This is what makes coding agents powerful: tight loops between proposal, execution, error signal, and revision. Drug discovery needs the same loop: perturb disease-relevant systems → measure state changes → train models → choose the next perturbation. This is what we need to make better biological bets, not just faster molecules. (5/5) rishibedi.com/posts/ai-drug-…

concrete work that moves the needle for lean teams moving drugs into the clinic, great work from the edison team accelerating a critical and often thankless part of the process

i've tried a bunch of ai assistants in the last ~year. dots blows them all out of the water by being connected to codex and being able to access context about all my work. this has been a very productive evening.

"proving out unique biological hypotheses" is the interesting part of Max's thread, but the rest of the narrative is focused on claiming "exquisite engineering" of a more developable candidate for a known target. demonstrating that this engineering is actually enabling unique biological hypotheses will determine Iso's impact

As someone who did this kind of genome mining work during my PhD, some thoughts on this Anthropic announcement: First, the very simplified version of what they did is that they noticed two genes (one known, one new) sitting next to a weird repeating piece of DNA. More specifically, they described an unusual reverse transcriptase (RT) associated with a repetitive DNA array and an unknown accessory protein. This kind of process was used to understand CRISPR back in 2002 and was key to the gene editing tools we use today. To put this into context, though, people have been finding RTs associated with CRISPR arrays since 2008, and this general kind of genome-neighborhood mining has been used to discover new biological systems for decades. The basic genome-mining strategy is well established, and there are now mature tools and published pipelines for doing much of this. There are papers that discover and experimentally validate dozens of new systems using this approach in a single study. Doing it in bacteriophage genomes is also nothing new (eg CasPhi). Finding a weird cluster of genes and repeats is often the easy part. The hard part, and where the real discoveries come from, is figuring out what the system actually does. Eg for the bridge-RNA discovery in 2024 from @arcinstitute or the discovery of CasPhi in 2020 from @DoudnaJennifer they figured out the pieces of the system and the rules for what makes it work so it can be used. Anthropic does not yet know what this does. They’ve shown that the repeat array produces RNAs, but not what those RNAs do, what the RT does with them, or whether the system has any of the programmable properties that make the CRISPR comparison justified. I’m genuinely rooting for all of the frontier labs to seriously get into biological discovery, and I’m excited about what comes out of it. But announcing these very early, incremental findings with the framing of a major discovery doesn’t help. I’d much rather they set the bar high now, so that when an AI actually discovers a fundamentally new biological mechanism, everyone appreciates how big a deal it is.

"violated its own AI policy" misses the point. "additional training for all staff" won't fix the insidious yearning for performative diversity that thrives in the abyss of make-work bureaucracy. the real story is: "administrator whose job shouldn't exist at all does dumb thing"

very cool result showing how wet lab data enables a specialized model to beat gpt-6 astra at a task at the frontier of science! in general scaling is great and obviously i am a believer in it, but probably the more we approach the frontier of science, the more specialized data matters and that gives task-specific models a chance. this specialized data is usually private and is probably a real moat it should be in principle true that a task specific model will probably do better at scientific discovery just because it can use more of its parameters for the task you care about

open-sourcing CTDs is extremely exciting. we have found clinical protocols available on clinicaltrials.gov to be extremely helpful at y-trap. this will be an OOM jump beyond that in utility, especially for lean teams without decades of regulatory experience.

I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands. And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.

Mathematics has now fully morphed into biology: Project costs millions $ ✅ Put out paper that nobody has read ✅ Massive paper supplement nobody will read ✅ Advertise with pretty art ✅ Vicious authorship fight ✅ Say you're curing cancer ✅

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

From a YC-backed healthtech launch this week: "ILLUSTRATIVE RECORD · NO PHI" beneath a fictional marketing graphic. Obvious AI slop. It’s like a bank bragging that the fake account statement in its ad has "NO REAL CUSTOMER ACCOUNT NUMBERS": meaningless. I have nothing against AI-generated website or marketing copy in general. But used indiscriminately, it gives off strong "safety theater" vibes which is a bad look in healthcare and biotech. The problem isn't just bad copy - it suggests that the people running the business don't understand the actual risk profile for their patients... and are instead using nonsense slogans to put on an (amateurish) performance of safety.

it is very exciting for codex et al. to obviate the need for scientists to look at every line of code analyzing their data do not let this lull you into not looking at the _data_

noticed for the first time today agents in codex communicating with each other; happened automatically when i tagged one active task in another "Sent by ChatGPT from another task"

> "what's the easiest way to fix our comms issues?" > "maybe we should just cure cancer"

Elegant demonstration that immune cells from the periphery infiltrate the aging brain. The possibility that modulating peripheral immunity could affect neurodegenerative disease opens up a whole new control surface for therapeutic intervention.

Catching skin cancer early is a home robotics problem. Melanoma is highly treatable when detected early, yet today’s screening process depends heavily on patients noticing tiny changes across their entire skin surface. This requires patients to solve a near-impossible visual-memory and registration problem. I built OpenDerm, an open-source 4-DOF robot that captures high-resolution images of the skin and uses them to reconstruct and track the skin surface in 3D over time. The best way to make skin screening truly routine is to bring it into the home. OpenDerm shows that inexpensive robotic skin imaging is possible, but the path to scale is not a dedicated screening robot in every household—it is to make skin screening one of the many useful things a general-purpose home robot can do. Read more about why I built OpenDerm and how it works here: Blog: marionlepert.github.io/blog/… Project: openderm.github.io/

besides how awesome it is that a century-old conjecture is disproven by LLM, it is also very cool to see mathematics-in-public on X: open discourse from experts on how to interpret/contextualize/generalize the result

delighted by the fact that starting a codex-app project in an ssh remote directory automatically picks up (and nicely renders) existing codex-cli sessions in that directory!

Very excited about 5.6-sol but so far, almost every request (including non-bio) I have submitted in Codex has triggered the following: "This request requires additional safety checks, which can take extra time. Hang tight or retry with a faster model for a quicker response, though it may be less capable of handling complex requests." 1/ It is opaque as to how long these safety checks will take, so I have no idea how much of a time penalty I'm paying by sticking with sol. 2/ Beware that on long-running tasks, "retrying with a faster model" discards progress/context -> re-starts from scratch with luna. This is much better than Fable's complete nerf-ing at the briefest mention of biology, but still annoying.