Tags are like pre-computed search terms that aim to figure out what podcast episodes are about. Aside from a few synonyms there isn't any magic going on as you can probably see from the results below.

However, there is a lot of room for improvement and there are a couple of changes on the horizon:

How can we fix this?

1. The actual tags aren't so great right now, for example an astute observer would notice the tags "ai" and "artificial intelligence" are both in the list. Worse, "ai" has a suspiciously high count because there are some low value matches being returned from other words that contain the letters "ai". This is easy to fix as I have both a white list and black list of terms that I can use to tweak the tags.

2. Tags really ought to have a hierarchy. For example, if an episode is about React, then it is also about JavaScript. Same with Kubernetes and DevOps.

3. There are a lot of "none" results. This is a combination of weak episode descriptions and topics that are hard to classify by search. Examples include things like "soft skills" or "career management". I think we'll need a bit of a human touch to handle these items.

All Tags

Note: Because of some caching there may be a small discrepancy between the number you see below, and the count you see after clicking. The number over on QIT is closer to real-time.