Space cowboy riding a massive rock across the galaxy, wrangling genomes and herding data. PhD in Biochemistry @TAMUBCBP. Working @LanzaTech.
Chicago, Illinois
Joined October 2020
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Dan Browne retweeted
Beyond thrilled that the first paper from our lab is out today in @nature! We show that SWI/SNF loss results in gain-of-function for CTCF and cohesin, reshaping the 3D genome, and an unexpected switch between chromatin remodeling complexes.
nature.com/articles/s41586-0…
1/n
Dan Browne retweeted
1/4
Meet Qwenputer: Qwen3.5-4B with a full RISC-V CPU inside its weights, using the model's own Gated DeltaNet layers as RAM.
It runs C and MicroPython in its forward passes, no external runtime.
On DeepMind math: base 55–90%, base + Python tool 95–100%, Qwenputer 97.5–100%.
Dan Browne retweeted
I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data.
Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less?
Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.
Dan Browne retweeted
Surprise 1: Anthropic acquired a one-person biotech
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Surprise 2: that person is me
Surprise 3: I’m here to tell you all about it
Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post
…and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline
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Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere.
The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases.
Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat.
In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally.
Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants).
In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model.
From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team.
Why join Anthropic? Three reasons.
(1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work.
(2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter.
(3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing.
Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit.
Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest).
Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI.
Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have.
It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way.
But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember...
Gotta Cure 'Em All
Dan Browne retweeted
The names that built American manufacturing are iconic. Slater, who brought the mill. Lowell, who integrated it. Carnegie, who scaled steel. Ford, who set the line in motion. Deming, who taught the world quality.
The forces of production that once built America are rebuilding America again. This is one of the most important economic and national security imperatives of our time.
General Catalyst has backed @ReBuildMFG since 2024, and today we are co-leading their $2.1B round alongside JPMorganChase’s Security and Resiliency Initiative.
Dan Browne retweeted
Today we're coming out of stealth, introducing Neywa Labs 🚀
We built the infrastructure to build and deploy AI that runs on the devices people already own. Private, offline, unlimited.
One mission: make AI available to everyone in the world.
neywalabs.ai/
Dan Browne retweeted
We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.
openai.com/index/building-sc…
Dan Browne retweeted
Many of you don't understand how serious this is, so let me break it down.
In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force.
For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew.
About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea.
They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science.
Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption.
Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean.
And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea.
So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited.
Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved.
But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.
Dan Browne retweeted
Panagram reveals pangenome architectures underlying trait variation in plants and animals biorxiv.org/content/10.64898… powerful k-mer based pangenome approaches from Mike Schatz and Katie Jenike & colleagues
Dan Browne retweeted
This is going to be a big week, in terms of releases.
First up: Cherimoya CATv1 is now in proto-tools (github.com/evo-design/proto-…), making our ~7.5k DNase+ATAC models available for design using this extremely cool tool. Thanks @benviggiano!
Dan Browne retweeted
PCBs made in America. Twice as fast as Shenzhen.
Today @CircuitHub is unveiling The Grid, our factory-scale robot in Massachusetts that builds circuit boards on demand.
No changeovers, no per-design setup. Prototypes and production batches, fully automated.
Dan Browne retweeted
Final version is out:
onlinelibrary.wiley.com/doi/…
"Reservoir Computing for Pattern Recognition and Time-Series Prediction Based on Nanofluidic Negative Differential Resistance"
Dan Browne retweeted
I've been meaning to share these thoughts for a while and touched on them briefly in a recent conference talk. They are relevant to a lot of the discussions today.
Dan Browne retweeted
Look at this 33B monster LLM! 👀
🚀Excited to announce Nanbeige4.3-33B!
To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first.
Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters:
* Strong results on agentic coding and cowork benchmarks.
* When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference.
* It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B.
huggingface.co/Nanbeige/Nanb…
huggingface.co/Nanbeige/Nanb…
#LLM #Agent
Dan Browne retweeted
Everyone who works in “AI or Techbio” should be forced to shadow in a lab for at least 1-4 weeks to see what the next steps after you have a structure on your computer.
And that’s just boring brute force research, not exploratory research with new hypothesis.
