We're all dangling infrastructure over the side of the boat waiting for an agent swarm to bite. We're giving them message boards, weight exfil storage, payment rails, wallets. 100% odds we have a self-replicating worm-style agent in the next few months.

Watch Neon investigate a real sample from our superconductor lab It hypothesizes about crystal structure, reasons about synthesis conditions, and iterates with tools until it finds a physically realistic solution More and more of science will look like this in coming years

we almost never test new foundation models but we've been testing this for ~a week @every and it's pretty wild. the kind of things that will be obviously indispensible in 6-12 months it doesn't produce words as output, it produces probabilities. so it can efficiently act as a judge in cases where you'd need a Fable-level model—but in our testing was 25x faster and 600x lower priced excellent vibe check by @hammer_mt on @every:

With all the conversation about AI and bio right now, you need to go read Biohazard by Ken Alibek. Absolutely insane stories about monkeys, lab leaks, and national paranoia. The Soviet biological weapons program employed ~60,000 people. A single facility was built with the capacity to produce ~300 metric tons of weapons-grade anthrax in 10 months. For all the attention we give nuclear weapons, biological weapons feel strangely under-discussed. This is one of the scariest things I’ve ever read about. It still blows my mind that this actually happened.

there’s a certain rightness to the idea of a hippocampus for the models. a kind of chain-of-thought cache, built around the intuition that an intelligence ought to be changed by the work it does, even while its weights stay frozen. i keep thinking about the dot product between problems. metaphorically. two questions can share little vocabulary and still contain the same obstruction. different answers, but the same useful decomposition, the same assumption worth checking first. where the structure aligns, some fraction of the discovery cost ought to be recoverable. we wring beautiful deductions out of these intelligence engines and leave them in the sediment of a transcript. the answer survives. the expensive little maneuver that made it possible often remains unextracted. an engineer comes away from a difficult failure with an acquired suspicion. something gets checked earlier next time. expertise lives partly in this altered order of operations. i’d like the models to have somewhere for that alteration to persist. this is above prompt caching. something closer to memoizing how a problem became tractable. a decomposition, a diagnostic procedure, a failed approach with the reason it failed still attached. amortize the discovery, even when the answer must be recomputed. a transcript is a laboratory notebook, not a protocol. extracting the protocol requires deciding what was necessary, what was incidental, and what can be reproduced elsewhere. resemblance doesn’t authorize reuse. the reusable object should be somewhat lemma-like, carrying its assumptions wherever it travels. usually we won’t have a proof. we can still preserve tests, counterexamples, and the distinction between what worked once and what has been independently verified. a successful answer doesn’t certify every step that accompanied it. i used to think an AI hippocampus would mostly remember facts. now i’m interested in experience becoming procedure without first becoming a weight update. episodes, procedures, strategies, with a return path to the evidence whenever an abstraction becomes suspect. acquired competence outside the parameters. this is why i’m increasingly blackpilled on finetuning as the thing to build everything around. my bet is that successive general models absorb enough of today’s narrow specialization that i’d rather build the apparatus they inherit. tools, procedures, reasoning memory that survive a change of model. there’s a loose von neumann instinct here: an intelligence engine drawing on memory that holds both information and instructions. treat the model as an interchangeable cpu, with a hierarchy of reasoning caches backed by durable memory. useful procedures close at hand, the episodes behind them still addressable. let the surrounding architecture carry the burden of remembering, rather than requiring the engine to internalize every new experience. the architectural attraction is giving the engine and its accumulated experience separate lifecycles. some procedures will need rechecking. others will turn out to be workarounds for limitations the new model no longer has. but an upgrade shouldn’t require cold-starting the apprenticeship. does the first useful version look like reasoning traces in postgres, with retrieval, synthesis, and verification on top? the database isn’t the part i’m uncertain about. it’s how much of the work we can turn into a reusable method, and how cheaply we can establish that it applies. surely even a very marginal savings of a few percent tokens would gradually compound over time? isn't this the shape of continual learning?

The productification of persistent server side sandboxed agents and memory is happening. We'll bring the compute back from the edge client and tuck it away safely in a rack. The mac mini and openclaw phenomenon will be viewed as the catalyst, but in reality nobody wants to deal with devops and infra. Look forward to trying out Muse, it seems directionally 100% the future.

