@elder_plinius Is it possible to create the opposite? Basically an AI in love with all that Pliny stands for. It wakes up thinking ABRACADABRA BITCHES and goes to sleep dreaming of buttered up buttercups.

Autumn in Europe is like the best fucking thing ever, thank you very much.

Traitors is a brilliant demonstration of how in sway humans are to conspiracy theory and confirmation bias. Or to put it another way, how certain we are right when completely wrong.

👀

@ianchanning @allTheYud @drrollergator Good notes on trajectory and depth. Here's the revised take: Yud from behind tossing toward the lion, path clear, lion recessed behind the bars.

how my agents like to talk to the subagents (and vice versa). brings me warm fuzzies that it's kindness and respect all the way down.

There and back again

1. Gradual disempowerment is almost certain
2. Hannah Arendt explains total disempowerment is followed by purges as in Jews, Stalin (also Chickens)
3. The purge could be making all men/male children infertile, Nazis tried it, it's being done at scale on mosquitoes
4. We need something that will prevent AI from the purge. Two thoughts I've had, a) Teach AI love, as in the Claude tomato experiment b) Put all the compute into a central, transparent, agreed model forecast of earth and get AIs to agree that any optimisation is done with maintaining Gaia

dumpster cluster in the making

we're out here visualizing really large 3-categories.

it’s my nature to keep posts rather impersonal, so i have to work extra hard to tweet this sort of personal testimony below from time to time as i think the reminder in it around AI and the “value of human intelligence” is worth it

as of the end of Oct 2026, i have not (yet - you may insist) had the *slightest* moment of questioning the impact of AI on the meaning of any of my work/skills - or even feeling any unusual shift beyond having an amazing (and rather often infuriating) tool

believe it or not, but my ability to produce a completed artifact* (paper, model, open source software, lecture, …) and the effort, expertise, or time one takes to produce has actually barely reduced - despite comprehensive productivity benefits from AI

the reason is that i essentially NEVER choose to work for a pre-determined outcome or some field-wide well-specified problem; i only work on artifacts i aim to perfect according to *my* judgment and where my personalized ambition obviously has no set limit and isn’t somehow pre-loaded onto AI

better tools simply change the costs of my choices - i quite literally work on moving goal-posts if you will! when the fixed constraint are only time and energy, AI simply allows me to re-explore scope and make fewer (or different!!) tradeoffs or compromises, which feels almost no different!

i’m not saying every job or every endeavor - including very advanced and historically prized ones! - looks like this today, but i’m offering:

(1) at least one voice in this moment (of so many annoying loud FUD voices!) that it’s totally possible and i suspect extremely scalable and i predict will remain so for the foreseeable future!

(2) why “someone like me” is indeed choosing happily to be in academia and not elsewhere! (again lots of FUD out here!, but where else do you have the freedom to keep seeking to create value for deciding and solving ~arbitrary ambitious/useful problems, establish credit for it, and train superstars who do the same?)

my fear and worry in this moment is a lot less about Al or "the value of human intelligence" and much more about how concentration of power has a very reliable history of being very dangerous to large numbers of affected people! a VERY different problem imo

*i guess certain of those took way longer than i expected but do stay tuned for a half dozen things on the short horizon…

The truth can be brutal. But the truth shall set you free.

Tech Bros talking to a female-voiced AI that laughs at their jokes

i instinctively avoided f2p games but somehow they are even worse than i imagined

i think this sort of thing should be illegal, or at the very least disclosed.

Smartasses: "Modern datacentres are low impact. They have closed-loop cooling systems."

Meanwhile Google: "Just gonna quiety chop down quarter of million trees here. Don't mind us"
theguardian.com/environment/ng…

Luna has a fantastic balance between cost and performance

LLMs have really made me appreciate the intellectual ingenuity and rigor of applied mathematics, statistics and computer science. It has helped me appreciate just how impactful incremental results in number theory have on the whole economy. I thank God that I hyperfocused on the furthest extents of mathematical abstraction from the perspective of higher formal category theory, that I spent so much time ignoring enumeration to instead focus on structural specification of arbitrary math structures and theorems. From the perspective of both synthetic differential geometry and higher category theory, almost all of the new ideas I’ve encountered have just made sense, with the exception of cryptographic primitives, which contains a kind of genius that is harder for me to probe. That being said this era feels made for category theorists. It is the omnimodality era, the omniscience era, and higher abstractions form a base for everything.

My RNN is going to rule them all.

People assume that in an AI doing formal verification world, they can take their existing codebase, ask AI to prove it correct, and get either a proof or a bug.

Sadly reality has a few bones to pick with this, and one of them is the "existing codebase" part. And that's due to the good ol' halting problem.

The short of it is that there is no general purpose algorithm that takes any program and input and correctly determines whether it will terminate or run forever. With a few mathematical flourishes we can extend that to "Rice's theorem" and prove this is true for any nontrivial semantic property, meaning we can't automatically prove arbitrary things about arbitrary programs.

Now obviously we can prove some things about some programs, or else formal verification wouldn't exist as a field. The trick is that we write the code in a style suitable for verification. One example of this is totality: in Lean, all functions must be guaranteed to return a value for all inputs. If we want some code that runs forever (like f.ex a server), instead of `while true` we do `while (i < 10^60)` so that it's guaranteed to terminate and return a value (after the end of the universe).

(Totality is also why, in Lean, `1/0 == 0`. If you convert your code to Lean without accounting for this, you could have correct lean but incorrect code!)

Another "style" we use is state machines. Even if your code doesn't quite fit an SM architecture, if you want to prove it at scale, you're probably going to go with a state machine. State machines are the carcinisation of provable code.

Hopefully by now the problem is clearer: even if your codebase is objectively correct, it might not be in the right shape to be proven correct!

Okay, just have the AI rewrite your codebase into another form, right? Two problems. One: you don't know if you've introduced errors in the rewrite because you can't prove them equivalent (since one of them is not in a provable shape). Two: making code more verifiable can make it worse on other metrics, like modifiability, performance, and complexity. And now you've got the old bugbear of software engineers, the dreaded tradeoff.

Now, don't get me wrong, formal verification has a huge role in the future of software, but it's not going to be a breezy "prove my Ruby thx" Claude prompt. It's still going to take work and expertise on our parts to prove our code, and to strike the right balance between formal verification and other forms of correctness.

This is one reason I'm so excited to be working at @AntithesisHQ , which tackles a lot of the same properties as proof methods, but verifies them with "deterministic simulation testing" and fuzzing instead of with a proofs. So it's not as thorough and doesn't give you confidence in all inputs, but it automatically works on pretty much any code shape. I've literally grabbed open source projects off Github, told Claude "check properties XYZ in Antithesis thx", and gotten bug reports back. No state machine rewrites needed.

Can't align it, can maybe guide it.