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Lakshmi Sravya Vedantham
Lakshmi Sravya Vedantham

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I built a tool that tells you what developers are actually learning — not just Twitter hype

#python #opensource #programming #productivity

Every few months there's a new "hottest technology" trending on Twitter. Half the time it's just noise from a few loud accounts.

I wanted to know what developers are actually gravitating toward — based on what they're building, writing, and discussing.

So I built trend-radar: a Python CLI that pulls real signals from three sources and ranks them by weighted growth velocity.

The Problem

Tech hype is asymmetric. A single viral tweet can make a niche tool look dominant. Meanwhile, slow-burning technologies that millions of developers are quietly adopting get ignored.

The only way to cut through this is to look at behavior, not opinion:

  • What are developers actually building? (GitHub)
  • What are they writing about? (Dev.to)
  • What are they discussing? (Hacker News)

What trend-radar does 

pip install trend-radar
trend-radar
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It fetches trending data from all three sources in parallel, normalizes the signals, weights them (GitHub 50%, Dev.to 30%, HN 20%), and outputs a ranked table:

 Technology    GitHub  Dev.to  HN    Score
 Rust          ████    ███     ███   89
 Bun           ████    ████    ██    84
 Deno          ███     ███     ██    71
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You can also get raw JSON for scripting:

trend-radar --json --top 10
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How it works

Three independent collectors run concurrently via ThreadPoolExecutor:

GitHub collector — scrapes github.com/trending and counts repos per programming language/tool using BeautifulSoup.

Dev.to collector — hits the Dev.to public API (/api/tags) and pulls tag post counts as a proxy for what developers are writing about.

Hacker News collector — uses the HN Firebase API to fetch top story titles and counts keyword mentions across them.

The analyzer normalizes each source to a 0–100 scale, applies the weights, and returns a sorted TechScore list. The display layer renders it as a rich terminal table.

What I learned

Scraping is fragile, APIs are gold. The GitHub scraper works today but GitHub changes their HTML occasionally. The Dev.to and HN APIs are stable and versioned — much more reliable.

Signal weighting matters more than collection. Getting the data is the easy part. Deciding how much to trust each source (and why) is the actual design problem.

ThreadPoolExecutor makes IO-bound fetching trivial. Three HTTP calls that each take 2–3 seconds run in under 4 seconds total. The code is about 10 lines.

Try it 

pip install trend-radar
trend-radar --top 20
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Source: github.com/LakshmiSravyaVedantham/trend-radar

Would love to hear what you think — and what sources you'd add to make the signal stronger.

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