I built one of the connected tools included in this launch (the Biomni HPC [1]), and I have spent an inordinate amount of my life working on this problem. (I also worked at Anthropic, but not on this product.)
As other comments have pointed out, this is for data science – but it's capable of more than making plots and writing papers [2]. It has integrations with many databases and computational tools, including a researcher's institutional cluster.
That alone is valuable. I founded a startup after struggling with this problem at a bio startup; integrating these tools and databases is hard and time consuming. If the only outcome of this product is that great APIs are built for LLMs, it will be a massive positive impact. Many databases used in computational genomics are still only accessible through FTP!
LLMs are particularly good at navigating these tools and databases. It's often very specialized, but straightforward, work that benefits from in-context skills. Seeing an early glimpse of my former customers – bioinformaticians – using LLMs to solve this problem is what led me to join Anthropic in 2024.
Also, this pattern isn't fundamentally constrained to data science: you can also integrate with a wet lab or a CRO for some kinds of science. This is what I'm spending my time on now.
This type of science doesn't solve everything, but it's useful in some niches. For example, progress on many rare diseases is bottlenecked by researcher attention rather than a fundamental breakthrough.
Can you speak to what makes this different from simply including or configuring various agent skills? Or is it simply the combination of lots of helpful defaults that makes this product useful?
Connecting AI directly to the data sources (instead of just asking it to provide code that I run locally for myself) can get quite complicated in terms of meeting institutional policy, applicable law, data access-storage requirements (e.g. NIH data repositories), and can require legal agreements between institutions and the AI provider.
I'd really like to see much better visualization from Claude Science at some point. Educational-esque, with full threejs + shaders scenes over just these plots and protein/chemical structures. This for a lot of papers in the literature review would be awesome.
Sounds like the perfect use case for some kind of framework where you have a local LLM (that can run on lower spec hardware) collaborating with the main LLM to optimise latency and all the other niche and legacy use cases ?
The most interesting thing here is that Claude Science runs a local server and a web-based UI that connects to that server from your browser. This is very different from Claude Code and Cowork, where the UI is more tightly coupled to the host machine (which makes things like computer use possible).
I think I recognize the strategy: most pharma environments connected to interesting data are tightly locked down, to the point where you can't just connect your Macbook to the source data.
Similarly, access to large genomic biobank datasets like UK Biobank or NIH's All of Us program is granted only through a Trusted Research Environment (TRE), a remote data analysis platform usually quite restricted on internet access, etc. You can't easily run desktop apps, but these environments do usually support running JupyterLab or VS Code, tunneling the user interface through to the end user. (Source: I previously ran the team that built the All of Us TRE.)
Claude Science looks a lot more like something one could imagine spinning up in one of those highly-constrained data environments (with the "server" running within the TRE and the UI proxied to the end user's browser) than the does-everything Claude mega-app. That will be critical for traction within pharma R&D environments.
I will say that for moderately-computational scientists, who are daily driving RStudio, JupyterLab, or maybe VS Code, Claude Science will be quite an unfamiliar shaped product. I'll be curious to see whether something like this gains adoption (1) in place of, (2) alongside, or (3) eventually wrapping around the more traditional data science workbench tools out there.
Anecdotally, as someone with a lot of moderately computational sciencey tasks at work (part of my job is as a data analyst for a geology firm that has some interesting sensor data), combining Claude Code and standard python data libraries has been extremely powerful and sped up my workflows immensely. If I just need a quick analysis or visualization, Claude can write something for me in minutes that would take me an hour or so to sort out on my own. I know the relevant libraries well enough to read and verify the code, which is an important distinction from blindly using a black box AI.
I will note that Claude Code and Jupyter in VSCode don't play nicely together right now - it forces me to rerun the whole notebook from the start after every edit Claude makes. This has led to me stepping back from notebooks and having Claude write standalone scripts that I then spend time merging back into a pretty notebook.
Tried this to see how it goes in my particular field - computational design of RNAi-based biopesticides. One-shotted a design for targeting the DvSnf7 transcript of western corn rootworm. It took a fairly naive approach (maybe how a 1st year PhD student would go about it), but got the job done. Also noted caveats with its approach (e.g. using mammalian design rules, limited off-target screening). Not bad really. But also not great. When its flaws were pointed out, the AI determined that it could have taken a more informed approach. Then Opus 4.8's safety system flagged the session.
> Then Opus 4.8's safety system flagged the session.
The jokes write themselves these days.
I suggest collecting 10 seminal works on the subject matter including 10 textbooks in the general field, converting them to plain text via OCR or text extraction, then trying the same thing with a superior agentic harness, like omp.sh
/goal set create biopesticide targeting the DvSnf7 transcript of western corn rootworm
When I saw "Science" I didn't think they meant Data Science, which is what the UIs full of pandas code and plots imply. Even if the focus is on the sciences, I suspect that's the less valuable part of the announcement particularly with the implication of Jupyter Notebook 2.0.
Image-understanding for data viz is a use case that has been ignored, and modern LLMs are getting better at proper EDA. But, uh, I may need to update my resume.
