Professional print statement writer

Palo Alto, CA
Joined March 2017
Cody Johnson retweeted
We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve 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
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Cody Johnson retweeted
SpaceXAI has just introduced Grok @Bot Creator Rewards! Rewards will go out every two weeks. Rewards are based on usage: • How many people use your Bots. Are your Bots useful and popular? • How often your Bots are used. Are your Bots providing consistent value over time? For your templates to be eligible for rewards, you must: • Post about your public Bots (and specifically link to them) on X ≥ 2x per pay period • Have X Money set up • Payouts will be sent as part of X’s Original Content Rewards for now.   In the last two weeks, I’ve earned $500 in rewards due to people using my Home Robots template 🔥 More info: legal.x.com/en/grok-bot-temp…
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Cody Johnson retweeted
636606729769440499166579950236036751749912014371509557713570027508971809534551913252252094954941974952859310861988904737359709200557919 is a factor of RSA-896 saweis.net/posts/rsa-896.htm…
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Cody Johnson retweeted
Congratulations to all the Fields medalists who successfully shut down an undergrad coding workshop.
We recognize that the rapid progress of AI in mathematics is disruptive. We're looking to engage with the math community more on the best way to integrate this technology and communicate its impacts. After considering concerns raised by members of the mathematics community, we’ve decided to withdraw OpenAI’s sponsorship of the Caltech Mathathon and have notified the organizers.
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Cody Johnson retweeted
Last week we published a factorization of RSA-260. Today, we’re sharing the methodology of how Devin and a Cognition researcher built the world’s fastest GPU optimized lattice siever, to make factoring numbers 10x cheaper than the previous state of the art: cognition.com/blog/factoring…
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Cody Johnson retweeted
Checking that a major mathematical proof is correct can take years. Formalization—converting the mathematical reasoning into a form computer proof assistants like Lean can verify—can help. Last month, Claude completed the first formalized proof of Fermat’s Last Theorem, one of the most famous theorems of all time. This was a project experts thought would take many years. It is the largest Lean proof ever written. Fermat’s Last Theorem was first proven in 1995 by Sir Andrew Wiles, more than 350 years after it was conjectured. Our proof, which totals over 13 million lines of code, provides machine verification. More importantly, it proves over 29,000 other theorems that the proof requires, across many areas of math which had never before been formalized. We see this as a major step in the long process of firming up the core of mathematical knowledge, building on work from three centuries of mathematicians and hundreds of contributors to Lean and Mathlib. We are optimistic that AI-assisted verification of mathematical proofs will help reduce the burden of refereeing mathematics in an era where more proofs are being produced than ever before. You can read about the process on our Science Blog: anthropic.com/research/forma… And see the complete proof on GitHub: github.com/anthropics/fermat…
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Cody Johnson retweeted
4397328654844826923795068102505872571721883526553349659561256924505973939597593482272505698004801207988043088656411102133523080581 divides RSA-260
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Cody Johnson retweeted
Please welcome to the world a beautiful new geometric object, to do with a problem i’ve always loved. claude really contains multitudes:D Does S^6 admit a complex structure? Yup
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Cody Johnson retweeted
We applied AlphaEvolve's autoresearch powers to an ML pipeline tackling one of the most famous problems in CS: time complexity of matrix multiplication (ω). We improved the SOTA! A small step for ω (similar to recent works), but a nice milestone for AI arxiv.org/abs/2608.16884v1
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Cody Johnson retweeted
Cursor is now part of @SpaceX. Today, we have officially closed our acquisition. We will join the @spacexai team to help make Grok the world's most useful AI and improve Grok Build, Grok Bot, Grok API, Cursor, and more. SpaceX has built some of the most inspiring and impressive technology in the world, and we’re grateful for the opportunity to become part of such a special company. Onwards.
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Cody Johnson retweeted
We asked an unreleased research version of Claude to take a stab at the Riemann hypothesis. It didn’t solve it, but it did make strides on a related problem: it increased the lower bound for the fraction of zeros of the Riemann zeta function that satisfy the hypothesis from 41.6% to 67.2%. anthropic.com/research/riema…
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Cody Johnson retweeted
1/5 Oh wow. GPT-5.6-Sol seems to be on a whole other level. In just three weeks, it seems to have made nontrivial progress on 10+ problems in social choice. Exciting! (But I'm still processing what the existence of such a model means for research.) A snapshot:
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Cody Johnson retweeted
IMO, the next steps are: Within 1 year: strictly better than human AI in all problem-solving aspects of math. Within 2 years: producing mathematics becomes so cheap, AI will produce mathematical theories for all kinds of applied domains on the fly at need. Mathematics becomes the true infrastructure of all of engineering and applied sciences (including AI, biology, optimization, cybersecurity, etc) where approaching things theoretically was too expensive to be practical.
