Vibe coding better content analytics
It’s been… a while since I posted here, but I wanted to share a little vibe coding project I’ve been working on during my time as a John S. Knight Journalism Fellow at Stanford: It’s a rough prototype of a tool that demonstrates what I call “value analytics” (as opposed to the volume analytics we primarily use in the news industry). In particular, I am introducing two new metrics:
- Quality completion rate: What percentage of users who started reading this article completed it in a reasonable amount of time? (To compute what’s reasonable, we measure the interval between when the user starts scrolling and when they reach the bottom of an article and compare that against the article’s “minimum skimmable” engagement time based on word count. If the dwell time on the article is at least as big as the minimum reasonable dwell time, we log a completion.) The Guardian has a version of this metric they call “deeply read“.
- Value votes: How many completing users said the article they just read was valuable (compared to the total number of completions)?
Combined, these metrics go way further than volume metrics (like page views, engaged time and conversions) at capturing the actual value a user receives from a given article. (They can also form the basis of some interesting user-level engagement metrics, but that’s for a different post.)
How does it work? Gradually scroll to the bottom of this post and you’ll see. If you don’t scroll too fast, you should see a prompt to give feedback on whether this article was valuable. (There is frequency capping, so you might not see the prompt on every page view.)
A dashboard I built in Looker Studio then aggregates the responses into value metrics by article:
If you want to see more about what is going behind the scenes, pull up this article in debug mode.
What do you think? Valuable?
I am an ex-Flash user. I uninstalled the Flash plug-in on my primary browser about a month ago, and I haven’t looked back. Here’s how it happened:



