Thank you for sharing, that provided some good context for how to interpret this
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I wish we didn’t need these again, but here is the honest version of Anthropic’s biology announcement
(Caveat: I haven’t worked in bioinformatics for many years.)
The good: Anthropic ran ~950 Claude agents over a large biological sequence database. Claude searched, wrote code, compared sequences and genomic neighborhoods, and found an interesting pattern that apparently had not been noticed before: a known reverse transcriptase associated with another gene and a repetitive DNA array.
That is cool. Automating this kind of open-ended bioinformatics search at scale is useful, and Claude may have found a lead a human would have missed.
But: Claude did not do a biological experiment. It searched databases and analyzed data.
Humans then took the candidate into the wet lab. And the wet-lab result so far is modest: they showed that the repeat array produces short RNAs.
We still don’t know what the system does. No function, mechanism, phenotype, targeting, defense activity, or programmability has been demonstrated.
This is also where the CRISPR framing gets ahead of the result. Right now, “it has some features reminiscent of known programmable systems” is a hypothesis for what to investigate next, not a discovery that it behaves like CRISPR.
And there is a missing baseline: bioinformatics has had tools for finding unusual gene neighborhoods and candidate systems for years. The interesting comparison is 950 Claude agents vs. an expert using the best existing computational pipelines - not Claude vs. someone manually looking through 200,000 sequences.
So my honest announcement would be:
Claude autonomously found an interesting candidate for a previously uncharacterized biological system. A small human wet-lab experiment confirmed that part of the candidate is expressed. We don’t yet know what it does.
That is a good result.
But in a regular biology lab, this isn’t the finished paper. It is the result you show at lab meeting and say: “This looks interesting. Now we need to figure out what the hell it does.”
Maybe that next step leads to a major discovery. But that discovery hasn’t happened yet.
I am irked by the CRISPR framing. That seems to be IPO positioning.
Good hypotheses are a dime a dozen in life sciences. Biology is very unforgiving and most hypotheses lead to nothing when thoroughly tested. This is true for something as "simple" as enzymes as in this case, but even more true for curing diseases. Otherwise, there would not be any failures of phase III clinical trials, after billions USD spent on preclinical research and prior clinical trials.
When overinterpreting these (interesting) results, you are entering Andy Grove Fallacy [0] territory very fast.
He was a PhD student. He knows the significance level of this result. He knows that if he had walked into Bill’s office (his advisor) with “we found an interesting system, but we still don’t know what it does” and said he was ready to graduate, Bill would have kicked him out of the room.
But somehow, when the IPO is around the corner, this becomes “AI is starting to drive biological discovery.”
100% AI per Pangram. I caught it at "This is also where the CRISPR framing gets ahead of the result." -- somehow this is not a sentence anybody non-obnoxious would write. It's a weird structure where the AI talks about something specific as if it were an example of a common theme. This paragraph is an even clearer ekample:
"But in a regular biology lab, this isn’t the finished paper. It is the result you show at lab meeting and say: “This looks interesting. Now we need to figure out what the hell it does.”"
While I agree with you that this is likely AI assisted, I think this may be changing now.
People speak in the manner of what they consume. If you consume a lot of claudish, you will eventually start talking claudish too. And I've already noticed people talking claudish in real life.
Seems more than good enough for a live model though! I can imagine this demo being extended to be a lot nicer to play with. You can just feed the model engine analysis and it can make as high of quality moves as needed. No longer any correlation between the model's understanding of the position and the moves that would be made but I think that's still a really nice improvement when thinking about this as adding live voice interaction to existing chess vs computer functionality rather than adding chess to possible interactions with the latest live voice model.
Great summary! I would just add that cherry on top though -- that HuggingFace tried using the top commercial models in response but couldn't because of the cybersecurity restrictions so they had to use GLM 5.2 instead
"When we started the log analysis, we first used frontier models behind commercial APIs. This did not work: the analysis requires submitting large volumes of real attack commands, exploit payloads, and C2 artifacts, and these requests were blocked by the providers' safety guardrails, which cannot distinguish an incident responder from an attacker. We ran the forensic analysis instead on GLM 5.2, an open-weight model, on our own infrastructure. This had a second benefit: no attacker data, and none of the credentials it referenced, left our environment."
