Ask HN: Best practice for posting links to large Mastodon threads?

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  • Mastodon

    Your self-hosted, globally interconnected microblogging community

  • Postmortem on what happened here: https://news.ycombinator.com/edit?id=39305884

    The v1 API of Mastodon limits the size of the tree that it will expand for users who are not logged into the server: https://github.com/mastodon/mastodon/blob/main/app/controllers/api/v1/statuses_controller.rb . I am guessing that this or some similar limit applies to threads being returned to unauthenticated users of the web UI. It just arbitrarily stops expanding the replies at some point, including the main thread from the OP.

    If a thread is truncated, users expect it to expand automatically and autoscroll when you hit the bottom. In my desktop browser, that does not occur, and there is no indication that there is more to see. This is the situation of the web interface as of Mastodon version 4.2.5.

    The issue is very sensitive to observer conditions. If you are logged into the server, the behavior is different. If you use a Mastodon app instead of the web, the behavior might be different. As the tree expands, the cutoffs become different. If you look at the thread on a different Mastodon server, the tree is different because every server has its own view of the Fediverse.

    HN needs a best practice for linking to Mastodon threads in a way that provides a consistent experience to HN readers. The average Mastodon server would be crushed by hundreds of HN readers grabbing the entirety of a huge thread all at once, so this might involve some thread-unroll-and-cache service. I tried https://mastoreader.io/ but it did not solve the problem.

    Alternately, we push changes into the Mastodon web UI to warn users when they need to click to see more and assume that people will get used to the navigation.

    Suggestions?

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
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