A lot of the soft and hard sciences use hacky matplotlib code to produce results and visualisation, without being necessarily data science
From the bits I've seen, I'd take claude-generated code any time over that written by maths, physics, biology, linguistics people. Even though I've seen Claude make some super-big mistakes while doing data analysis I'd guess it's already more reliable than most academics trying to code.
I think presentation via software just isn't a lot of their strong suits. A lot of researchers' personal or research lab sites too are usually way out of date or just really badly presented from what I've seen. They could all do with some thinking about aesthetics and understandability more.
Conveniently, you can use published results as tests of equivalence, provide the ugly code as context, and regenerate it to your liking. I think the odds of such a regeneration introducing a bug that's within the usage domain but that dodges the golden tests are quite low... so long as you resist the urge to add features along the way.
My take based on the video is that they're thinking more about bioinformatics, which might technically fall under the "data science" umbrella depending how you define your terms, but which is not described that way in common usage.
It's the content that determines the sort of science, not the toolchain.
Before LLMs the tech groups I followed were ripping with discussions about this and that topic, what to use and when; I believe these discussions sparked the creation of many frameworks and tools out of "this seems like a good idea, wouldn't hurt to implement it". Unfortunately it all resolves around LLMs nowadays and how to make some LLM work some way or another, we don't even discuss the very topics the groups were created to discuss. I fear science is soon to taste the same thing - discussions about LLMs taking place instead of the actual topics that would be discussed otherwise.
Raw dog Chat LLMs are pretty worthless. But run an agent with tool invocation and they get scary good. It's amazing how much reasoning is packed into the English language. Provide your model with enough information and it can pull some miracles out of thin air. It's not the "Replace humans" level yet, but you can automate a lot of stuff you wouldn't expect to be able to automate.
Well, for Arch Linux, there was the unofficial version from the official binary in the AUR already... (Not sure what you mean by 'no arch based distro support').
I basically did the same thing almost one and half years ago and not many people cared, but I still believe that this is the future for computational biology.
So it's like Claude Cowork for Science, i.e. for less tech-savvy users? I would imagine scientists with some coding background might just prefer to use Claude Code normally and integrate it with their stack of choice, but perhaps the comfort and ease of use of Claude Science still wins out.
A "standing review agent" seems to be one of the main differences beyond the new connectors and in place visualisation tools.
>A standing reviewer agent. This runs in the background during a session, checking citations against sources, flagging numbers it can't trace back to evidence, and catching figures that don't match the code that supposedly generated them. That's not something Code or Cowork do automatically — you'd have to ask Claude to double-check itself as a separate step.
Working on the uniprot services that might be used from the connector it would be nice to learn if this uses public resources or if there is a private anthropic copy of certain uniprot data sets.
Mostly targeted at life sciences - e.g. integration for FDA, PubMed, genomics databases but no ACM / IEEE as far as I can tell.
Edit: arXiv search seems to be supported - but not Google Scholar etc. So, this tool is of little use for most researchers outside life sciences.
Edit 2: Quick walkthrough: the AppImage starts a browser window with an onboarding wizard and a chat interface. It suggests a few things one might do at the start of a research project - e.g. do a quick literature review. When I chose that option, wrote Python scripts that used MCP calls to do arXiv searches. Stayed seemingly stuck there for a few minutes not returning anything. Then:
> The free-text search returned too much noise
Claude decided to choose a certain paper as a starting point for further research. Shortly afterwards:
> That DOI resolved to the wrong paper. Let me find the correct anchor papers by title/author search directly.
Then it meandered a few more minutes doing research and creating a citation graph (that it did not show to me).
> I have a complete picture. Let me verify the key DOIs resolve and then write the review.
Then:
> The lint flags em-dash overuse. Let me reduce them, then save.
Then: a nice but verbose literature overview of my chosen topic
<blink>BUT it includes at least one hallucinated reference!</blink>
P.S.: What does this mean?
[reviewer] verifier_mode=default-on downgraded to off: pro subscription tier, autoReviewer withheld (frame=f2a81cb2)
It sure is! But ironically, because of the intention behind the obfuscation. Not the fact that AI was used in a research paper.
I have no issues with AI use in science. If claude can explain my research better than me, then have at it. But I do NOT want to read a passage thinking it was written by a human when it wasn't. Science has no idea yet how such disclosures should work yet. What should be done by humans as a matter of principle, and what can't be or should not be done by humans.
Some authors may even choose to leave syntactical errors as a tell for those self-authored passages; long-term, some interesting language drifts may come of it.
When I was doing my phd, around 2 decades ago, I was often going to the library’s compactus to fish for a Phys Rev from the 80s. Back then papers were sparse and expensive. But the quality!
The Higgs boson is 3 papers, 6 authors and 6 pages in total!
At the end of my phd, 30++ pages slop papers were the norm.
Nowadays, well..
The paper by Higgs was one page. The guy probably published less than a hundred pages in his career.
One reason that made me abandon a career was the disgust caused by the publishing frienzy.
