Social previews would be great to add

https://socialsharepreview.com/?url=https://nowigetit.us/pag...

One LLM feature I've been trying to teach Alltrna is scraping out data from supplemental tables (or the figures themselves) and regraphing them to see if we come to the same conclusions as the authors.

LLMs can be overly credulous with the authors' claims, but finding the real data and analysis methods is too time consuming. Perhaps Claude with the right connectors can shorten that.

1. Add a donate button. Some folks probably just want to see more examples (or an example in their field, but don't have a specific paper in mind.)

2. Have a way to nominate papers to be examples. You could do this in the HN thread without any product changes. This could give good coverage of different fields and uncover weaknesses in the product.

probably need to have better pre-loaded examples, and divided up more granularly into subfields. e.g. "Physical sciences" vs "physics", "mathematics and statistics" vs "mathematics". I couldn't find anything remotely related to my own interests to test it on. maybe it's just being populated by people using it, though? in which case, I'll check back later.

I increased today's limit to 100 papers so more people can try it out

Feedback:

Many times when I'm reading a paper on arxiv - I find myself needing to download the sourced papers cited in the original. Factoring in the cost/time needed to do this kind of deep dive, it might be worth having a "Deep Research" button that tries to pull in the related sources and integrate them into the webpage as well.

https://nowigetit.us/pages/9c19549e-9983-47ae-891f-dd63abd51...

The actual explanation (using code blocks) is almost impossible to read and comprehend.

- https://mlu-explain.github.io/decision-tree/

- any article from distill.pub

- any piece from NYT

100 papers processed.

Cost breakdown:

LLM cost $64

AWS cost $0.0003

Claude's editorial comment about this breakdown, "For context, the Anthropic API cost ($63.32) is roughly 200,000x the AWS infrastructure cost. The AWS bill is a rounding error compared to the LLM spend."

Category breakdown:

Computer and Information Sciences 41%

Biological and Biomedical Sciences 15%

Health Sciences 7%

Mathematics and Statistics 5%

Geosciences, Atmospheric, and Ocean Sciences 5%

Physical Sciences 5%

Other 22%

There were a handful of errors due to papers >100 pages. If there were others, I didn't see them (but please let me know).

I'd be interested in hearing from people, what's one thing you would change/add/remove from this app?

But it got me interested in a topic I have been calling "token economization." I'm sure there's a more common term from it but I'm a newb to this tech. Basically, how to optimize the "run rate" for token utilization per request down.

Have you taken a stab at anything along this vein? Like prompt optimization, and so on? Or are you just letting 'er rip and managing costs by reducing request volume? (Now that I've typed this comment out I realize there is so much I don't know about basic stuff with commercial LLM billing and so on.)

[1] https://github.com/mattdeboard/itzuli-stanza-mcp

edit:

I asked Claude to educate me about the concepts I'm nibbling at in this comment. After some back-and-forth about how to fetch this link (??), it spit out a useful answer https://claude.ai/share/0359f6a1-1e4f-4ff9-968a-6677ed3e4d14

I could change to a simple cost+ model but don’t want to bother until I see if people like it.

Ideas for splitting the difference so more people can use it without breaking my bank appreciated

Maybe a combo where I keep a list and automatically process as funds become available.

Totally agree with what you're saying. This tool ignores supplemental materials right now. There are a few reasons - some demographic, some technical. Anything that smells like data science would need more rigor.

Have you looked into DocETl (https://www.docetl.org/)? I could imagine a paper pipeline that was tuned to extract conclusions, methods, and supplemental data into separate streams that tried to recapitulate results. Then an LLM would act as the judge.

Interesting idea about pulling references. My head goes to graph space...ouch

For me personally, the pain point is being interested in more papers than I can consume so I’ve gotten into the habit of loading papers into LLMs as a way to quickly triage. This app is an extension of my own habit.

I also have friends without scientific backgrounds who are interested in topics of research papers but can’t understand them. The reason for the cutesy name, Now I Get It!, is because the prompt steers the response to a layperson