Personal Logging was a session at IndieWebCamp Austin 2019.
Notes archived from: https://etherpad.indieweb.org/personallogging
IndieWebCamp Austin 2019
Session: Personal Logging
When: 2019-02-23 15:40
- Stephen Bowling (session facilitator)
- David Shanske
- gRegor Morrill
- Aaron Parecki
- Chris Aldrich
- Add yourself here… (see this for more details)
Stephen has a lot of siloed data that he tracks and is interested in putting it all in one place where he's in control. Visualization of yearly activity would be cool, on-this-day feature.
Discussed watch, listen, read posts briefly.
Mentioned cleverdevil's podcast listen tracking, via Overcast's OPML feed. Overcast.fm#How to export your data
- done as individual posts
- there have been requests to update posts for want to read/reading/finished reading
- Why not do a collection based on book title?
- There is an ISBN view that shows everyone who has read/interacted with a particular title -- it's not an often used feature
- But different editions of the same book can have different ISBNs (eg different between hardcover, e-book, and paperback)
- Supports a variety of post types in core and has additional plugins to extend the functionality
- There are no free hosted accounts anymore
- Easy turn-key solution for those who can self-host and install it
Gatsby - a react static site builder with lazy loading, graphQL
idea: micro.blog feed to syndicate to one's personal site?!
Everything on Stephen's site is private by default, then he makes it public selectively
Last.fm is something he'd like to do, but it's a huge stream of data
Aaron: Listens from Last.fm and steps from Fitbit are issues I'm trying to figure out how to log, especially since his site only has individual posts put into a timeline, so what does a hourly/daily summary type post look? With summaries, what about timezones? How to do daily summaries, particualrly when traveling across multiple timezones on a regular basis.
Channels in Microsub that don't necessarily do time ordered feeds Find like posts of all subscriptions and then aggregate them to surface "popular" posts Instead of exposing hundreds of songs by time, but roll them up by "favorite", "most frequently listened", etc.