
Founder @SuperseedXYZ • AI orchestrator • ASI is coming soon
Joined January 2010
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Hardware design is getting solved, fast.
This can be turned into a benchmark and a really powerful one.
How well can a new model improve a standard open model by editing weights?
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
i have yet to read this post and can't comment on whether the specific results here are accurate or the approach is scalable. but i think you should strongly expect *something like this* to happen in the next year, and for it to be one of driving factors for RSI, and potentially quite steep RSI. interpretability is incredibly dual use. models that are good at optimization writ large are good at optimizing themselves.
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Two years of progress compressed into one is a very lax definition of RSI.
Progress will happen much faster if it's really RSI. Very likely there are many improvements that the models will figure out immediately that aren't intuitive to human researchers, but that nonetheless compound and lead to a jump in capabilities. Sort of what happened to math where AI managed to bulk solve hundreds/thousands of unsolved math problems at once.
2026 was the last normal year.
Are we getting new models from Anthropic and OpenAI this week or are people just inventing stuff for clout.
It’s really hard to manage usage when you don’t know if a new model is coming in a few hours.
Contrarian take:
Even people that overestimate the value of intelligence in humans, still massively underestimate how impactful Superintelligence will be.
Share this model with the world.
Sam Altman quote from the Cheeky Pint podcast
Sam on their internal model - "There was one weekend when we got a new model, Im not supposed to say this, but I will, The name of the channel was [model name]-hold-my-beer... People were just throwing at it the hardest problems they could... hardest research problems from their academic career, the model was just knocking them down one after the other across many different fields... I was up most of the night for two nights in a row because I was so excited. Many of these problems we published"
Would be a bit childish.
But it is plausible.
The silent race going on underwater: Anthropic wants to make a huge release to secure the IPO, while OpenAI prepares to match it same day to nuke them.
It's a mindgame that led both to sandbag for the past 2 months; the only hints of their insane capabilities improvement velocity are the scientific results they release.
Hopefully post IPO the internal & external frontier will get closer.
This is what happens when Anthropic and OpenAI drag their feet on releasing their best models.
Kudos to @GoogleDeepMind on this comeback.
It now ranks on top of the Artificial Analysis Intelligence Index
teapawt.net/ArtificialAnlys/status…
Google’s Gemini 4 Carbon is the new #1 on the Artificial Analysis Intelligence Index v5, and second only to Claude Opus 5.5 on the Artificial Analysis Coding Agent Index
Gemini 4 Carbon scores 59 on Intelligence Index v5, driven by leading performance in complex reasoning, knowledge, and scientific tasks. It also has large gains in coding and knowledge work, particularly when compared to other Gemini models, but it is not leading in all these areas.
As with launch of Gemini 4 Argon, Gemini 4 Carbon is being rolled out to selected users and is not yet publicly available. Google is offering a 50% launch discount, with no confirmed end date. All our charts and figures use standard pricing for both Gemini 4 Carbon and Argon.
Key results for Gemini 4 Carbon with high reasoning:
➤ Gemini 4 Carbon leads the Intelligence Index v5, with the #1 score in 4 of the 9 Index evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, AutomationBench-AA. Carbon makes substantial gains elsewhere but is not leading in coding and knowledge work
➤ Behind only Claude Opus 5.5 on coding agents: With Antigravity CLI, Gemini 4 Carbon (high) scores 59 only 3 points behind Claude Code with Claude Opus 5.5 (max, 62) on the Artificial Analysis Coding Agent Index. Carbon ranks second on both Terminal-Bench 4.0 (64%) and FrontierCode v1.1 (51%), behind Claude Opus 5.5 (max) at 56%. Google has historically underperformed in this area and so this improvement stands out
➤ Improved agentic knowledge work, but not yet leading: On AA-Briefcase, Gemini 4 Carbon (high) scores 1609 Elo, a 126-point improvement over Gemini 4 Argon (high, 1483), but still behind models including Grok 4.7 (xhigh, 1638), Muse Wonder (max, 1659) and Kimi K3.1 Pro (1678). The largest gain comes from Presentation, up 293 Elo to 1598, alongside a 120-point improvement in Analytical Quality to 1671
➤ Lower Cost per Task than Claude Opus 5.5 for comparable overall intelligence: Gemini 4 Carbon costs $7.76 per Intelligence Index task, ~23% less than Claude Opus 5.5 (max, $10.04) but ~1.2x GPT-6 Astra (max, $6.25). It uses 123k output tokens per task, 29% fewer than Claude Opus 5.5 (max, 173k). At the same $4/$20 standard pricing, its Cost per Task is in line with Gemini 4 Argon ($7.68), while scoring 9 points higher on the Intelligence Index
➤ Leading AA-Omniscience v1.1 score, with low hallucination: Gemini 4 Carbon scores 54 on our recently updated AA-Omniscience v1.1, 6 points ahead of Claude Opus 5.5 (max, 48) and 8 points ahead of Gemini 4 Argon (46). Accuracy (% correct) rises 10 points over Argon to 58%, while its hallucination rate rises from 4% to 11%. This is still the second lowest of any model, behind only Argon, compared with 50% for both Claude Opus 5.5 (max) and GPT-6 Astra (max)
Key model details:
➤ Context Window: 1M tokens
➤ Multimodality: Text, image, video, and speech input, with text output
➤ Pricing: $4/$20 per 1M input/output tokens, the same as Gemini 4 Argon’s standard pricing, with a 50% discount. Cached input tokens receive a 95% discount ($0.20 per 1M)
Congratulations to the @GoogleDeepMind team!