There is an obscure topic where I have read basically every single dissertation, study, etc on that topic (or even just articles that mention it). It is very noticeable how much briefer older publications were.
It would be impossible to do that today. I guess I could have an LLM just summarise all the papers…
Science isn’t suffering from a lack of papers. It’s suffering from a lack of good papers. Making it easier to just pump out paper-mill publications is about the last thing science needs right now.
My hope is that the flood of AI articles pushes the academic publication system to its highly-anticipated breaking point.
The most absurd part is that everyone in academia knows that publish or perish is tremendously damaging to real research. Yet we’re all hostage of this system that we created in the name of “merit” and “efficiency”.
We need a different system to identify and reward talented hard-working people. Back in the day it all relied on actual interpersonal interaction and subjective judgment, but there were also much fewer researchers worldwide.
> My hope is that the flood of AI articles pushes the academic publication system to its highly-anticipated breaking point.
This will just make research inaccessible to most researchers. There is no incentive to limit publishing, at all, other than at the highest echelons. Publish or perish will just become worse. Look at what is happening to programming and extrapolate that to research work.
And all for what? Just to keep up this facade of society until most of society can be excised, whether artificially or naturally though lack of reproduction.
They are going to make it a thousands times worse.
It wasn't perfect before, but it at least took some time to fake a paper. The problem is now people can produce a very plausible looking completely fake paper in minutes. Peer review is in the process of completely collapsing, in fact I think it's already basically done.
The only way this might fix things is if we require all papers are completely reproducable (that doesn't help in subjects like biology of course. They can still provide all the experimental data in the rawest format possible which doesn't break any laws).
I'm actually quite excited for when (if) the models get good enough to start replicating compsci papers. I'd love it if there was a system which calculated a reproducibility score per-lab or per-researcher, which I could look up alongside their citation count.
I want to see who did the hard work properly, and who focused on publishing with concealed details.
It seems to me that LLM's could massively improve reproducibility issues if journals would require that the papers be reproducible by model X using a standardized prompt in < N minutes, etc...
it could also be said that scientific interpretation is suffering from a framework crisis. the scientific convention of experiment, is the test of an hypothesis, as a logical construct.
repetition of materials and methods toward reproducibility, holds far less wieght than multiple variants of process designed to test a common hypothesis resulting in agreement.[null, or failure to null]
For a while now there has been very little incentive for providing these alongside the paper, and I don't see why exactly 'AI' would change this. I could even see how making it vague to be harder to test with LLMs could be profitable for citation hackers.
You can imagine using AI agents to tag papers that don’t have code or similar work attached and just filtering them out.
The Chinese open source community has made a lot of incentive to make research reproducible for example. The most reproducible works from I.e. deepseek get widely cited and adopted.
I don’t think we can just say “AI” and it’s fixed but with deliberate effort there’s reason to be optimistic.
Unless reviewing becomes more profitable than publishing, anything that makes both easier will drive one up far more than the other. And it is difficult to conceive of something that would make reviewing much easier without making publishing much easier.
Just as a counterpoint ML and AI research has become much more reproducible over time. I feel like this is relevant because ML / AI researchers are huge power users of AI tools.
Between 2016 and 2021 the share of ML/ robotics/ AI researchers being reproducible (ie contianing code and similar instructions to reproduce) doubled [1].
The major US labs have gone largely closed source (I.e. they no longer publish frontier research) but the Chinese ecosystem has incredibly reproducible code.
This is field dependent obviously but I think it atleast gives reason to be optimistic.
Yes people will churn out fake slop research, but it feels like that can be categorized and then ignored.
That's good to hear about ML and AI research, but most research is not based on computers and so would require laboratory setups to reproduce. Not only is trying to reproduce such findings (beyond what is effectively a sanity check) through simulations a lost cause, if AI can reproduce such research it would be capable of doing such research itself... in which case it would be far more fruitful to use AI to do further research.
This thread is about a product based on fully reproducible research though so I feel like we should stay grounded. Claude science is meant to be used in the context of reproducible science research, there is a decent reason to not be cynical on future research being reproducible.
> if AI can reproduce such research it would be capable of doing such research itself
Well there is a big distinction between research validation and research generation, it is generally much easier to verify that a math proof is true or false than to find a truly novel proof.
But yes in the long run I’d think AI will be doing tons of research and it will by default reproducible. So maybe we’re aligned after all?
Por que no los dos? Scientific review times are up, it’s harder to find reviewers, and many reviews are AI generated anyway. Auto-generated research publications will arguably make the replication crisis worse, because there will be more slop to clog up the review system, and these papers will presumably be just as (if not more) not reproducible than human written science
In some fields like comp sci, when code isn't given but the paper describes the approach, LLMs do help with the reproducibility crisis: you can ask it to reproduce the result through reimplementation by reading the paper.
