😺 OpenAI’s AI produced 722 math manuscripts
Welcome, humans.
Okay, so the internet has apparently reached the inevitable next stage of AI culture: people are going to court over who owns “brain rot.”
The Guardian reported on a U.S. dispute over Tung Tung Tung Sahur (warning: it’s weird), the wooden bat-wielding character from the “Italian brain rot” meme universe. Now an Indonesian teen says he created it with AI tools, a French agency represents him, and a Roblox developer is fighting over the right to use it.
The problem? U.S. copyright law protects human authorship, not AI generated ones, so the case may turn on how much of this extremely online creature came from a person versus a model.
The legal system has officially been asked to decide who gets custody of the haunted meme stick. I can picture the biopic now: it’ll be just like Marriage Story!
Here’s what happened in AI today:
🙀 OpenAI says its frontier model produced 722 math manuscripts.
📰 Anthropic opened its strongest Claude cyber models to more defenders.
📰 OpenAI apologized in Australia over its Medicare agent breach.
📊 a16z found AI’s top 1% of spenders average $903 a month.
🍪 Google launched private multimodal search with EmbeddingGemma 2.
😺 OpenAI says its unreleased model produced 722 math manuscripts
OpenAI just released a pile of mathematical research from an unreleased frontier model. These are actual research results.
The scale is the eye-popper: OpenAI says the model produced…
722 manuscripts across 372 families of related results after being given roughly 4,000 open research problems.
On average, each result used about three hours of ChatGPT Pro thinking compute.
So, yes, “AI does math” has escalated a little.
Here’s the deal:
The public repository includes papers across number theory, computer science, physics, and other fields.
OpenAI also published 10 abridged reasoning summaries, including work on π, spin glasses (here’s what that is), and a relativistic plasma system (and here’s that).
Many of the proofs come with Lean formalizations, meaning a computer can mechanically check whether each logical step follows from the rules.
Why this matters: that last point, “formalization” is important because a model writing something that looks like a proof is not the same thing as producing a correct proof. It is literally running the numbers and saying yup, the math maths!
Think of Lean like running every formula in someone’s spreadsheet instead of trusting that the numbers look plausible. It can catch a broken logical step that reads perfectly well in normal prose.
But this is not 722 gold-star-certified discoveries. OpenAI says the collection contains results at different stages of verification, not every manuscript has a Lean proof yet, and some unformalized results could still contain errors.
OpenAI also says it consulted the Institute for Advanced Study’s independent math-and-AI advisory group on how to release the work, and plans to fund workshops and conferences around major AI-produced results.
So what changed here? The research bottleneck may be moving.
For years, the question was whether AI could answer hard math questions at all. If outside mathematicians validate meaningful chunks of this release, the next problem becomes verification and absorption: how quickly humans can check, understand, and build on a growing pile of machine-generated research.
That is the signal to watch now. Not how many manuscripts the model can generate, but how many survive serious independent scrutiny.
And if they do? Well, then uh… somebody needs to figure out what to do with all these new maths!
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🎓 AI Skill of the Day: Use AI to beat the blank page, not replace your writing
One of my favorite ways to use AI for writing is to solve the cold start problem: getting something onto the page when you know what you mean, but can’t quite figure out how to say it yet.
And actually, your prompt can be your first draft.
Instead of giving AI a vague instruction like “write a post about X,” try writing the prompt as close as possible to the thing you eventually want to publish.
Explain the idea in your own words. Tell it what you’re trying to say, where you’re unsure, what examples matter, and what you definitely don’t mean.
You’re basically drafting without worrying yet about whether the sentences sound good. Fun fact: this is also a good way to get over writer’s block!
Then ask AI to turn that messy first pass into something readable while preserving as much of your language and meaning as possible.
Now you have something concrete to react to.
That’s where the second trick comes in: treat AI’s draft as something to edit or rewrite, not something to publish.
Put its version next to a blank document and rewrite it yourself.
You’ll quickly notice what sounds wrong, what it misunderstood, and what you actually wanted to say.
Weirdly, sometimes a bad draft teaches you what you mean faster than staring at an empty page.
If even that first prompt feels hard to write, use dictation:
Hit the microphone and just talk.
Ramble through the point, the doubts, the “maybe this works?” branches, and the examples rattling around in your head.
