Something I've learned is that the standard, "Summarize this paper" doesn't do a great job because summaries are so subjective. But if you tell a frontier LLM, like Opus 4.6, "Turn this paper into an interactive web page highlighting the most important aspects" it does a really good job. There are still issues with over/under weighting the various aspects of a paper but the models are getting better.

What I find fascinating is that LLMs are great at translation so this is an experiment in translating papers into software, albeit very simple software.

If I turned this into a paid app then more attention would be given to quality. There’s only so much an app that leverages LLMs can do, though. With enough trace data and user feedback I could imagine building out Evals from failure modes.

I can think of a few ways to provide a better UX. One is already built-in - there’s a “Recreate” button the original uploader can click if they don’t like the result.

Things could get pretty sophisticated after that, such as letting the user tweak the prompt, allowing for section-by-section re-dos, changing models, or even supporting manual edits.

From a commercial product perspective, it’s interesting to think about the cost/benefit of building around the current limits of LLMs vs building for an experience and betting the models will get better. The question is where to draw the line and where to devote cycles. Something worthy of its own thread.