If it fails you may have to double check it did properly reimplement it, but if it succeeds you do get a reproduction.
impressive to me, but sadly i feel a little misleading since this is only the data-science part of life sciences.
every few weeks though i test claude and chatgpt on their scientific reasoning and it has definitely improved over time. in my experience without specific instruction on what is known/unknown they typically are lagging behind the leading edge of the field (dev bio/pluripotency in my case). probably because scientific research articles are not open-source so they can't crawl them.
claude has definitely outperformed chatgpt in this regard however, it's scientific reasoning is impressive.
The fact that we are coming up on a month of Fable being unavailable with essentially zero actual signal from Anthropic around when it may be back is crazy to me. Yet still we have these random new products coming out?
> Anthropic @AnthropicAI Jun 27, 2026 · 12:29 AM UTC
> Since June 12, we’ve been working closely with the US government to restore access to Claude Mythos 5 and Fable 5. Today, the government notified us that Mythos 5, our strongest cybersecurity model, can be redeployed to a set of US organizations that operate and defend critical infrastructure.
> We’re restoring access for these organizations quickly, and we’re continuing to work with the government to expand access to Mythos 5 and make Fable 5 available for general use again.
I mean the company has like 3k employees or more right? Lots of them are just working on more applied AI use cases that don't require frontier AI just the right integrations and structure etc.
Opus 4.8/ GPT 5.6 level models with the right workflows/ data/ access are still good enough to do huge amounts of economically valueable work.
This plus it's entirely plausible their employees have access to Fable or their own other pre-released models internally. Other than the perks you mentioned they've got excellent distribution too.
Why would you people ever use this companies products? They're actually evil and are trying to scam you and or make you unemployable./worthless. You people really gotta wake up.
Why have they talked about this for a long time? They predicted date of code maxing out, and did so not from fitting a sigmoid or something but they predicted it would max out right during a steep part of the slope?
AI brand identity has made the unfortunate pivot to "how much do you trust us" which is going be a real race to the bottom. I don't want LLMs managing nuclear reactors or replacing junior lab technicians. I don't trust any of these LLMs to do the bare minimum, regardless of how good it is for your brand.
It's gross watching these stunts unfold. Next ChatGPT will fly a passenger jet, which Claude will one-up with an agentic surgery, which OpenAI will respond to by putting a humanoid robot on the moon. If this is what 21st century market competition looks like, we are all fucked.
I built one of the connected tools included in this launch (the Biomni HPC [1]), and I have spent an inordinate amount of my life working on this problem. (I also worked at Anthropic, but not on this product.)
As other comments have pointed out, this is for data science – but it's capable of more than making plots and writing papers [2]. It has integrations with many databases and computational tools, including a researcher's institutional cluster.
That alone is valuable. I founded a startup after struggling with this problem at a bio startup; integrating these tools and databases is hard and time consuming. If the only outcome of this product is that great APIs are built for LLMs, it will be a massive positive impact. Many databases used in computational genomics are still only accessible through FTP!
LLMs are particularly good at navigating these tools and databases. It's often very specialized, but straightforward, work that benefits from in-context skills. Seeing an early glimpse of my former customers – bioinformaticians – using LLMs to solve this problem is what led me to join Anthropic in 2024.
Also, this pattern isn't fundamentally constrained to data science: you can also integrate with a wet lab or a CRO for some kinds of science. This is what I'm spending my time on now.
This type of science doesn't solve everything, but it's useful in some niches. For example, progress on many rare diseases is bottlenecked by researcher attention rather than a fundamental breakthrough.
[1] https://x.com/phylo_bio/article/2029233694775624096
[2] In comparison, OpenAI's science product – Prism – was effectively a LaTeX editor they acquired with Crixet.
Can you speak to what makes this different from simply including or configuring various agent skills? Or is it simply the combination of lots of helpful defaults that makes this product useful?
Previously integrated Biomni into our intelligent workspace:
https://blog.codesolvent.com/2025/07/ai-assistant-with-biome...
Happy to chat if intrigued.
Connecting AI directly to the data sources (instead of just asking it to provide code that I run locally for myself) can get quite complicated in terms of meeting institutional policy, applicable law, data access-storage requirements (e.g. NIH data repositories), and can require legal agreements between institutions and the AI provider.
I cannot touch. At least not yet.
I'd really like to see much better visualization from Claude Science at some point. Educational-esque, with full threejs + shaders scenes over just these plots and protein/chemical structures. This for a lot of papers in the literature review would be awesome.
How do you validate this kind of work to weed out any confabulating by the LLMs?
Sounds like the perfect use case for some kind of framework where you have a local LLM (that can run on lower spec hardware) collaborating with the main LLM to optimise latency and all the other niche and legacy use cases ?
The most interesting thing here is that Claude Science runs a local server and a web-based UI that connects to that server from your browser. This is very different from Claude Code and Cowork, where the UI is more tightly coupled to the host machine (which makes things like computer use possible).
I think I recognize the strategy: most pharma environments connected to interesting data are tightly locked down, to the point where you can't just connect your Macbook to the source data.
