alt Hacker NewsClaude Science
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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.
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.
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.
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.
This seems to have unblocked Claude Desktop for Linux ( https://code.claude.com/docs/en/desktop-linux )
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.
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..
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?
[reviewer] verifier_mode=default-on downgraded to off: pro subscription tier, autoReviewer withheld (frame=f2a81cb2)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?
They forgot to include an example of prompt error on “cancer” with Fable in that “nice” video.
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
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?
Big Pharama = Big Budgets.
So targeting them with a tailored product is understandable.
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.
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?
Download for mac. Find out I need a different subscription. Cannot quit program (must force quit).
Perhaps I need AI to use it.
Weird that it runs as a local webserver rather than as an app
"Pre-configured for your domain [...] cheminformatics" as in something like ChEMBL?
maxed out on coding improvements so now they're trying to expand to other markets
DoA
whats up with all these samosa? Samosa Manuscript, Samosa Benchmarking?
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.
Disappointing that science came after cowork. Shows how their priorities are for profitability first and help humanity second.
Another overrated packaged workspace to drain more usage... No thank you.
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!
[dead]
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.
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.