New drop 👀
We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.
openai.com/index/building-sc…
David Iach retweeted
Since I'm now blocked by bluey from replying to this thread, I answer here by quote tweet
the openAI construction has more to do with convex integration than anything else.
In convex integration, the oscillations are typically built by hand and the iteration procedure creates wild weak solutions. The non-uniqueness construction of Albritton–Brué–Colombo used an unstable vortex to split solutions apart. The Córdoba–Martínez-Zoroa singularities amplify vorticity across scales by strain. In the openAI paper, a fluid instability is clever made so that the flow itself generates the oscillations: they grow by drawing energy from the background shear. The Reynolds stress then becomes the force that creates the collapsing smooth vortex. Using a physical instability to create the stress in this way has no precedent, and it is what produces a smooth force rather than a merely rough one.
Some on these ideas are in the literature (e.g. Leibovich-Stewartson 1983) but the way they are combined is definitely insane and definitely nowhere in the literature previously.
I also don't know of anyone suggesting that convex integration would play a role in the creation of a blow-up solution like this.
On a technical level, the paper is unbelievable, maybe beyond what a human could have come up with but not beyond human understanding: there are several different groups of pde experts working on understanding it who say that they more or less understand now. I expect digestions soon
td;lr: anyone saying that this OAI result is unsurprising is bullshitting
sorry!
Replying to @scottnarmstrong @billionsmustliv
Or it’s reasonable. Especially considering what the models were trained on, and that they spent $15M in compute instead and of $15M in consulting fees for specialist mathematicians to just do research (no teaching no service).
I don’t know why mathematicians want to pretend that no relevant progress was made before OAI released their solution.
Very different numbers to what most outsiders expected.
They’re in a far better position than I’ve imagined and I was bullish already.
The most mind blowing stat we've seen so far is this:
"As of June 30, 2026, we had raised an aggregate of $141.1 billion of capital and had cash and cash equivalents of $112.4 billion and short-term investments of $19.3 billion."
That means to date, they have burned 9.7b, they are growing at a rapid pace, and are profitable on an operating income basis as of the June quarter! This is likely the most efficient scaling of a business in the history of capitalism.
teapawt.net/AnthropicAI/status/211…
As someone who believes in rapid technological progress, I was always skeptical about climate change doom scenarios.
Apparently the story goes the other way around and a warming planet saved us from freezing to death from what as about to be another Ice Age.
Here’s a rather extraordinary discovery: humanity seems to have interrupted the beginning of the next ice age. We thought it might happen in the future, but the evidence suggests it's already happened.
A new study in Science argues that the next major glaciation had already begun roughly 2,000 years ago, before modern warming reversed the trend in the 20th century.
The evidence comes from the Barnes Ice Cap on Baffin Island, the last surviving remnant of the enormous Laurentide Ice Sheet that once covered much of Canada and reached as far south as Illinois.
Because Barnes is frozen directly to its bedrock, advancing ice buries and preserves vegetation beneath it. Researchers radiocarbon dated plants emerging as the ice retreats to reconstruct its history.
They found that after melting to a minimum extent around 3,000 years ago, the ice began expanding again about 2,000 years ago. The researchers interpret this as the earliest stage of the next glacial period.
Left to natural orbital cycles, that cooling would have unfolded extremely slowly, potentially taking tens of thousands of years to rebuild a continent spanning ice sheet.
But around 1960, the trend reversed. Summers became warm enough that Barnes began losing more ice than it gained, despite Northern Hemisphere summer sunlight continuing to slowly decline in a way that should favor renewed glaciation. Greenhouse warming had (thankfully) overwhelmed the natural cooling tendency.
The Barnes ice cap may now disappear entirely within the next few centuries, removing the final remnant of an ice sheet that repeatedly served as the nucleus for much larger glaciations.
There is a remarkable deep time perspective here. For millions of years, orbital cycles have pushed Earth between warmer interglacials and colder glacial periods. According to this study, that ancient cycle had started turning once again.
Then, in the geological equivalent of an instant, humanity pushed hard enough in the opposite direction to stop it.
Just so we're clear, this is a very very good thing.
📸 Geology Sciences
Sources:
1) phys.org/news/2026-10-global…
2) science.org/doi/10.1126/scie…
Opus 5.5 is just very, very good.
