stonerri

I'm amazed ticalc.org is still alive and kicking. So much nostalgia. Joltima was what convinced me to learn assembly. So far ahead of its time on the TI-86. Full featured RPG with turn-based combat on a graphing calc. Glad the history is still accessible online.

Volta Labs (YC W19) uses this technology to prepare samples for DNA sequencing. https://news.ycombinator.com/item?id=40075571

While it’s not in the ARPA-H announcement, the broader press coverage is using the moonshot frame. https://www.nytimes.com/2024/08/13/us/politics/biden-researc...

Check out vtk.js (and VTK proper) as a fairly mature implementation of this. I haven’t kept up with the development but it was pretty far ahead when it came out years ago.

Yes. I don't use it heavily, but I have set up an account and added a moderate number of articles to it. It has an option to notify you of new, relevant publications that works pretty well. It tends to find articles my pubmed RSS searches / google alerts miss.

The second layer is hard. I tried something in this space in mid-2018. Full text extraction and sentence segmentation tech was adequate, but extracting the discourse tree and building the graph was a bit of a struggle (trying to repurpose a collection of academic/open tools to get something useful). Never published or released the code.

If interested, a few rabbit holes to explore (no affiliations):

https://scite.ai -> best option for citation mapping, but same issues you described above

https://www.semanticscholar.org and AI2 -> the best group working on tooling in this space

https://www.weave.bio -> early startup trying to build this out

The hardest challenge in my view is solving the intermediate representation issue. You have to establish a DSL/nomenclature that provides the range required to represent a complete scholastic discourse while also being computable.

As someone who just got the 7B running on a base MacBook M1/8GB, I strongly agree. The rate of tool development & prompt generation should see the same increase that Stable Diffusion did a few months (weeks?) ago.

And given how early the cpp port is, there is likely plenty of performance headroom with more m1/m2-specific optimization.

A biorxiv paper on the neural sewing technology: https://www.biorxiv.org/content/biorxiv/early/2019/03/14/578... - I believe the authors are now at Neuralink.

LateX Lab was/is exactly that: http://code.google.com/p/latex-lab/

A demo is up and running at http://docs.latexlab.org/

* not affiliated with the project in anyway, just remember using it awhile back.

^^ What he said.