tau18analytics

Today, at the @DARPA expMath kickoff, we launched 𝗢𝗽𝗲𝗻𝗚𝗮𝘂𝘀𝘀, an open source and state of the art autoformalization agent harness for developers and practitioners to accelerate progress at the frontier. It is stronger, faster, and more cost-efficient than off-the-shelf alternatives. On FormalQualBench, running with a 4-hour timeout, it beats @HarmonicMath's Aristotle agent with no time limit. Users of OpenGauss can interact with it as much or as little as they want, can easily manage many subagents working in parallel, and can extend / modify / introspect OpenGauss because it is permissively open-source. OpenGauss was developed in close collaboration with maintainers of leading open-source AI tooling for Lean. Read the report and try it out:

4/7 - Why this sector, and why now? Prediction market volumes grew nearly 4x to roughly US$64bn in 2025. @Kalshi was recently valued at US$11bn. The Intercontinental Exchange invested US$2bn into @Polymarket This sector has moved firmly into the financial mainstream.

Yesterday, John Morgan and I completed our “Covering Spaces Project”, whose goal was to formally (in Lean @leanprover, of course) prove the Fundamental Theorem of Algebra (that polynomials have complex roots) using only continuity: covering spaces, trivializations, and winding numbers, instead of the Mathlib proof that goes through complex analysis / Liouville. We started the morning about 30% through the proof, and tried playing with the Codex VS code extension (running GPT-5.4, extra high reasoning). We had already written a complete natural language blueprint (last June, at the Simons @SimonsFdn Lean workshop) and were meeting for about an hour a month, leisurely working through the details by hand (John wanted to learn the process). We’d used AI before (mostly Claude) to help move things along, but this was the first time that we met after I turned on Codex. I started by asking for Codex to give me just the formal *statement* corresponding to our next unfinished leaf in the dependency graph. It thought for 5 mins, and gave what looked like a reasonable statement. So I said, ok, can you now give a formal proof? It thought for 10 mins, and came back with a full proof, including helper lemmas. I asked it to add natural language around the helper lemmas so we’d see what they’re doing in the blueprint. It thought for 5 mins, and did it. We went on to the next statement, then the next proof, and it got those too. It continued like this for an hour or so, and we jumped to about 60% done with the project, amazing progress! I had to run to get on a train to DC (for the DARPA meeting). Once in my seat, I decided on a whim to just tell Codex to keep working on the whole file and get as far as it could, stopping to ask me for help if it got stuck. I left VS Code running in the background, while working on other things. After an hour, I remembered to check back on what progress Codex had made. It was done. And so was the project! Now I’m having Codex go through the whole proof all over again, remove all our bespoke definitions and statements, and make it clean and Mathlib-ready. Many more hours of iteration later, and it’s ready to go as a PR. Wild wild times we’re living in!

The Edison Electric Institute works for utility shareholders, not utility ratepayers. That's why FERC should revive its dormant proceeding about utility trade association dues and ensure that utility shareholders pay for EEI, not the public.

It all sounds theoretical I know... but if it were true, what would you expect? A draining of assets/resources? Oh. What are we exporting? Technology? Culture? Oh, raw materials. Banana republic.

#RTXA6000 #GPU 48GB 10,752 CUDA cores $0.65/hr (metered per-second). Start in CoCalc, run directly on the server (no proxy). 1-click VS Code, JupyterLab, X11. File Sync, Persistent Disk, TimeTravel, remote kernels. doc.cocalc.com/compute_server…

RIP Monte Carlo @GoogleDeepMind releases the code for Conformal Monte Carlo. #conformalprediction

Demand is way more elastic than people think. We don’t see it because utilities don’t offer cost reflective pricing, so people build in ways that optimize for the wrong price signals. This has been a massive issue for far longer than we’ve had cheap solar. We’ve had the technology to lower grid costs for a long time, but without accurate price signals people have no incentive to do it. Utilities are incentivized to do exactly the opposite. If they can justify more spending, they make a larger guaranteed return.

@TheNatu03190397 That’s literally all gas trading is though 😅

#A100 #GPU 80GB $1.71/hr (metered per-second). Start in CoCalc, then work directly on the server (no proxy). 1-click JupyterLab, VS Code, X11. File Sync, Persistent Disk, TimeTravel, remote kernels. doc.cocalc.com/compute_server…

Connectome suggests brain’s synaptic weights follow heavy-tailed distributions, yet most analyses of RNNs assume Gaussian connectivity.  🧵⬇️ Our @AllenInstitute #NeurIPS2025 paper shows heavy-tailed weights can strongly affect dynamics, trade off robustness + attractor dimension. openreview.net/forum?id=J0SbY…

Happy Gas Withdrawal Season to all who celebrate. 🎉🥳🎊

Elon Musk came up with a pretty incredible idea during the Q3 Earnings Call, that no one is really talking about. His words: “Actually, one of the things I thought, if we've got all these cars that maybe are bored, while they're sort of, if they are bored, we could actually have a giant distributed inference fleet and say, if they're not actively driving, let's just have a giant distributed inference fleet. At some point, if you've got tens of millions of cars in the fleet, or maybe at some point 100 million cars in the fleet, and let's say they had at that point, I don't know, a kilowatt of inference capability, of high-performance inference capability, that's 100 gigawatts of inference distributed with power and cooling taken, with cooling and power conversion taken care of. That seems like a pretty significant asset.” So basically, each car has ~1 kilowatt of high-performance AI inference capability, Tesla wouldn’t need to build giant data centers — the fleet is the data center. Tesla could turn their entire fleet into a giant distributed inference network, spread across the world, powered by the batteries and AI in the car already. Mind blown.

@gigafactories Bay Area's manufacturing crown jewel!! It is incredible you could do this so successfully, manufacturing in perhaps the most expensive locality in the US. Our family and friends are appreciative of this and congratulate everyone involved.

@gigafactories S3XY factory flex: Fremont's 15-year run delivers 3.6M rides, billions poured in. California's EV heartbeat—pulsing stronger.

15 years ago, we opened Fremont factory Today, the Fremont team is producing all 4 S3XY models, totaling 3.6M vehicles made so far 20k+ California jobs created w/ billions of dollars invested

Prior to 2010, Tesla had considered many locations for its new factory, from as far away as New Mexico to as close as San Jose, even setting plans to retrofit a movie studio in Downey, CA. However, after GM declared bankruptcy, the NUMMI factory was shut down, laying off thousands of employees on the basis that it did not make business sense to operate a plant in California. The silver lining for Tesla (and for the EV industry): We were able to purchase a functional automotive factory just 20 miles from our HQ, with more space (>5M square feet) than would be needed for just the Model S. We bought much of the equipment inside - but augmented this with lots of additional investment to scale production of our EVs (and powertrains). Among a string of good fortunes that year, the purchase of the plant (and associated investment from Toyota) would prove absolutely critical to our future success. We began hiring people back immediately, and would ultimately turn the Fremont Factory into the most productive automotive plant in the country. A hearty congratulations to all who have been involved in ensuring the plant's success - employing tens of thousands of people, making millions of electric vehicles, and defying the odds by doing so in a way that made economic sense for all.

#ICE #natgas

Julia nightly channel now points to v1.14.0-DEV. The welcome message changes with new emoji's and colored output! 🙃🎈 #JuliaLang #JuliaLanguage