Tech Podcasts Rankings

How They Work

These rankings are updated daily and use combine several sources of data to estimate listenership. The question these rankings attempt to answer is: “What is the popular podcast and podcast episode on a particular dev topic.

This project is a work in progress.

Back Story

There are a lot of great podcasts out there and I certainly don’t have time to listen to them all. In creating CoRecursive, I take inspiration and guest ideas from a number of tech podcasts and conference talks.

To help me find guests I had a collection of scripts I built up over time that helped me identify what podcasts where popular and what episodes of a particular show were fan favorites. The rankings here are based on those scripts.

I’ve also found Apples Podcast rankings less than useful when you get into niche podcasts. Some developer podcasts are found in ‘Technology’, some in ‘News->Technlogy’ and others scatters around various other categories. And within those categories podcasts about building software mix with podcasts about cryptocoins mix with podcasts about big tech and gaming releases. The categories are far too coarse for my purposes. So this is my attempt to do better.

After seeing the great work Monica Lent did with creating Developer Blog Trends, I decided I should package up my scripts into something similar. ( I don’t have her aesthetic taste nor front-end skills, so my version is less pretty).

This is the result and it’s a work in progress. Send you suggestions to adam+rankings@corecursive.com

How It Works

I’ve hand curated a list of developer and tech targetted podcasts and grouped them in categories. (If you have a suggestion for something I’ve missed email me). Daily, a collection of bash, awk and python scripts run, that per podcast gather data from public sources and attempt to estimate popularity.

That data is then used to generate the ranking pages, which are static Jekyll pages. This whole thing happens in a GitHub Actions cron task and is best described as extremely janky.

Observations From The Data

  • Many great podcasts are infrequent enough, or small enough they don’t show up in my data set. (I had a Scala category, but there wasn’t enough data to show anything)
  • Podcasts about JavaScript and Python dominate (presumably because these langauges are super popular)

Plan / To Do

  • Find way to group and include more broader ‘tech’ podcasts
    • What are the sub-categories to break them down into?
  • Add episode exclude list
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