Dan Browne retweeted
There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused.
The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc).
The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays).
Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false.
This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology.
Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions.
Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible.
This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use.
How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months.
I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.
Dan Browne retweeted
Ever wonder if eventually cells could be trained out of disease states? We're on the path; first steps:
New #preprint - @WesleyPClawson
"Goal directed closed-loop training of network bursts in cortical cultures produces improved performance and error correction over days"
biorxiv.org/content/10.64898…
Dan Browne retweeted
🧬 We have been using 𝗔𝗜-𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 to rethink foundational methods in molecular evolution from first principles.
🚀 Our new preprint introduces 𝗥𝗵𝗶𝘇𝗔𝗲𝗼𝗻—a new paradigm for recombination analysis that replaces discrete tree searches and combinatorial triplet tests with 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 in linear O(NKL) time.
📄 Preprint: biorxiv.org/content/10.64898…
📖 Blog & 60-sec slides: spond.github.io/CV.js/blog/2…
🌐 Browser app: veg.github.io/rhizaeon-code/
💻 Code: github.com/veg/rhizaeon-code
⚙️ 𝗛𝗼𝘄 𝘁𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝘄𝗼𝗿𝗸𝘀:
🧭 𝗠𝗲𝘁𝗿𝗶𝗰 𝗠𝗮𝗽 & 𝗥𝗼𝗼𝘁 𝗦𝗶𝗻𝗸: K reference genomes form a fixed metric map with an unpolarized root sink at the origin. Branch-specific rate bursts flow into the sink, holding false positives at 0.58%–1.01% across 333,334 clonal genomes in a 1,000,000-alignment sweep.
🎯 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗮𝗽𝘀 & 𝗚𝗵𝗼𝘀𝘁 𝗔𝘅𝗶𝘀: Each sequence is tracked along the chromosome via soft attention weights over the landmarks, plus an orthogonal projection that captures unsampled ("ghost") donor lineages.
⚡ 𝗙𝗼𝗿𝗰𝗲 𝗗𝗶𝗽𝗼𝗹𝗲𝘀: Crossovers shift attention from one donor to another, creating an equal-and-opposite force dipole across the breakpoint that we test against a Brownian-bridge null.
🌊 𝗗𝘆𝗮𝗱𝗶𝗰 𝗪𝗮𝘃𝗲𝗹𝗲𝘁 𝗣𝗼𝗹𝗶𝘀𝗵𝗶𝗻𝗴: Multi-scale wavelet filters, 5-state Viterbi decoding, likelihood polishing, and iterative tract scrubbing resolve nested transfers from 80 bp gene conversions to 30 kb cassettes while merging shared clade transfers into a single ancestral event (98.8% exact).
🦀 Written in Rust and Python and compiled to WebAssembly for zero-install browser runs, RhizAeon maps:
🦠 83 whole sarbecovirus genomes (31 kb) in 2.6 seconds
🧫 240 S. pneumoniae chromosomes (2.22 Mb) in 36.1 seconds
🛡️ 379 primate HLA Class I alleles in 1.1 seconds
🤝 With Danielle Callan, @stvnwvr Hannah Verdonk, @JD_zehr @SpyrosLytras, @nekrut, @tpaisie, and @DarrenM98230782 .
Dan Browne retweeted
A systems-level atlas of carbon-response transcriptional states in Escherichia coli
doi.org/10.1073/pnas.2531884…
Dan Browne retweeted
We connected X-Scientist to our lab robots and watched the lab come alive. 🦾🧪
AI coordinating workflows. Robots moving samples. Experiments running into the night.
It’s incredibly rewarding to watch the future of biotech unfold right in front of us.
Meet X-Scientist: our step toward a self-improving virtual drug hunter!
Today, we’re introducing Xaira’s agentic AI platform for drug discovery:
🧠 Beats Codex using the same Astra model by 7.4 percentage points on DrugDiscoveryBench.
🧬 Nominates targets ~10× faster than human curation, with a comparable wet-lab hit rate.
🔁 Conducts autonomous research, outperforming AutoResearch and AutoScientists in the PerturbQA benchmark.
Here’s what we built, what we tested, and where we’re going. 🧵