Similarly, access to large genomic biobank datasets like UK Biobank or NIH's All of Us program is granted only through a Trusted Research Environment (TRE), a remote data analysis platform usually quite restricted on internet access, etc. You can't easily run desktop apps, but these environments do usually support running JupyterLab or VS Code, tunneling the user interface through to the end user. (Source: I previously ran the team that built the All of Us TRE.)
Claude Science looks a lot more like something one could imagine spinning up in one of those highly-constrained data environments (with the "server" running within the TRE and the UI proxied to the end user's browser) than the does-everything Claude mega-app. That will be critical for traction within pharma R&D environments.
I will say that for moderately-computational scientists, who are daily driving RStudio, JupyterLab, or maybe VS Code, Claude Science will be quite an unfamiliar shaped product. I'll be curious to see whether something like this gains adoption (1) in place of, (2) alongside, or (3) eventually wrapping around the more traditional data science workbench tools out there.
Anecdotally, as someone with a lot of moderately computational sciencey tasks at work (part of my job is as a data analyst for a geology firm that has some interesting sensor data), combining Claude Code and standard python data libraries has been extremely powerful and sped up my workflows immensely. If I just need a quick analysis or visualization, Claude can write something for me in minutes that would take me an hour or so to sort out on my own. I know the relevant libraries well enough to read and verify the code, which is an important distinction from blindly using a black box AI.
I will note that Claude Code and Jupyter in VSCode don't play nicely together right now - it forces me to rerun the whole notebook from the start after every edit Claude makes. This has led to me stepping back from notebooks and having Claude write standalone scripts that I then spend time merging back into a pretty notebook.
I agree that it's an interesting architecture, but I'm not sure how it would work in a highly controlled server.
If you can't connect from your Mac, then I doubt they will allow an agent to make requests from the server
Tried this to see how it goes in my particular field - computational design of RNAi-based biopesticides. One-shotted a design for targeting the DvSnf7 transcript of western corn rootworm. It took a fairly naive approach (maybe how a 1st year PhD student would go about it), but got the job done. Also noted caveats with its approach (e.g. using mammalian design rules, limited off-target screening). Not bad really. But also not great. When its flaws were pointed out, the AI determined that it could have taken a more informed approach. Then Opus 4.8's safety system flagged the session.
> Then Opus 4.8's safety system flagged the session.
The jokes write themselves these days.
I suggest collecting 10 seminal works on the subject matter including 10 textbooks in the general field, converting them to plain text via OCR or text extraction, then trying the same thing with a superior agentic harness, like omp.sh
/goal set create biopesticide targeting the DvSnf7 transcript of western corn rootworm
<sarcasm>make no mistakes</sarcasm>
When I saw "Science" I didn't think they meant Data Science, which is what the UIs full of pandas code and plots imply. Even if the focus is on the sciences, I suspect that's the less valuable part of the announcement particularly with the implication of Jupyter Notebook 2.0.
Image-understanding for data viz is a use case that has been ignored, and modern LLMs are getting better at proper EDA. But, uh, I may need to update my resume.
A lot of the soft and hard sciences use hacky matplotlib code to produce results and visualisation, without being necessarily data science
From the bits I've seen, I'd take claude-generated code any time over that written by maths, physics, biology, linguistics people. Even though I've seen Claude make some super-big mistakes while doing data analysis I'd guess it's already more reliable than most academics trying to code.
I think presentation via software just isn't a lot of their strong suits. A lot of researchers' personal or research lab sites too are usually way out of date or just really badly presented from what I've seen. They could all do with some thinking about aesthetics and understandability more.
This 100000x over. Nothing is worse than trying to productionize code coming from academics like this.
Conveniently, you can use published results as tests of equivalence, provide the ugly code as context, and regenerate it to your liking. I think the odds of such a regeneration introducing a bug that's within the usage domain but that dodges the golden tests are quite low... so long as you resist the urge to add features along the way.
My take based on the video is that they're thinking more about bioinformatics, which might technically fall under the "data science" umbrella depending how you define your terms, but which is not described that way in common usage.
It's the content that determines the sort of science, not the toolchain.
Honestly quite excited to see what can happen here, I think biology has generally had a lack of data science expertise.
Tell us, what gives you that impression?
They do mention things like protein and chemical structure visualization though
All of these new things are starting to look like soviet space program propaganda. Is there something really new?
Old wine, new bottle...
Before LLMs the tech groups I followed were ripping with discussions about this and that topic, what to use and when; I believe these discussions sparked the creation of many frameworks and tools out of "this seems like a good idea, wouldn't hurt to implement it". Unfortunately it all resolves around LLMs nowadays and how to make some LLM work some way or another, we don't even discuss the very topics the groups were created to discuss. I fear science is soon to taste the same thing - discussions about LLMs taking place instead of the actual topics that would be discussed otherwise.
Well LLMs are largely useless and people are realizing that.
Raw dog Chat LLMs are pretty worthless. But run an agent with tool invocation and they get scary good. It's amazing how much reasoning is packed into the English language. Provide your model with enough information and it can pull some miracles out of thin air. It's not the "Replace humans" level yet, but you can automate a lot of stuff you wouldn't expect to be able to automate.