For me, primarily the performance tbh. Chatto just flies and Slack feels incredibly sluggish. And from the "vibes" it just feels better designed, I like the overall user experience, it feels technologically solid but also looks nice and works how I feel it should. Hard to put my finger on it exactly.
Heh after reading that title card I thought this was going to be a mosquito based software bug analogy. I expected a description of how to write software that resulted in more "good bugs" that might facilitate finding other bugs somehow. Now I'm a little disappointed
It’s just a basic IntelliJ plugin which provides an infinite canvas to add code bookmarks to. I work on a large code base and often have to take on tasks involving lots of unfamiliar areas of code and components which influence each other only through long chains of indirection. Having a visual space to lay things out, draw connections, and quickly jump back into the code has been really helpful
The canvas and UI is built using Java AWT since that’s what IntelliJ plugins are built on, but it occurred to me that I could just throw in a web view and use any of the existing JS libraries for working on an infinite canvas. React Flow has seemed like the best option with tldraw being what I’d fallback to.
But then.. if the canvas is built with web technology then there’s no reason to keep it just within an IntelliJ plugin vs just a standalone web app with the ability to contain generic content that might open files in IntelliJ or any other editor. I’m pretty sure the “knowledge database on a canvas” thing has been done a number of times already so I want to also see if there are existing open source projects that it’d be easy enough to just add a special node type to
See also this recent post about Mercury-Coder from Inception Labs. There's a "diffusion effect" toggle for their chat interface but I have no idea if that's an accurate representation of the model's diffusion process or just some randomly generated characters showing what the diffusion process looks like
Just about every line of code for this was written by Claude 3.7 via Claude Code. I never gave the other AI development tools like Cursor and Aider a fair shake so I may have just been behind the times on what's possible with agentic editors, but I found Claude Code to be extremely impressive. The API costs did end up being something around $15 though and this is a really small project, so I imagine cost goes up quite a bit to do any non-trivial work on a project of a decent size
This was great for me though because this is something I've had in mind for a while but never considered quite useful enough to be worth the time it'd take to learn about IntelliJ plugin development + the Java AWT knowledge required to create a canvas app like this
lol yeah I tried to get it to whisper too. And talk faster or slower or do accents. It seemed to be able to kind of do each of those things but only very slightly. Enough to see that there was some successful interpretation of the request but lack of flexibility to fully execute on it. OpenAI's model still has this beat on that front imo (talking quietly / slower / faster)
Post content:
_____
I wish we didn’t need these again, but here is the honest version of Anthropic’s biology announcement (Caveat: I haven’t worked in bioinformatics for many years.) The good: Anthropic ran ~950 Claude agents over a large biological sequence database. Claude searched, wrote code, compared sequences and genomic neighborhoods, and found an interesting pattern that apparently had not been noticed before: a known reverse transcriptase associated with another gene and a repetitive DNA array.
That is cool. Automating this kind of open-ended bioinformatics search at scale is useful, and Claude may have found a lead a human would have missed.
But: Claude did not do a biological experiment. It searched databases and analyzed data.
Humans then took the candidate into the wet lab. And the wet-lab result so far is modest: they showed that the repeat array produces short RNAs.
We still don’t know what the system does. No function, mechanism, phenotype, targeting, defense activity, or programmability has been demonstrated.
This is also where the CRISPR framing gets ahead of the result. Right now, “it has some features reminiscent of known programmable systems” is a hypothesis for what to investigate next, not a discovery that it behaves like CRISPR.
And there is a missing baseline: bioinformatics has had tools for finding unusual gene neighborhoods and candidate systems for years. The interesting comparison is 950 Claude agents vs. an expert using the best existing computational pipelines - not Claude vs. someone manually looking through 200,000 sequences.
So my honest announcement would be:
Claude autonomously found an interesting candidate for a previously uncharacterized biological system. A small human wet-lab experiment confirmed that part of the candidate is expressed. We don’t yet know what it does.
That is a good result.
But in a regular biology lab, this isn’t the finished paper. It is the result you show at lab meeting and say: “This looks interesting. Now we need to figure out what the hell it does.”
Maybe that next step leads to a major discovery. But that discovery hasn’t happened yet.