For the story summary: send a prompt to the Perplexity API (which can access URLs) requesting an extraction of the article's content (a sort of very detailed technical brief). Then, use Gemini to classify the results from the Perplexity API as valid or invalid (sometimes, Perplexity isn't able to access the URL but it doesn't reliably return the string I've requested it to return when it isn't). After that, send a more detailed prompt to Gemini requesting the story summary be generated with a specific style and format (Markdown).

For the comments summary: use the HN API to pull all comments, then prepare a Markdown document with a selected subset of comments (based on user karma, replies, and thread depth) if exceeding a given number of comments, otherwise all comments. Annotate comments with an index (eg, 1.1) to indicate the nesting to the LLM. Along with some formatting and stylistic guidance, send that to the LLM requesting summarization.

While the story summaries are generally static once generated, regenerate the comment summaries on an interval (this could be optimized).

Hopefully that helps a little!

"Ah, the concerns of the involved... Bakers and their care towards flour"

I think it was a good idea and implementation; there will be ways to perfect it.

And it will be useful as learning material for other applications.