This seems to have unblocked Claude Desktop for Linux ( https://code.claude.com/docs/en/desktop-linux )
unfortunately no arch based distro support. I'm curious why it's not packaged as a flatpak.
Well, for Arch Linux, there was the unofficial version from the official binary in the AUR already... (Not sure what you mean by 'no arch based distro support').
First party support would be nice since this is not a high-trust in the AUR period, but fair point, I'll probably use it, thank you!
Many deb packages are easily repackaged for arch by the community
I basically did the same thing almost one and half years ago and not many people cared, but I still believe that this is the future for computational biology.
https://celvox.co/solutions/axon
Should be called Claude-bio-big-bucks.
What about earth science, physics, engineering? The connectors and skills are all just biology and pharma. Boo
So it's like Claude Cowork for Science, i.e. for less tech-savvy users? I would imagine scientists with some coding background might just prefer to use Claude Code normally and integrate it with their stack of choice, but perhaps the comfort and ease of use of Claude Science still wins out.
A "standing review agent" seems to be one of the main differences beyond the new connectors and in place visualisation tools.
>A standing reviewer agent. This runs in the background during a session, checking citations against sources, flagging numbers it can't trace back to evidence, and catching figures that don't match the code that supposedly generated them. That's not something Code or Cowork do automatically — you'd have to ask Claude to double-check itself as a separate step.
Working on the uniprot services that might be used from the connector it would be nice to learn if this uses public resources or if there is a private anthropic copy of certain uniprot data sets.
tl;dr: Use this if you don't like doing science or doing things well. It hallucinates references.
Seems to be based on https://github.com/swaruplab/operon as evidenced by the authorization dialog and https://x.com/testingcatalog/status/2037684573161783373 .
Mostly targeted at life sciences - e.g. integration for FDA, PubMed, genomics databases but no ACM / IEEE as far as I can tell.
Edit: arXiv search seems to be supported - but not Google Scholar etc. So, this tool is of little use for most researchers outside life sciences.
Edit 2: Quick walkthrough: the AppImage starts a browser window with an onboarding wizard and a chat interface. It suggests a few things one might do at the start of a research project - e.g. do a quick literature review. When I chose that option, wrote Python scripts that used MCP calls to do arXiv searches. Stayed seemingly stuck there for a few minutes not returning anything. Then:
> The free-text search returned too much noise
Claude decided to choose a certain paper as a starting point for further research. Shortly afterwards:
> That DOI resolved to the wrong paper. Let me find the correct anchor papers by title/author search directly.
Then it meandered a few more minutes doing research and creating a citation graph (that it did not show to me).
> I have a complete picture. Let me verify the key DOIs resolve and then write the review.
Then:
> The lint flags em-dash overuse. Let me reduce them, then save.
Then: a nice but verbose literature overview of my chosen topic
<blink>BUT it includes at least one hallucinated reference!</blink>
P.S.: What does this mean?
> The lint flags em-dash overuse
An explicit text desloppification pass (i.e. LLM-use obfuscation) seems like outright scientific fraud.
It sure is! But ironically, because of the intention behind the obfuscation. Not the fact that AI was used in a research paper.
I have no issues with AI use in science. If claude can explain my research better than me, then have at it. But I do NOT want to read a passage thinking it was written by a human when it wasn't. Science has no idea yet how such disclosures should work yet. What should be done by humans as a matter of principle, and what can't be or should not be done by humans.
Some authors may even choose to leave syntactical errors as a tell for those self-authored passages; long-term, some interesting language drifts may come of it.
We send our regards: https://arxiv.org/abs/2510.15061 (ICLR 2026)
Biosciences mostly don't use arXiv, they have their own https://www.biorxiv.org/ but it's usage is not as common as arXiv is in e.g. physics.
When I was doing my phd, around 2 decades ago, I was often going to the library’s compactus to fish for a Phys Rev from the 80s. Back then papers were sparse and expensive. But the quality!
The Higgs boson is 3 papers, 6 authors and 6 pages in total!
At the end of my phd, 30++ pages slop papers were the norm.
Nowadays, well..
The paper by Higgs was one page. The guy probably published less than a hundred pages in his career.
One reason that made me abandon a career was the disgust caused by the publishing frienzy.
And now tokens..
There is an obscure topic where I have read basically every single dissertation, study, etc on that topic (or even just articles that mention it). It is very noticeable how much briefer older publications were.
It would be impossible to do that today. I guess I could have an LLM just summarise all the papers…
What's the reason for this? Publish-or-perish? Papers have to be more thorough? Extra junk tacked on for the sake of showing lengthier papers?
Any other researchers paranoid of using LLMs for fear of them using your data and front running your publications/work?
Or incorporating it in training data and then spitting it out to a competing lab?
Pay for enterprise or use one of the guaranteed no data retention models (e.g. Bedrock)
Science isn’t suffering from a lack of papers. It’s suffering from a lack of good papers. Making it easier to just pump out paper-mill publications is about the last thing science needs right now.
My hope is that the flood of AI articles pushes the academic publication system to its highly-anticipated breaking point.
