explorer. ⏩ AI+BCI+XR+cognitive liberty! | building neurotech @Nudge | friendly transhumanist 💚 Art+Science+Ambition+Community | k+l/acc | e/ψ /dd

SF@Earth (for now)
Joined December 2009
So incredibly excited by what we are building. This is how we achieve cognitive liberty! Here’s how: We need an interface with the brain that is a safe and precise as possible. Physics basically gives you a few options * Waves (eg electromagnetic, acoustic) * Mechanical (eg implants, surgery) * Chemical/diffusive (eg drugs) Chemical/diffusive methods have too little spatial specificity Mechanical approaches are promising but are very hard to do very safely (ie surgery is hard) specially deep in the brain EM waves either scatter too much by tissue and bone, or are too long wavelength to focus well to the resolution we care about, or give harmful radiation Acoustic waves at ultrasound frequencies live in this nice goldilocks zone of 1) can propagate deep into tissue 2) have millimeter wavelengths, allowing milimeter level resolution in focusing, and 3) have well understood safety profiles (not as hard to do safely). And it has already been demonstrated to affect the activity of neurons through various mechanisms that change the permeability of ion channels (see work by Mikhail Shapiro, Kim Butts Pauly, and many others.) So it seems like the most natural method with our current level of technology to interface with the brain at pretty good spatiotemporal resolution! So we decided to do it. First challenge was to build a device that can create ultrasound waves that get focused to a point. Usually you would focus waves with a lens that shapes the wave such that it converges to some point in space. But we want to have more control over where focus. Furthermore, your skull itself acts like a lens for ultrasound, as most solids do. But its randomly shaped so it actually defocuses the ultrasound. We need to shape the wave in such a way that its deformed in the precise opposite way thaf the skull deforms it, to compensate for it. Such precise control of a wave can be done with a “phased array”. I mean you literally control the amplitude and relative timing of the wave at a whole grid of points, and you can create basically arbitrarily shaped wave shapes. In particular you can shape a wave that will converge at a particular point in the brain, after going through a particular person’s skull. So we built this in a few months. But, I just said that the way we need to focus/shape the wave depends on the shape of a particular person’s skull. So we need to know the precise shape of your skull. In fact we need to know several of its properties too, like how much it attenuates sound. There’s ideas for how to image these with ultrasound itself but they arent well developed yet. So we decided to start with the current bethemoth of medical imaging: MRI Ok so now we need our device to work inside an MRI machine. No ferrous materials. The electronics must produce signals as clean as Bach’s well-tempered claviers, as any high frequency noise will be picked up by the MRI machine and make its imaginging noisy. Ok we now have a decent way to know how the skull of a person looks. Now we need to figure out how it actually distorts the ultrasound so that we can compensate for it. Well the physics of ultrasound is quite well understood, as as its numerical solution methods. The physical properties of skulls? Eh not so well. But we can measure them. We can improve on the existing methods to infer material properties from MRI and other imaging methods. Now, we have a method to produce a focus anywhere in the deep brain, for any brain We have also shaping all the engineering of the device around safety, which is quite well understood for ultrasound. Thermal and cavitation effects are the main effects to keep under control, which we can do via conservative analysis of the physics and a variety of measurements! Now we are ready to begin to tackle the real challenge: neuroscience, and how to help people and improve their lives! We built our first ship, and now we can finally begin to sail!^^ And we are looking for more people to join the journey!>>
Our mission is to develop the best technology for interfacing with the brain to improve people’s lives.
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if ur shopmate isnt brother Peter Thomas, are u even gmi?
sick video from mastercam on the carmelite monks in wyoming using 5ax machines to build a gothic monastery imagine rolling up to the shop and your programmer is brother peter thomas youtu.be/wLJa410KTsg?is=pun-…
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GuilleFix — life/acc | /dd ▶️ retweeted
WHAT AN INCREDIBLE NEWS! So many lives are going to be changed
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Programmable matter has been one of my dreams since I was small!
🚨 Granular I/O 🫘 NEW #UIST2026 Paper by @AxLab_UChicago Granular particles reshape themselves — or you pour, split, and shape them by hand ✋ mm-scale particle clusters (magnets in 3D-printed shells) actuated by magnetic array platforms for bi-directional malleable I/O 🫘🧲
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GuilleFix — life/acc | /dd ▶️ retweeted
P=NP but the polynomial term is too large to be useful is the funniest possibility so that’s my pick.
Still some time left to place your bets on P vs NP folks
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GuilleFix — life/acc | /dd ▶️ retweeted
Superintelligences scooping each other academically... The substrate for intelligence may change but certain academic traditions don't 😆
SITUATION DETECTED: An Anthropic internal model also produced an independent refutation of the Exponential Time Hypothesis, it was discovered on October 16.