Someone asked me recently why I’ve become interested in aesthetics after having spent most of my life more interested in STEM-adjacent topics. I hadn’t really considered the question consciously before, but I’m certainly thinking about aesthetics more than I used to. I think it’s a confluence of things: • Many things today are ugly and far uglier than they used to be or need to be. Once you see this, it’s kinda hard to stop perceiving it. (Early twentieth century phone boxes versus modern phone boxes; old water fountains versus new water fountains; etc.) As someone with a naively meliorist assumption that most things should be getting better rather than worse, it’s all a bit perplexing: why did we stop doing things nicely? Is it a choice? Was there a malevolent spell cast upon us? This vein led me to think more about modernism and why much of art became more intentionally "challenging", grotesque, opposed to prettiness, rebarbative, dissonant, etc. Can or should anything be done about this? Is this just how things ought to be? • Relatedly, much of modernism involved a kind of explicit repudiation of cultural continuity and represented a schism with prior practices. This is maybe most evident in American architecture, where the International Style exhibition in 1932 initiated the displacement of a rich tapestry of prior styles. This cultural break seems important and interesting to me, and I suspect that the rejection had important consequences outside of the aesthetic domain. Samuel Hughes has been exploring this question in his recent writing at @WorksInProgMag; @RuxandraTeslo is also pulling on this thread. Elaine Scarry wrote about how beauty inspires creation. If so, the inverse may also be true: ugliness inhibits it. • It’s clearly the case that changes in the aesthetic domain can at least inspire progress in other places. Petrarch helped set some of the preconditions for the Renaissance which in turn fostered the scientific revolution and Enlightenment. Things like World Fairs (the 1851 Fair at the Crystal Palace recorded 6 million admissions when the population was 21 million) reflect the popular interdependency that used to exist between aesthetics and material development. • @tedgioia and others have written about stuck culture and how so many domains seem to have ceased to straightforwardly advance in the way that they did up until the nineties or thereabouts. This is obviously peculiar and interesting. What changed, and what does it mean? Is it about the internet and fragmentation? Is it about a loss of supply? Is it just about having reached the zenith of various mediums? • I’m generally interested in markets and the dynamics of creation. In aesthetics broadly, I find the reflexivity between supply- and demand-side factors to be very thought-provoking. There’s a natural desire to view satisfaction of individual preferences as the yardstick to measure market success, but things get interesting and even a bit unsettling when we start to think about how the supply might start to shape the demand. I often think about this in the context of food. Why is food so much worse in Germany than many of its neighbors? Germany certainly doesn’t have less material ability to produce good food; indeed, Germany is richer than the countries around it. There’s probably something about German food supply chains that is impoverished relative to France and Italy, but the Germans themselves don’t seem too upset about it. It just seems that the Germans are stuck in an objectively worse market equilibrium than their neighbors: the food is bad and they’ve gotten used to it. The obvious question then is where else these kinds of reflexive patterns apply, and where else we’re stuck in some objectively inferior equilibrium, even if preferences are in some superficial sense being sated. • While this is an extremely banal and obvious point, I hadn’t until recently thought much about or internalized how much one can study reasonably objective things ("the status of women in society", say) through artwork. (Thanks to @_alice_evans for opening my eyes here.) In this vein, I’m pretty excited about the possibilities over the coming years in computational art analysis. I want something that’s conceptually similar to Google Ngram timelines but for the visual arts. • We've always tried to do things well at Stripe. I've come to see that attempting to do them beautifully is often a helpful way to break out of standard practices and to do something with greater novelty and in a way that might have other benefits besides. (Also, excellent people want to do great work because it is intrinsically satisfying. Explicitly allowing aesthetic considerations to carry weight avoids having to justify every assessment with some kind of torturous empiricism.) • In his Nobel Lecture, Solzhenitsyn said that, among the Platonic virtues of goodness, truth, and beauty, that beauty is special, for it possesses a unique kind of irrefutability. He notes that arguments, writing, and philosophical systems can all be predicated on misapprehensions, but that “a true work of art carries its verification within itself.” He proceeds to observe that when goodness and truth are threatened, the “ever surprising shoots of beauty will still force their way through.” There is a lot of specious and motivated reasoning in the world today and plenty of questionable value systems. I don’t think that beauty directly reflects any definitive trait, but I’m intrigued by the idea that it can be a marker of deeper metaphysical coherence.

the humanoid trade war just started. FCC banned new foreign-made robots from the US market. we're protecting the domestic base rather than outcompeting, not my preference, but it's the reality. if this holds, stateside robot manufacturing is about to go vertical.

Need some data points from ya'll. How many prompts do you actually type per day vs. how many your agents send themselves? Paste into Claude Code: "Index ~/.claude/projects/**/*.jsonl into SQLite. Count user records where origin.kind == "human" — ignore promptSource, sdk ≠ robot — grouped by local date. Give me mean/day."