Here’s what my daily reading habit looks like: scroll through Facebook and Twitter feeds, the various Flipboard sections, first page of Hacker News, skim through the headlines/snippets, and save to Pocket anything that I might want to read fully. And I always try to get through all these saved links before the end of the day.
And this is excluding books & magazine (New Yorker, etc) reading.
So there is a final layer of curation (me) above a layer of curation (Flipboard) + social selection (Twitter/Facebook/HN).
What’s missing here?
Serendipity. The Flipboard/Twitter/etc. layer is above another self-curated layer where I choose which people and what topics I’m interested in, thus excluding myself from anything that is outside of those bounds.
Weak Signals of what my network is reading. What they share is a strong signal of what they really like, but they don’t necessarily share everything they liked reading. And enough occurrences of the same weak signal could make it a strong one.
Conversations that happen in the comments sections or tweet threads often have a lot of rich, varied opinions and information. I don’t even get to these and even if I tried to read them all, it would not be tractable because of the sheer mass. An aggregation + summarization would be perfect.
Contextual Relevance. 4-5 years ago a number of startups were working on various contextual algorithms to find you news you’d be interested in. I wonder what happened to them all. I’d love to discover news/other articles based on what I’ve been searching on Google, places where I’ve been checking in, links I save to Pocket, etc.
And because everyone likes getting more than what they expected, here’s a bonus 5th one:
- Non-Real time stuff. The problem of everyone being online all the time means that you have to be too. It is not easy to catch up on a few days’ worth of missed reading. Aggregation and ranking by popularity + relevance would be very valuable. Quora and LinkedIn do it somewhat already.