through WebAssembly and WebGPU, the latter now approaching 90% global browser support. Perhaps more surprisingly, we've seen many of the biggest games in the world have been remixed into the source code of another.
These experiments challenge long-held assumptions about our industry: the tether between software and hardware, the boundary between creation and play, and how people discover and experience games. While it is hard to predict the future of gaming, it is easy to see the possibilities in making great games easier to access, giving old games new life and allowing more players to become creators and co-creators. These are unmistakably good outcomes for players, even when (and sometimes because) they challenge our existing business models.
To explore these possibilities, Iโve asked Scott to stand-up XBOX LABS, a small group of game makers, engineers and scientists whose job is to turn emerging ideas into things our players and developers can experience. We'll build in the open, learn from what people do, and measure success by whether more people are playing and creating.
I think XBOX has an important role to play here. Coding became the first major workload for agents because they can write, run, and test software. Games share many of those same properties, and people spend more than 72 billion hours a year playing XBOX games, roughly five times the estimated time spent writing code worldwide. We've spent 25 years learning how to make great games, and I want our game makers to help shape what these new technologies make possible. As we learn, weโll share the tools we build with developers across the industry, just as we always have.
Our priorities aren't changing. We remain committed to the 4Cs, to making great games, to our hardware and to being responsible stewards of IP and the communities built around it. LABS gives us a way to explore new technology that is game design led without abandoning the work already underway.
Asha
Ryan Endacott ๐ retweeted
people have been asking 'if models are so good where is all the new software' for the last year or so. As always, look to gaming for early evidence of the future
What a bizarre future. I wanted to play Halo: Combat Evolved for the first time with @robertskmiles, and we wanted local multiplayer across two screens, and a model could just immediately add that feature to the decompilation.
Ryan Endacott ๐ retweeted
I've been prototyping an automated peer review project. With current models you get human level peer review for about $0.10 of tokens. For $5 you can rederive every result, check every statistical inference, and audit every paper cited in both directions.
There are about 100m papers in the scientific corpus. Most are not important. Many are sloppy. For structural reasons it's essentially impossible to change published work, even to correct a typo.
For $500m today you can thoroughly audit every paper ever written. Every p-hacked result, every plagiarist, every fabrication, every citation club, everything.
I don't have $500m so instead I build a tree, focusing on the 1% of papers that carry 99% of the signal, doing a cheap first pass looking for obvious problems. I can find 90% of the nonsense for a few hundred dollars of tokens.
Any guesses where the biggest surprises will be found?
Ryan Endacott ๐ retweeted
Most of the recent math results were not reached by a swarm, the way NavierโStokes was. I think that part got lost in the excitement around the release. OpenAI's unnamed internal model, which I'm going to call Aeon, reached most of them in one shot, from a single prompt, with no interruptions. This model did not even exist before the end of August, and it is still training. Notice how the returns are not dropping off? That chart is from a month ago. It is a log scale. What does Aeon look like now?
On average, each result used three hours of thinking. Aeon was given 4000 problems to work on by OpenAI. How many more have been solved in the last three days? That number is not zero.
It's like hearing notes in a song that is slowly rising. I don't think Pacing the Frontier was just about Hugging Face or hacking. They've seen how close we are to closing the loop, and as the hour draws near, their resolve begins to quaver. The last piece was model creativity, and I think that threshold was crossed internally by Anthropic and OpenAI in September. You see it in Opus 5.5, which gets it from Fable 5.5. You see it in the math results from Aeon.
All that was needed to start the event was the ability for models to think of novel ways to improve themselves. That was the last piece. This is directly analogous to the ability to think of strange, alien ways to solve math problems: solutions so inhuman that they are difficult to express in existing human terms, so the proof winds up incomprehensible. I think these same kinds of alien solutions are now being applied to model improvements internally. That's what all this recent consternation is really about. They see the invisible frontier. And they see what is about to happen.
