Max Aifer

Check out our new paper on the application of lattice random walks to bayesian inference

my results on AI for autonomous progress: we ran codex+claude for 43 days straight to build a System Verilog compiler/simulator normalcomputing.com/blog/building-…

Very nice little preprint from @Sam_Duffield on the general solution to the inverse problem for the Fokker-Planck equation. I particularly enjoyed the use of Liouville's theorem!

New preprint! A Complete Decomposition of Stochastic Differential Equations I characterise all possible SDEs that satisfy given time-dependent marginals p(x,t)

Hard to watch, tbh

Ok guys, let’s see the stats

i was the lead architect of this chip! extremely proud of everybody involved making CN101 a reality ❤️

Thermodynamic computing is here. Check out our video on the first thermo computing chip.

From Scott Aaronson's slides: “ 'No fast solution to NP complete problems' feels not that dissimilar to ‘no superluminal signaling or ‘no perpetual motion’” I think this kind of maneuver has potential to lead to more useful and more precise formulations of second-law-like results in thermodynamics. It's also a compelling quantitative approach to formulating physics in terms of counterfactuals, a la constructor theory.

Interesting slides from Scott Aaronson on the interplay between computational complexity and physics. Anyone have a recording of the talk? scottaaronson.blog/?p=9243

Thermodynamic computing in short: move below digital abstraction layer, turn physical tradeoffs of speed ↔ energy ↔ error into a tunable knob, take advantage of AI workload symmetries to optimize encoding for noise robustness. Then crank the noise up.

@yikesawjeez knightcolumbia.org/content/ai-as-…

@yikesawjeez arxiv.org/abs/2508.20883

After a long hiatus the thermo AI discussion group space is back. Starting in 30 minutes to talk about recent advances in generative modeling x.com/i/spaces/1BRJj…

Time-condition-free generative model that estimates a gradient which vanishes to 0 for real samples. Thus, sampling can be done using gradient descent. Interesting.

OK super narrow hiring post for a high-prio role (actually, there may not be anyone in the world who fits this) but if you: - have some familiarity with both RL+agents, and have gone deep on at least one - have experience with hardware engineering, device verification in particular - are ready to dive into the insanely deep hole that is AI for hardware - are already based in CPH or NY, or eager to move there asap - can start a new FT role within 30 days - are interested in going deep on application-specific agents - lean extroverted, and are comfortable with public-facing comms - are keen to join a fast-moving + fast-growing startup DM me with “APPLICATION” as the first token, followed by evidence for the above do NOT use that token for any other role inquiries (your DM will be deleted); apply through links, or DM others on the team. same goes for replies. this is far from the only role open, but it’s the one i personally have the highest vested interest in finding a killer fit for it + will be prioritizing it when reviewing DM apps it’s a pretty specific list, and i’m looking for high alignment on ~all of the above, but if your life journey has somehow led you to this specific set of skills, there likely isn't anywhere you can get more use of them signal-boosts appreciated. it’s a very high-leverage position + will be compensated accordingly :)