Building a Tech Trend Dashboard from Hacker News Data with Python

Every day, thousands of developers discuss emerging technologies on Hacker News. What if you could turn that firehose of discussion into a structured trend dashboard? In this tutorial, we build a Python script that analyzes HN stories and comments to detect trending topics and visualize what the developer community cares about right now. The Data Pipeline We need stories and their comments. The official HN API gives you individual items, but fetching hundreds of stories plus nested comments is slow since each comment requires a separate HTTP call. For bulk collection, I use the HN Stories + Comments Scraper on Apify which grabs full story metadata and comment trees in one run. But you can adapt this to any data source. Assume we have our data as JSON: stories = [ { " title " : " Show HN: I built a Rust web framework " , " score " : 342 , " num_comments " : 156 , " comments " : [ { " text " : " Really fast compared to Actix... " , " score " : 45 }, { " text " : " How does this handle as