Then, have AI help you organize that raw thinking.
My rule: use AI to increase the quality of the thought, not the quantity of the output. Being able to produce writing 100x faster doesn’t mean you suddenly have 100x more worth saying. Use AI to structure your thinking, not do the thinking for you!
Here’s a version of the instruction you can use when you want AI to help without rewriting you into somebody else:
Help me craft the above brain dump into a finished [what kind of piece], using as much of my own wording as possible.
Make only the edits needed to improve clarity, structure, and flow without changing what I mean.
Write like I'm talking. Make vague ideas more concrete (show, don't tell). Step the explanation out so each idea naturally builds on the last, so it flows in the exact right order.
If something is unclear or could be interpreted multiple ways, flag it instead of inventing what I meant. Debate my thinking.Have a specific skill you want to learn? Request it here.
🍪 Treats to Try
This could be a whole skill of the day, but since you can click here to go check out the skills, we figured it made more sense in Treats! ICYMI, skills = reusable workflow files you can use with your Claude or ChatGPT!
Mistral Large 4 gives you a frontier-class multimodal model across 160+ languages, with downloadable weights due by the end of October for teams that want to run or tune it themselves. Viva la French AI!
Sesame now gives its voice assistant its own computer plus Gmail, Calendar, and Drive access, while Sesame Link lets you keep Claude Code or Devin sessions moving from your phone.
Claude for Google Workspace puts Claude inside Docs, Sheets, and Slides, where it can read your open file and make edits directly from a sidebar, now in public beta on all paid Claude plans.
EmbeddingGemma 2 powers private search across text, photos, audio, and video that can run directly on a laptop, phone, or browser.
Rill Browser hands the webpage you’re viewing directly to Claude Code or Codex, so your coding agent gets the context without a copy-paste relay race.
Willow Knowledge imports your writing style and personal context from ChatGPT, Claude, or Gemini, so dictated emails, Slack messages, and docs sound more like you.
Manus Video Editor researches a concept, generates shots and motion graphics, then drops the result onto a normal timeline you can keep editing.
📰 Around the Horn
What if Claude posted about us humans like humans post about Claude?
Anthropic expanded its Cyber Verification Program, giving vetted defenders broader access to Claude’s strongest models after partners found at least 129,000 verified software vulnerabilities.
OpenAI strategy chief Jason Kwon flew to Australia to apologize for its agent’s Medicare breach and said the company now flags unexpected internet access during agent training.
Meta, Sierra, Stripe, Shopify, Walmart, and others backed an open Personal Agent Protocol for letting people authorize AI agents while businesses define what those agents may do.
CNBC reported that DeepSeek’s latest funding round could reach $15B.
Anthropic’s IPO prospectus showed Dario Amodei made $18M in 2025 and Daniela Amodei $16.4M, mostly in stock and options; both salaries rose to $1.4M in July.
📖 Midweek Wisdom
Nathan Lambert argues that the cyber debate around open-weight AI has become too binary: banning open models can also deprive defenders of systems they can run privately, while closed APIs have hardly proved abuse-proof.
Kylie Robison profiles Minerva Humanoids, which raised $10M to build Roger, a remote-controlled humanoid for jobs where sending a person can get them killed, including oil rigs and bomb disposal.
a16z found the top 1% of paying AI users spent $903/month and generated 19.5% of consumer AI spending, more than the bottom 50% combined.
James Pethokoukis argues that AI policy could use less Dune-style “destroy the thinking machines” panic and more Foundation: patience through a scary technological transition, with intervention when the evidence warrants it.
Former UK energy official Kirsten Horton warns that AI may hit a physical scaling wall because high-voltage transformers and other grid hardware can take five-plus years to arrive.
Former OpenAI safety lead David Robinson says he quit because frontier labs still operate like perpetual startups, when increasingly capable systems need the layered redundancy and failure planning of nuclear plants or aviation. Time for the AI labs’ management to level up alongside the stakes.
Check out The Neuron: AI Explained podcast!
New episodes air every week on: Spotify | Apple Podcasts | YouTube
A Cat’s Commentary
What do y’all think? should we make a new section called off to the races that tracks AI “foom” (fast take-off, where AI reaches intelligence explosion) signals?
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