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Joined 1 year ago
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Cake day: July 3rd, 2023

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  • If you get prep time you could set up some traps.

    Assuming both sides see it as a fight to the death, the horse will also engage so you could just run away into a bunch of traps. All you need is for the horse to injure a leg in one trap and it’s done for. I think even just some holes with a couple spikes would be enough to injure and maybe even sprain an ankle.

    Without prep time you’re pretty doomed, I think your best bet is either climbing up a tree to buy you some prep time to make a spear out of the branches or worst case diving in, aiming to do damage to its legs (unlikely) and hope you are able to get out without being trampled (unlikely)






  • Oh yeah, it’s actually pretty extensive and expressive. If you’re interested in this sort of stuff it’s worth checking out the IR language reference a bit. Apparently you can even specify the specific garbage collection strategy on a per-function basis if you want to. They do however specify the following: “Note that LLVM itself does not contain a garbage collector, this functionality is restricted to generating machine code which can interoperate with a collector provided externally” (source: https://llvm.org/docs/LangRef.html#garbage-collector-strategy-names )

    If you’re interested in this stuff it’s definitely fun to work through a part of that language reference document. It’s pretty approachable. After going through the first few chapters I had some fun writing some IR manually for some toy programs.










  • I know they are used in google’s BigTable. All data there is stored in seperate SSTables and you can specify that a locality group should have bloom filters generated for its SSTables. Apparently cassandra has them too.

    Both are the same general application though and you already mentioned databases.

    I did think about using them at some point for authentication purposes in a webservice. The idea being to check for double uses of a refresh token. This way the user database would need to store only a small amount of extra storage to check for the reuse of a refresh token and if you set the parameters accordingly, the false positives are kind of a benefit in that users cannot infinitely refresh and they actually have to reauthenticate sometimes.

    Edit to add: I also read a paper recently that uses a datastructure called a collage that is closely related to bloom filters to perform in-network calculations in a sensor network. If I understand correctly, the basic idea there is that every node in the network adds a bit to the datastructure while it is in transit, so data from the entire network is aggregated. The result can then be fed to a classifier ML model. (Source: Oostvogels, J., Michiels, S., & Hughes, D. (2022). One-Take: Gathering Distributed Sensor Data Through Dominant Symbols for Fast Classification. )