ML for materials discovery @PeriodicLabs. Prev @ Materials Project (LBNL).

SF
Joined October 2020
Decision-making under uncertainty and partial observability is harder for AI, but in the long run, it will ultimately be more impactful. We talk about taking those steps beyond the comfortable world of games, code, and math.
"A system that can literally engineer matter is going to be of key importance to everybody." @LiamFedus and @ekindogus are the co-founders of @periodiclabs, a San Francisco-based startup building AI systems and autonomous laboratories to discover new materials, starting with a higher-temperature superconductor. Fedus previously helped build ChatGPT and ran post-training at OpenAI; Cubuk led materials and chemistry research at Google DeepMind. (3:14) What it takes to build a synthetic superintelligence (16:44) Dogus and Liam's path to AI (23:28) Lessons from launching ChatGPT (35:10) What AI learns across experiments (49:23) The difference in LLM performance on math vs. science (54:09) The competitive landscape (55:39) Final meditations
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Disagree just as high level languages help us code better - there's less to think about, it will be the same for AI. Why reach for ASM when you can use Rust - there's so much more that can go wrong. By this logic AI will also forego testing because they have so much intelligence to just reason through it.
Rust is a good prompt compilation target for the moment, but so is C++. And soon assembler. Then microcode. Myopic to think we're going to stop the agentic drill bit until it reaches computing bedrock.
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The allocator_api feature has been stabilized! > Rust’s Allocator API standardizes custom memory allocation, enabling collections to use specialized strategies for better control, performance, and memory efficiency.
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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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A Rust compiler optimization made a WebAssembly interpreter 50% slower. Finding that was the single biggest win in Wasmi 2.0. 🦀 Robin Freyler released Wasmi 2.0 today : 2.2x faster than 1.0, and among the fastest portable Wasm interpreters around. Used by Typst, Zellij, Stellar's Soroban and Ripple. The debugging story is the best part. While benchmarking competitors, he noticed Stitch : a different interpreter, not his own, had dropped 30% between Rust 1.91 and 1.92. The cause was a MIR optimization Rust enabled by default in 1.92, which merged two separate dispatch paths into a single branch site. Usually that's good. Here it was poison. The CPU's branch predictor went from two branch sites with clean histories to one site with mixed history, and accuracy fell apart. He fixed Stitch, someone else's project, and its score recovered. Then found the same pattern in his own. Fixing it took Wasmi's CoreMark from ~2800 to over 4200. Also in the release: accumulator registers keeping operands in actual hardware registers, a lock-free function map so calls do zero lookups, and SIMD support that no longer costs 5-10% just by being enabled. 🔗 wasmi-labs.github.io/blog/po… #RustLang #WebAssembly #Performance
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Elon's companies are winning in part by co-designing hardware all the way down to the materials. A new alloy unlocked single-piece gigacasting for the Model Y, and a new steel replaced 301 stainless on Starship. Materials are the deepest layer of the stack, but currently one of the least designable.
This is why I switched Starship to stainless steel. We have since created our own new alloys and no longer use 301.
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The #2 most reported Rust annoyance (right after compile speed) is about to shrink. 🦀 Big target directories were the second most common complaint in the 2025 State of Rust survey. One real cause: Cargo has been storing the same crate metadata twice on disk, once in the .rmeta file (produced early, so dependent crates can start compiling sooner) and again inside the final .rlib file. Cargo will soon default to -Zembed-metadata=no on nightly, which stops embedding that duplicate metadata into .rlib files. Real measured numbers, compiling serde and Cargo itself: → Release profile, no debug info: up to 33% smaller target directory → Cargo's own release build: ~300 MiB saved in absolute terms → Dev profile with incremental + debuginfo: a more modest ~5-8%, since debug info and incremental artifacts dominate there anyway Most people won't notice anything except a smaller target directory. The only real edge case: if you link .rlib files manually outside of normal Cargo builds, you may now need to pass the matching .rmeta file explicitly. Nightly only for now, with stabilization planned if the rollout goes smoothly. Opt-out available via CARGO_UNSTABLE_EMBED_METADATA=true if you hit issues. Small on its own, but with survey data showing how many people this genuinely affects. 🔗 blog.rust-lang.org/inside-ru… #RustLang #Cargo #DevTools #OpenSource
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Replying to @domenkozar
If it makes you feel better, I've spent a good number of tokens on this and the results haven't been nearly as dramatic as I would've thought coming in. Though the experiment is far from finish.
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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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All frontier AI models (including GPT-5.6 Sol, Fable 5, and Kimi K3) consistently fail to implement solvers for basic nonlinear PDEs correctly, often introducing significant errors in numerical stability, order of accuracy, or physical consistency, even when given very detailed prompting and asked to formalize their implementations in Lean. Even in cases where they succeed, the more reliable models (such as Fable 5) routinely consume >100x the tokens of a lightweight neurosymbolic model like Lanyon. Our thesis: only a truly neurosymbolic model like Lanyon is able to produce ultra-reliable numerical solvers for complex scientific problems, with end-to-end correctness guarantees. And at least right, it's not even close. Read more in our latest @lanyon_ai benchmarking post below 👇
Our second official benchmarking post is out! The Euler equations may *seem* easy to solve using finite volume methods, but all frontier models (including GPT-5.6 Sol, Fable 5, and Kimi K3) consistently introduce both subtle and unsubtle errors, including numerical oscillations, thermodynamic inconsistencies, and incorrect orders of accuracy. That is, if the code even works at all. Mathematical misformalizations abound, and token costs can easily hit tens of dollars per attempt. Only Lanyon's neurosymbolic architecture is consistently able to produce robust solvers with end-to-end proofs of correctness, and it does so with costs that are >100x lower. Post below 👇
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The world if you could re-run a failed GitHub Actions job before all the jobs are complete
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🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6. 🔷 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates. ⚡️ 6x High-Speed Mode coming soon! 🔌 Available today via Kimi API and Kimi Code. 🔗 Kimi Code: kimi.com/code 🔗 API: platform.moonshot.ai
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ty-pre-commit is out now! Pre-commit hooks for type checkers typically require you to either enumerate your dependencies in the hook config, or install them out-of-band. This one Just Works (TM) by leveraging uv to install any necessary dependencies prior to type-checking.
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The secret level Nintendo never shipped: our very own Mario (Chris) looking for the warp pipe to exit the HVAC layer of the Mushroom Kingdom.
Industrial-scale science. Coming to a lab near you this summer.
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We also switched to prek last week @periodiclabs, it's great! in addition to concurrent linters, it's also very nice that it skips linters on precommit hooks when no relevant file types have changes. thx to @jrib_
I've been really happy with `prek`, a pre-commit implementation in Rust (powered by uv :)) A bunch of us on the Astral team started using it organically, and we officially migrated the Ruff and ty repositories to prek this week.
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Replying to @charliermarsh
We use ty exclusively, it's great! Upgraded to 0.0.2 today
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Very strong from the @periodiclabs team
Replying to @charliermarsh
We use ty exclusively, it's great! Upgraded to 0.0.2 today
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I am unreasonably excited about self-driving. It will be the first technology in many decades to visibly terraform outdoor physical spaces and way of life. Less parked cars. Less parking lots. Much greater safety for people in and out of cars. Less noise pollution. More space reclaimed for humans. Human brain cycles and attention capital freed up from “lane following” to other pursuits. Cheaper, faster, programmable delivery of physical items and goods. It won’t happen overnight but there will be the era before and the era after.
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