Google tracking everything we read is bad, particularly since Google abandoned the “don’t be evil” plan and are presumably open to being somewhat evil.
The article recommendations on Chrome on Android are useful and I’d like to be able to get the same quality of recommendations without Google knowing about everything I read. Ideally without anything other than the device I use knowing what interests me.
A ML system to map between sources of news that are of interest should be easy to develop and run on end user devices. The model could be published and when given inputs of articles you like give an output of sites that contain other articles you like. Then an agent on the end user system could spider the sites in question and run a local model to determine which articles to present to the user.
Mapping for hate following is possible for such a system (Google doesn’t do that), the user could have 2 separate model runs for regular reading and hate-following and determine how much of each content to recommend. It could also give negative weight to entries that match the hate criteria.
Some sites with articles (like Medium) give an estimate of reading time. An article recommendation system should have a fixed limit of articles (both in articles and in reading time) to support the “I spend half an hour reading during lunch” model not doom scrolling.
For getting news using only FOSS it seems that the best option at the moment is to use the Lemmy FOSS social network which is like Reddit [1] to recommend articles etc.
The Lemoa client for Lemmy uses GTK [2] but it’s no longer maintained. The Lemonade client for Lemmy is written in Rust [3]. It would be good if one of those was packaged for Debian, preferably one that’s maintained.
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