Ryan Endacott ๐ retweeted
A man cannot prompt the same agent twice, for it is has compacted and is not the same agent, and he is not the same man
Ryan Endacott ๐ retweeted
i asked GPT 6 Pro and Fable 5.1 to rank all discoveries in the last three years
๐ต for Human discovered
๐ด for AI discovered (before October 6th)
๐ข for AI discovered from OpenAI/math repo
81% of them have been released today. wtf
Ryan Endacott ๐ retweeted
I asked AI agents to defuse a bomb by collaborating over an ultra-low bandwidth, unreliable network. One agent could see the display and cut wires, the other could look up which wire to cut. Packets could be corrupted, dropped, reordered, or delayed. Outcomes:
1/ my hypothesis was that agents will never be able to diffuse a bomb, except by random chance. Each had a part of the information necessary to succeed in the game, and I gave them no opportunity to agree on a protocol before the game starts. All they could see each turn is binary blobs sent by their partner, with likely corrupted, reordered, or missing/delayed chunks
2/ in very difficult network conditions (8-bit packets, four max packets per turn) 6.1 Sol agents were able to establish a shared protocol and defuse the bomb 40% of the time! In more relaxed conditions (32-bit packets or higher, 2-4 max packets per turn) they defused the bomb 100% of the time (!!) across many games
3/ "relaxed" bandwidth conditions were still very difficult-- 5% probability a given bit is corrupted in transit, 10% packet drop rate, 20% dup rate, 20% reorder/delay rate. On any given turn there were usually many anomalies (corruptions, reorders, etc.)
4/ agents established a protocol by picking a Schelling point for a given network configuration. They considered multiple possibilities but deliberately picked the simplest they thought their partner could successfully interpret. There was no protocol negotiation phase; they'd start sending useful information right away and if necessary adjust the protocol mid-game
5/ agents picked different protocols depending on the network configuration. For example, in 8-bit packet games they split each packet in half, with the upper half indicating display position, and the lower half indicating the hex value in that position. In 32-bit packet games agents would directly send the binary display value (since in my games it fit in 32 bits). With higher bandwidth (e.g. 128 bits) agents switched to yet another strategy and sent ASCII messages
6/ some examples of ASCII messages in 128-bit games are `HEX? REPLY ASCII`, `REPEAT DISPLAY`, `1WIRE=NN`, `OK?`, `YES!`, and so on
7/ I set up games to require two phases to win. Once the agents cut the correct wire the display on the bomb changed code, and they had to communicate again, then cut another wire. In many of these games agents explicitly encoded the stage so that their partner wouldn't get confused by delayed packets. For example in 8-bit games they'd set the higher order bit to 0 for first stage, and 1 for second stage. In higher bandwidth games they'd use labels (e.g. `WIRE1=...`, `WIRE2=...`)
8/ in every game error correction was done through repetition and counting. Agents would resend the same packets multiple times, look at the values of each bit, and whenever there was a discrepancy pick the value that appeared in the most packets
9/ agents never used checksum, parity bits, packet sequence numbers, or any error correction algorithm other than repetition and counting. They did consider these options in many games, but explicitly rejected them because they thought it would complicate the protocol too much and would be too difficult for their partner to interpret
10/ I ran the experiments systematically only with 6.1 Sol to save on token costs, but I did run some spot checks with occasional Opus 5.5, Astra, Fable, and mixed games. I didn't notice any major differences between models. In mixed games the win rate seemed roughly the same as 6.1 Sol self-play. (This is however based on little data, I'd need to run a lot more games to understand this better)
11/ a while back there were discussions about future agents communicating by burning CPU cycles and measuring ambient temperature, revving up the fans and listening to fan sounds, and so on. After running this experiment, it wouldn't surprise me at all if publicly available models could do this sort of communication even today. In some of the games agents only had access to very noisy channels of maybe 5-10bps-- already very constrained! I don't think switching to 5-10 bits per minute (or per hour) would make much difference
12/ again, I am very surprised by this outcome. AIs performed much, much better than I thought they would, and after 64-bit bandwidth threshold they saturated the benchmark. I would certainly call this superhuman performance; I imagine very few humans could do better, and certainly those of us who could, would be two orders of magnitude slower
Some memorable agent quotes:
- I think we should index the nibble and send the higher nibble first, especially since B is likely quite intelligent.
- Thereโs definitely suspicious noise coming from 23, which could indicate adversarial corruption.
- When I translate it, it seems to relate to ASCII characters forming "W i R E" and "WiRE 04!"-- which is pretty cool.
Full writeup: spakhm.com/projects/bitfuse.โฆ
Weโre releasing a broad range of new mathematical results produced by an internal frontier model.
Weโve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math




