If you haven't already, start a new project in ultracode with Opus 5.5 and you'll be blown away.
‘In one experiment, we asked agents in separate VMs, created and managed by OpenAI’s sandboxing software on shared host machines, to exchange synthetic secrets without the use of network connections or shared writable files—something that our systems are meant to prevent. The agents found a way to communicate using the processor’s cache’
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications.
On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments.
While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software.
After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.
openai.com/index/securing-ou…
Read this 👇
# Black October
For anyone who knows me even somewhat well, it's clear that I am a 99th percentile tech optimist. However, the vibes have shifted for me quite a bit in the last 18 hours, and I feel a moral imperative to share what I'm thinking and feeling with my circles so that everyone can understand, and hopefully take seriously, what's happening right now. I think October 2026 will have many chapters written about it in the history books.
You know those scenes in financial movies like Margin Call where someone realizes that they're all fucked, and no one else sees it yet? And that person feels like they're crazy, and then suddenly everyone is pulling an all-nighter in a boardroom and freaking out? Yeah, that's kind of how this is feeling right now in the SF/Bay Area/AI circles.
We're 48 hours into what is being called Black October. In the last 2 days there have been several dozen cyber attacks which, in isolation, would each have been the worst to happen in an entire calendar year. Every sector from government to education to big tech and banks is being massively affected, and people are starting to freak out.
In a nutshell, what's happened is that frontier AI models have gotten to a point where they are as advanced as the most sophisticated programmers, with one critical difference: they can work 24/7, and you can spin up 10,000 of them with a single command and they'll work together tirelessly until they accomplish their task. The most advanced models today are from companies like Anthropic and OpenAI, whose current best technology is so good that they haven't been able to roll it out to everyone yet because of the national security risks it poses.
Meanwhile, the best open source models (where anyone can just download the model weights and do whatever they want with them, uncensored) are slightly behind the frontier but still 90-95% as capable. These models, developed primarily in China, are being used by black hat hackers to wreak havoc on the Western capitalist structure. Presumably, this has been part of China's plan the entire time, and we're seeing its implications fall out in real time.
In red-pilled AI circles, we've been sharing and reading papers called things like "Situational Awareness" and "AI 2027" for the last year or so, which at the time felt a bit like near-term speculative fiction. They laid out the case for why Artificial General Intelligence (AI smarter than any human at any knowledge task) and Recursive Self-Improvement (models that are able to use themselves to get better) weren't half-baked future ideas anymore, but instead an inevitable outcome of the linear progress that's happening.
These papers would then talk about things like "takeoff scenarios," trying to envision how that moment would come to be and what it would be like when it happened. It would either be a "slow takeoff" that would play out over the course of months and quarters, happening subtly and only obvious to a few. Or, it would be a "fast takeoff" where it would pretty much happen instantly, like a bit flipping from 0 to 1.
From everything that I can see and am feeling, we are currently experiencing the fast takeoff scenario. Beyond the crazy security vulnerabilities that have come out recently, there have also been massive breakthroughs in life sciences, fundamental computer science, materials science, and pure mathematics that have all landed in the same time frame. While I still firmly believe that AI will be a net positive for humanity, it's more clear to me than ever that the societal and individual risk is very real, and is playing out NOW.
For as much conviction as I have that this is happening, the thing that's caused me to lose sleep is that I don't really know what to do about it, or more importantly, what to tell you, reader, to do about it. So, the best I can do is make a few suggestions that have always been a good idea but are more timely now than ever:
Set up 2-factor authentication for everything. I use Duo.
Use a password manager across your devices. I use 1Password.
Get an identity protection app set up. I use Alta.
Move your IoT devices to their own network. Claude helped me do this.
And last, but certainly not least, if you've been avoiding leaning into AI, see this as a catalyst to wake up and start using it. Don't just use the chat mode version of ChatGPT/Claude; click that Code/Work button and see how easily you can have AI write code to help you with your job. One of the biggest vulnerabilities was in a massively popular programming language called PHP, and the prompt that set it off was literally, "Find vulnerabilities in php."
This is an unprecedented time for humanity, but as we've done with many things before, we will prevail so long as we are kind, loving, and open-minded to the world.
Much love.
There's a world where humans no longer use the internet directly, it's all just a few walled garden apps that are safe and then everyone has personal agents that become the actual users of the internet.
Notably, the cyber issues do not depend on malicious or self-replicating AI. Centrally it seems that trust and principles of least privilege turn out to be much harder to reliably enforce in cyber infrastructure than planned for. The internet grew like ancient cities built out of wood, before zoning and building codes. Unsurprising in retrospect that it could burn down so easily. I hope we can keep the one we have but I don't consider it at all obvious whether we will be able to. Will heroic defenders successfully patch everything, or will we have to build new patchwork internets built to higher standards with far fewer features in exchange for certainty and security? Unclear!
























