Its funny how its mostly US-based libertarian types concerned about AC in Europe. There are plenty of things Europe over-regulates, but this is not it.

> What makes the “simulate inputs” approach work is that the engine takes utmost care to keep calculations identical on each client. This is not trivial because you still have to work with things that naturally differ on each client, such as mouse position or which units are selected - this is called the unsynced state. On top of that, there can be hardware differences that have to be worked around to get identical results - the huge effort involved is one of the reasons why Recoil is not available outside x86-64.

https://recoilengine.org/articles/netcode-overview/

And also it has inspired a few other clojure datalog databases, so there is much more choice: * https://github.com/datalevin/datalevin * https://github.com/replikativ/datahike * https://github.com/threatgrid/asami

There is also xtdb, but it abandoned datalog and is going in another (albeit interesting direction) https://xtdb.com/

here is a comparison website, but it is somewhat out of date: https://clojurelog.github.io/

Comments URL: https://news.ycombinator.com/item?id=46807073

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So it must have tools save, manipulate and visualize conveniently (pretty printing, folding etc) the values of vars that contain nested maps, sequences, sets, etc.

Existing clojure IDEs like CIDER for emacs or Calva for VSCode do that too, and it is a must have to have a nice experience with the language

For me the best textual interface I've ever used remains Magit in Emacs: https://magit.vc/ I wish more of Emacs was like it.

I actually use emacs as my git clients even when I'm using a different IDE for whatever reason.

Comments URL: https://news.ycombinator.com/item?id=45013356

Points: 1

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https://lispcookbook.github.io/cl-cookbook/strings.html

https://clojuredocs.org/clojure.string

For our team, it is very simple:

* we use a library send traces and traces only[0]. They bring the most value for observing applications and can contain all the data the other types can contain. Basically hash-maps vs strings and floats.

* we use manual instrumentation as opposed to automatic - we are deliberate in what we observe and have great understand of what emits the spans. We have naming conventions that match our code organization.

* we use two different backends - an affordable 3rd party service and an all-on-one Jaeger install (just run 1 executable or docker container) that doesn't save the spans on disk for local development. The second is mostly for piece of mind of team members that they are not going to flood the third party service.

[0] We have a previous setup to monitor infrastructure and in our case we don't see a lot of value of ingesting all the infrastructure logs and metrics. I think it is early days for OTEL metrics and logs, but the vendors don't tell you this.