last.fm is one of my very favorite services. It's rough around the edges in some parts, but I've gotten incredible value from it. A couple of websites built on it that I check out from time to time:

- https://lastfmviz.netlify.app/ - shows what you've been listening to as a grid of album covers. You can scroll down as long as you want. It's cool to look back and remember where I was when listening to specific music.

- https://lastfmstats.com/ - generates tons of rankings, line charts, racing bar charts, etc. A couple I like: "Artist streaks" (I listened to Pavement tracks 122 times in a row in August 2023), "Unique artists in a single month" (225 in July 2025) and "Unique weeks per artist/album/track" (good to identify what you're always listening to vs. what you listened to heavily in a specific time)

- https://pmcdonough8133.github.io/last.timer/ - shows your listening rankings by hours, minutes instead of just scrobble count. This really should be a default feature in the site, as some artists have average track length 2-3x times of others.

If you use Spotify, another site I've had loads of fun with is https://explorify.link/.

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joenot443 2 hours ago | parent | next [-]

Give LastWave a try! My buddy built it back in college and updated it recently.

https://savas.ca/lastwave/

Generates a groovy wave chart of all your past listening.

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tyrust 5 hours ago | parent | prev | next [-]

> shows your listening rankings by hours, minutes instead of just scrobble count

I've wanted to build something like this for a long time, cool (and unsurprising, really) to see it's already done!

Swans is my number 30 by scrobbles but 4 by playtime, which makes total sense.

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quirino 4 hours ago | parent [-]

If you're a Spotify user, you can get even more precise data by downloading your listening data. The website I linked gets data from MusicBrainz and tries to fill in the gaps with an average, but even then it gets some things wrong.

E.g. Fishmans - Long Season is a 40 minute song, but the website's considers it as divided into 4-5 parts. And you don't have to listen to the full song to get a scrobble.

In the Spotify data you get the exact number of seconds you listened to it. And it is surprisingly complete and easy to use too. With LLMs I bet you can load it into pandas and construct queries for any insight you want in seconds.

	▲	tyrust 4 hours ago \| parent [-]
		Nice tip, but I use YouTube Music. I just downloaded my listening history, looks like they don't include listening duration, alas.

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tclancy 4 hours ago | parent | prev | next [-]

The middle one is fascinating. The first track I ever scrobbled is by an artist I have yet to listen to again in 22 years. Much of the longest gaps is taken up by bands I found or started to like due to Rock Band which came out around that time. Man I miss that too, we had 30 or 40 people over right after it came out and turned the house into a karaoke dive, right down to having to kick them off the couch the next morning.

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barcode_feeder 2 hours ago | parent | prev | next [-]

Thanks for sharing lastfmstats, wasn't familiar with that one! Delightful and nostalgic seeing the listens sliced as they have

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majora2007 2 hours ago | parent | prev | next [-]

Thanks for sharing, these are awesome ways to explore your data.

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palpfiction 4 hours ago | parent | prev [-]

a while ago I created this one, for when you want to listen to a familiar album, but can't decide which one: https://what-to-listen.chef-labs.deno.net/