The most absurd part is that everyone in academia knows that publish or perish is tremendously damaging to real research. Yet we’re all hostage of this system that we created in the name of “merit” and “efficiency”.
We need a different system to identify and reward talented hard-working people. Back in the day it all relied on actual interpersonal interaction and subjective judgment, but there were also much fewer researchers worldwide.
> My hope is that the flood of AI articles pushes the academic publication system to its highly-anticipated breaking point.
This will just make research inaccessible to most researchers. There is no incentive to limit publishing, at all, other than at the highest echelons. Publish or perish will just become worse. Look at what is happening to programming and extrapolate that to research work.
And all for what? Just to keep up this facade of society until most of society can be excised, whether artificially or naturally though lack of reproduction.
Oh it's getting there. I've turned down several referee requests this year because the paper looks like AI slop. A lot of it seems to come from China.
Scientific research is suffering from a reproducibility crisis. Not a publication crisis. LLM's aren't going to solve reproducibility issues.
They are going to make it a thousands times worse.
It wasn't perfect before, but it at least took some time to fake a paper. The problem is now people can produce a very plausible looking completely fake paper in minutes. Peer review is in the process of completely collapsing, in fact I think it's already basically done.
The only way this might fix things is if we require all papers are completely reproducable (that doesn't help in subjects like biology of course. They can still provide all the experimental data in the rawest format possible which doesn't break any laws).
I'm actually quite excited for when (if) the models get good enough to start replicating compsci papers. I'd love it if there was a system which calculated a reproducibility score per-lab or per-researcher, which I could look up alongside their citation count.
I want to see who did the hard work properly, and who focused on publishing with concealed details.
The two feed into each other. "Publish or perish" ups the incentive to pump out shaky papers to pad resumes. LLMs make it easier to churn them out.
Underlying reproducibility is integrity.
Underlying integrity is rigor.
Underlying rigor is education.
It goes deep, for sure, IMO.
It seems to me that LLM's could massively improve reproducibility issues if journals would require that the papers be reproducible by model X using a standardized prompt in < N minutes, etc...
it's suffering from having 1 million researchers, when there aren't 1 million important easy problems to solve, yet you must publish something
it could also be said that scientific interpretation is suffering from a framework crisis. the scientific convention of experiment, is the test of an hypothesis, as a logical construct.
repetition of materials and methods toward reproducibility, holds far less wieght than multiple variants of process designed to test a common hypothesis resulting in agreement.[null, or failure to null]
They're gonna worsen it
Isn't this just blanket cynicism?
In the long run conceivable we could use AI to hold papers to a much higher standard, audit all the data and code that is associated etc.
> audit all the data and code that is associated
For a while now there has been very little incentive for providing these alongside the paper, and I don't see why exactly 'AI' would change this. I could even see how making it vague to be harder to test with LLMs could be profitable for citation hackers.
You can imagine using AI agents to tag papers that don’t have code or similar work attached and just filtering them out.
The Chinese open source community has made a lot of incentive to make research reproducible for example. The most reproducible works from I.e. deepseek get widely cited and adopted.
I don’t think we can just say “AI” and it’s fixed but with deliberate effort there’s reason to be optimistic.
Unless reviewing becomes more profitable than publishing, anything that makes both easier will drive one up far more than the other. And it is difficult to conceive of something that would make reviewing much easier without making publishing much easier.
Just as a counterpoint ML and AI research has become much more reproducible over time. I feel like this is relevant because ML / AI researchers are huge power users of AI tools.
Between 2016 and 2021 the share of ML/ robotics/ AI researchers being reproducible (ie contianing code and similar instructions to reproduce) doubled [1].
The major US labs have gone largely closed source (I.e. they no longer publish frontier research) but the Chinese ecosystem has incredibly reproducible code.
This is field dependent obviously but I think it atleast gives reason to be optimistic.
Yes people will churn out fake slop research, but it feels like that can be categorized and then ignored.
[1] https://arxiv.org/pdf/2308.10008
That's good to hear about ML and AI research, but most research is not based on computers and so would require laboratory setups to reproduce. Not only is trying to reproduce such findings (beyond what is effectively a sanity check) through simulations a lost cause, if AI can reproduce such research it would be capable of doing such research itself... in which case it would be far more fruitful to use AI to do further research.
This thread is about a product based on fully reproducible research though so I feel like we should stay grounded. Claude science is meant to be used in the context of reproducible science research, there is a decent reason to not be cynical on future research being reproducible.
> if AI can reproduce such research it would be capable of doing such research itself
Well there is a big distinction between research validation and research generation, it is generally much easier to verify that a math proof is true or false than to find a truly novel proof.
But yes in the long run I’d think AI will be doing tons of research and it will by default reproducible. So maybe we’re aligned after all?
Por que no los dos? Scientific review times are up, it’s harder to find reviewers, and many reviews are AI generated anyway. Auto-generated research publications will arguably make the replication crisis worse, because there will be more slop to clog up the review system, and these papers will presumably be just as (if not more) not reproducible than human written science
In some fields like comp sci, when code isn't given but the paper describes the approach, LLMs do help with the reproducibility crisis: you can ask it to reproduce the result through reimplementation by reading the paper.
If it fails you may have to double check it did properly reimplement it, but if it succeeds you do get a reproduction.
impressive to me, but sadly i feel a little misleading since this is only the data-science part of life sciences.
every few weeks though i test claude and chatgpt on their scientific reasoning and it has definitely improved over time. in my experience without specific instruction on what is known/unknown they typically are lagging behind the leading edge of the field (dev bio/pluripotency in my case). probably because scientific research articles are not open-source so they can't crawl them.
claude has definitely outperformed chatgpt in this regard however, it's scientific reasoning is impressive.
Looks like Cursor and Jupiter Lab had a baby.
The fact that we are coming up on a month of Fable being unavailable with essentially zero actual signal from Anthropic around when it may be back is crazy to me. Yet still we have these random new products coming out?
https://xcancel.com/AnthropicAI/status/2070665903440871779
> Anthropic @AnthropicAI Jun 27, 2026 · 12:29 AM UTC
> Since June 12, we’ve been working closely with the US government to restore access to Claude Mythos 5 and Fable 5. Today, the government notified us that Mythos 5, our strongest cybersecurity model, can be redeployed to a set of US organizations that operate and defend critical infrastructure.
> We’re restoring access for these organizations quickly, and we’re continuing to work with the government to expand access to Mythos 5 and make Fable 5 available for general use again.
This thing is also surprising considering Fable was not allowed to answer any biology questions.
I mean the company has like 3k employees or more right? Lots of them are just working on more applied AI use cases that don't require frontier AI just the right integrations and structure etc.
Opus 4.8/ GPT 5.6 level models with the right workflows/ data/ access are still good enough to do huge amounts of economically valueable work.
This plus it's entirely plausible their employees have access to Fable or their own other pre-released models internally. Other than the perks you mentioned they've got excellent distribution too.
Big Pharama = Big Budgets.
So targeting them with a tailored product is understandable.
pharma is currently in a tailspin and not really spending money. they'd rather outsource everything possible to china or india right now.
Eli Lilly's recently partnered with NVIDIA to spend a lot of money for a new research lab in the Bay Area so not entirely
Download for mac. Find out I need a different subscription. Cannot quit program (must force quit).
Perhaps I need AI to use it.
They forgot to include an example of prompt error on “cancer” with Fable in that “nice” video.
It has Sonnet 5 as a usable model. Interesting.
Looks like they've just announced it - https://www.anthropic.com/news/claude-sonnet-5
Just released!
Claude Sonnet 5
https://news.ycombinator.com/item?id=48736605
Thought I'd give it a whirl - crashed immediately.
I was tickled they had a "Download for linux" button prominently shown, but nothing yet.
So I guess they released this instead of Sonnet 5?
Weird that it runs as a local webserver rather than as an app
I've always found that what science is really lacking is closed, proprietary ecosystems trying to build for-profit moats around research.
Thank our lords at Anthropic for stepping into this void
"Pre-configured for your domain [...] cheminformatics" as in something like ChEMBL?
Why would you people ever use this companies products? They're actually evil and are trying to scam you and or make you unemployable./worthless. You people really gotta wake up.
maxed out on coding improvements so now they're trying to expand to other markets
Why have they talked about this for a long time? They predicted date of code maxing out, and did so not from fitting a sigmoid or something but they predicted it would max out right during a steep part of the slope?
Blog post: https://www.anthropic.com/news/claude-science-ai-workbench
Why does HN let OpenAI and Anthropic basically advertise but it throws down the gauntlet at a small developer like myself when we do "self promotion"?
Top 3 posts as of this moment are all about Claude.
DoA
Disappointing that science came after cowork. Shows how their priorities are for profitability first and help humanity second.
Now this... this is a hot take. How exactly do you expect these companies to "help humanity" if they're bleeding money?
whats up with all these samosa? Samosa Manuscript, Samosa Benchmarking?
Another overrated packaged workspace to drain more usage... No thank you.
Dude! Give me some stolen science!
Claude: "Not that science"
> every step from data wrangling to *publication*
Do they have no shame?
Edit: seems like no https://news.ycombinator.com/item?id=48736814
this a great application for the sycophantic, non-deterministic lying machine!
It's called Claude Science, not Claude Politician.
Bill Maher ass joke
How about no?
AI brand identity has made the unfortunate pivot to "how much do you trust us" which is going be a real race to the bottom. I don't want LLMs managing nuclear reactors or replacing junior lab technicians. I don't trust any of these LLMs to do the bare minimum, regardless of how good it is for your brand.
It's gross watching these stunts unfold. Next ChatGPT will fly a passenger jet, which Claude will one-up with an agentic surgery, which OpenAI will respond to by putting a humanoid robot on the moon. If this is what 21st century market competition looks like, we are all fucked.
Meanwhile in the real world, these Math Olympiad AIs can't even take your fast food order correctly.