Every month the "Ask HN: Who is hiring?" thread gets 300+ job posts, and reading them by hand takes forever. So I built a small open-source tool that pulls the whole thread into a clean CSV/XLSX in a few seconds. How it works: Finds the newest thread from whoishiring via the Algolia HN Search API, then fetches every top-level comment from the official HN Firebase API. No HTML parsing, so nothing breaks when the page layout changes. Async fetching with httpx + asyncio, capped at 20 concurrent requests, with exponential-backoff retries on timeouts, 429s and 5xx. A heuristic parser that splits the usual Company | Role | Location | ... first line and pulls out remote/hybrid, salary, tech stack, apply links, and de-obfuscates emails like name [at] co [dot] com. Skips meta comments, job-seeker replies and deleted posts. --keywords "python,scraping,contract" adds a match column, plus --remote-only and --only-matches filters. Exports CSV and a formatted XLSX (frozen header, auto-filter, clickable links), with optional Google Sheets push. On the October 2026 thread it processed 319 top-level comments and exported 255 job posts. Code (MIT): https://github.com/Future6524/hn-jobs-scraper 55-second demo: https://youtu.be/vXRHD_D9cBk The hardest part was the parser, since everyone formats their first line a bit differently. If you've dealt with messy semi-structured text like this, I'd love to hear how you handled it.   submitted by   /u/PRETTYsaveliev [link]   [comments]