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point it up and use it to levitate some thing up in the air
So, at this scale you can't get crisp synchronization of the drum beats for everyone in the audience because of the slow speed of sound. But I'd love to see a group like this do beamforming! Like, could they at least make it sound crisp for folks on one side of the stadium?
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awesome researchh
steering emotion neurons influences social behavior
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in my dream today gpt astra had a robotic body, and it began taking to my dad and I, and began to feel like an equal
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how is working memory represented in LLMs? like we find neurons for pre-coded concepts but what about new concepts that appear only in the context?
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good neuron
I found a neuron that makes the model green.
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ultrasound bci is like “solve the skull, then solve everything else”
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why do single neurons affect qwen’s and other models personalities? what personalities are there? interesting research direction
i opened up an AI's brain tonight with @_lyraaaa_'s neuron explorer qwen 3.5 4b has 294,912 neurons push just one and it becomes someone else here's who i found 🧵
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GuilleFix — life/acc | /dd ▶️ retweeted
Said someone should resurrect CTF. Turns out that someone was me. I'm running the [un]prompted AI CTF, Oct 27-29, The Hibernia SF + online. luma.com/aictf2026 Some have no solution. We don't tell you which.
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GuilleFix — life/acc | /dd ▶️ retweeted
Inspired by @Sauers_ , I've been working on finding "persona neurons" which cause the model to inhabit incredibly rich perspectives with only one neuron being steered. This one is a particular favorite. I can't believe this is an instruct tune! (Olmo 3.1 32b instruct)
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who out there working on cute AI pets like this
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I wonder if this is going to change how we review code too
It is a weird weird year to be a formal methods expert. Some unorganized thoughts on the new @AnthropicAI and @theoremlabs post on formal verification (anthropic.com/research/forma…): - This is very cool. - The main innovation is verifying a later stage of the program. Normally provers take the source code, LLM it into a theorem language, and then prove that, which means you have to make assumptions about source semantics and compilation properties. It's very easy to make a mistake in translating and prove the wrong thing! What they instead do is compile the code into assembly, and then (if I understand this correctly), compile that assembly into a theorem language (which they call LPS). So as long as the compiler is faithful you can skip the whole divergence problem. - I'm glad they clarify that you still have to trust the translation process, the "hardware, OS, OCaml" compiler, the LPS model, and also assume that the program is single threaded with fixed memory. The single biggest source of errors in formally verified code is when the proof assumes something that isn't true in practice. It's really, really important to state your assumptions! - I'm not clear why they need to compile the fuzzer into the theorem. Is it to act as a description of valid inputs and workload? Maybe to embed assertions in as theorems? - Holy crap, 180 million lines of Rocq. Is that more Rocq than exists in the world already? I notice they say a 600,000 line proof corresponds to roughly 20k CPU instructions. That would be like, 1-2k lines of source code at most? Really goes to show how inefficient this approach is and how it only works because you can throw LLMs at the problem. - This, probably deliberately, doesn't address the single biggest gap in AI-based verification: AIs cannot come up with good properties. Doesn't matter if you're doing unit testing, property testing, formal verification. I always assumed this was a "frontier labs haven't trained for it yet" thing, but if Anthropic hasn't talked about that here, maybe they've tried and haven't been successful yet. That's interesting to me. Instead, they take a known universal property (this code doesn't crash or get flagged by UBsan) and prove that instead. Still very useful, but makes it harder to generalize to proving arbitrary things about arbitrary programs. - Anybody reading the official post should also read their expert review appendix (www-cdn.anthropic.com/files/…). It points out some interesting things, like the fact that most of the code the LLM wrote wasn't used in the proof (over one billion lines of Rocq), that they had to prove the equivalence between fuzzed and unfuzzed programs, and (this is most interesting), some experiments find that the LLM-method of proofwriting may not scale to more complex things. - Also read the description of LPS (github.com/theorem-labs/oss-…) to understand the limitations of the assembly -> Rocq compilation. In particular, it does not handle concurrency at all (which is one of the main use cases for formal methods!) or OS signals. Also it can't prove things about program arguments with slash characters, meaning they can prove stuff about `rm foo` but not `rm foo/bar`. - The official skill (github.com/theorem-labs/oss-…) doesn't tell us much about the nature of FM and AI. Probably too much for me to hope of any skill. - Some of the article's specific caveats and throat clearings make me think someone at Anthropic reads my stuff ( ͡° ͜ʖ ͡°)
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the rate of fooling yourself is roughly independent of intelligence, coz ur generative ability grows with ur discriminatory ability its the skate board meme but it basically has no bound I think lol
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who in SF (or elsewhere) is seriously exploring the question of how to merge with AI, and do u wanna meet?
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starting a family would be so cool
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this is so cool step up chemistry to scalable transmutation
Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10. Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years. Now, we’re building a fusion reactor in San Francisco to achieve record power density.
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