I wanted to share a little side project of mine that I created while tinkering around with GPT-3.
The project uses the Algolia HN Search API [1] to retrieve the "Who is hiring?" posts from HN and then parses them with the help of GPT-3 / GPT-3.5 (I do not have API access to GPT-4, yet, but it already works quite well even with the older models). It then puts the job postings into a structured list that is hopefully easier to skim than the original postings. There are some additional features like sorting jobs by semantic similarity (based on the text embeddings from OpenAI). Filtering, sorting and saving favorites is implemented client-side, so your data and preferences remain local to your browser.
Originally, this wasn't even meant to be a public product, but if people find it useful (and HN is fine with it), I'll try to keep it running. I've also written a short article about how the parsing works behind the scenes [2]. It's quite amazing how easy many of the classic NLP tasks have become with the newer LLMs.
Happy to answer any questions about the project!
[2] https://marcotm.com/articles/information-extraction-with-lar...
For example, for the March one it is ID 34983767 (from the algolia search or a "there's only so many of them, here's a list that I'll add to each month").
You can then get a list of all the top level comments at https://hacker-news.firebaseio.com/v0/item/34983767.json?pri...
And then pulling up a comment at https://hacker-news.firebaseio.com/v0/item/35255027.json?pri... to not have to parse any of its child comments or the HTML of the page.
(late edit: and re-reading the blog post while not trying to pay half attention to a meeting... that is what you are doing)