some thoughts on ai and math 1) a new openai model has solved 10 important problems in mathematics; and, the cost of solving them would be ~$2,000 at current api prices 2) on the one hand, this is important, it shows that models are continuing to advance in mathematical capabilities at an extreme pace 3) on the other hand, this feels to me to have been basically priced in, anyone who has following ai math developments should have known this was coming 4) the reason is that once we understood how effective reinforcement learning with verifiable rewards was, it was pretty obvious you could hillclimb math 5) and, at that point, it was basically just the cost of compute for training and figuring out how to get harder problems that were still relevant to capabilities 6) but even that felt like something you should basically be able to solve synthetically; so once again, the question was just, are you willing to pay the compute 7) and, there are just not that many research mathematicians and the skill to be one is fairly rare in humanity, so this also makes the domain easier 8) anyway, the real question is what you can unlock with greater frontier mathematical skills; i would like to see people's thoughts here 9) cryptography is one very promising avenue, obviously applied math ideas for linear algebra, theories of neural networks maybe, maybe simulation, etc... 10) i'm interested in @ChrSzegedy's idea that llms will be able to develop a wide range of applied mathematical theories across tons of domains 11) i would be curious to see what this looks like or whether anyone has proposed research directions; what should we be pointing these models at? 12) also, separate from the frontier, there will be gains in just applying decent graduate level math to all the problems in the world, which are already solved 13) but, for which the expertise was not available, because it was a less valuable domain and a robust understanding of statistics, etc... was scarce 14) there should be some interesting startups here; where people can begin to take lower value domains or smaller sites and apply math cheaply to them 15) i think though that otherwise i'm sort of bored with mathematical announcements; like they are cool but i want to see the transformation of the world 16) let's see what the rest of the year has to offer and let's see how the labs begin to cross into domains with real world loops (like chemistry, biology, etc...)
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yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model. We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann algebras (disproof of Connes' Rigidity Conjecture) to better bounds for high dimensional sphere packing, for circuit complexity, for monochromatic triangles in multicolored graphs, and more. More thoughts here: openai.com/index/ten-advance…
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Cody Johnson retweeted
Exciting result: k-Coloring is in (2-eps_k)^n time, where eps_k>0 for every fixed k! Algorithms faster than the 2^n time needed to compute the chromatic number were previously known only for k<=6. arxiv.org/abs/2607.25973
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Cody Johnson retweeted
My friend Zhengqing used GPT-5.6 to solve a beautiful well-known conjecture in probability. Feige's 1/e conjecture: for independent nonnegative X_1,…,X_n with E[X_i] ≤ 1 and S_n = X_1+⋯+X_n, we have P(S_n ≤ E[S_n] + 1) ≥ 1/e. Exciting to see a problem we used to kick around over lunch and dinner get solved! 1/2
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Cody Johnson retweeted
The KLS constant is O(log^1/4 n)! arxiv.org/pdf/2607.24164. Speaking informally, the KLS conjecture says that the best way to cut a convex shape into two equal volume halves so as to minimize the newly exposed surface area is more or less just a straight cut. The KLS constant measures the gap between the best arbitrary cut and the best straight line cut. Kannan, Lovász, and Simonovits conjectured that this term is O(1). About two weeks ago I had a breakthrough on some adjacently related work, and I had noticed that it would be quite fruitful to study the KLS conjecture through something in convex geometry literature known as a moment measure after I had discovered an interesting formula. This moment measure satisfies a second order PDE known as a Monge–Ampère equation. I used ChatGPT 5.6 Pro to differentiate this equation, and then after a lot of experimentation, ChatGPT 5.6 Pro produced a striking result, which after a some extra work gave what is now Theorem 2.5 in the paper. At the time of uploading this paper, it seems that independently Yuansi Chen and Boaz Klartag had used ChatGPT 5.6 Pro to produce a slightly weaker version of Theorem 2.5, which still is of course the main breakthrough, see here: weizmann.ac.il/math/klartag/…. Quite remarkable!
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Awesome! GPT 5.6 just solved a nearly 70 year old conjecture (Erdős #119) As @thomasfbloom writes: "This resolves the strongest form of the question using an argument only a page long, that uses completely standard harmonic analysis. "The second, weaker question, was resolved by Beck in a 44 page Annals paper, using a proof which was much more analytically involved. "It seems that this was a question where actually attacking the more difficult question makes the question easier somehow - "although going from a 44 page Annals paper to 1 page of easy harmonic analysis is a very dramatic jump!" Indeed, this echoes my feelings when GPT solved Erdős #1196: I was initially skeptical given the short length, then eventually elated to see a "Gordian knot" cut
Replying to @thomasfbloom
A fascinating proof to read (see my comment for an exposition), and a great example of 'we thought this problem was a lot harder than it was'! (With echoes of problem 1196, previous work took a different route which involves substantially more technical difficulties.) 4/7
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Cody Johnson retweeted
toly 🇺🇸
@tolyJul 16
Seeing how fast fees get competitively reduced in crypto makes me think that most of TradFi revenue is regulatory capture.
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