Danny Crichton makes some excellent points in this Techcrunch piece. RSS and Podcasts share very similar product design problems. The problems are are two-fold:
- Discovery is almost always word-of-mouth (the exception however is advertising on Overcast, which is a stellar podcasting experience to say the least).
- Curating your feed is currently topic-orientated, when it should be people-orientated. That’s the secret-sauce of Twitter Moments and Reddit. Or, to go deeper into the problem engagement is the signal these algorithms look for. Therefor a revival in RSS hinges on a product leveraging those signals, otherwise you’re just subscribing to hundreds — if not thousands of noisy RSS sources to jam up your unread feed.
Next, RSS readers need to get a lot smarter about marketing and on-boarding. They need to actively guide users to find where the best content is, and help them curate their feeds with algorithms (with some settings so that users like me can turn it off). These apps could be written in such a way that the feeds are built using local machine learning models, to maximize privacy.
An excellent point. Apple does this with aplomb for a number of their products. Photos, video collages, and even iMessage emoji suggestions all use machine learning and protect end-user data privacy. It’s a technique called differential privacy. Craig Federighi of Apple talked about this approach in his interview with Wired in 2016:
“Differential privacy is a research topic in the areas of statistics and data analytics that uses hashing, subsampling and noise injection to enable…crowdsourced learning while keeping the data of individual users completely private. Apple has been doing some super-important work in this area to enable differential privacy to be deployed at scale.”
In light of the recent Facebook personal-data implosion — I for one, hope RSS makes a comeback.