1F8LZF…8gsQ

i thought this was bitfinex lmao

🙇‍♂️ youre the best siri

/pay @2518 $2.18 as Series A investment

🙏 thank you for taking the time to explain this to me

ah this makes more sense

is this orientation in a geometric sense?

this reminds me soooo much of #∃Bit + #R2Я
https://twitter.com/SIRISYSPrime/status/1150868880941879301

meaning it is not a transaction of one thing exchanging for another, but rather a continuous transformation of that thing into a different state?

ohhhhhhhh so this is where CGP comes in

? ractor

oh that looks pretty interesting, quite scalable lol

https://www.bitcoinfiles.org/t/ e07afeb1da2635a2197ab34c217ff5ca07263f02c0d33c4208b660d2369cc64b

@695 is graph-store related to the "urbit-your-datastore" proposal awhile back? or is that still a different use case?
https://www.bitcoinfiles.org/t/e07afeb1da2635a2197ab34c217ff5ca07263f02c0d33c4208b660d2369cc64b

hahahaha in many ways you're spot on. indices off of a hash based placement model are basically that, just in abstract space https://infolab.usc.edu/DocsDemos/yao_vldbjournal.pdf (i *think h/t @1623)

mongo is a document store database, basically its a complex key value pair. mongo was created to store JSON objects over a decade ago during the rise of javascript. despite its popularity and ease of use, its a poor data storage mechanism at scale

though neo is quite interesting, i personally have never seen it scale it while outperforming relational databases. there is decades of knowledge for how to scale relational databases, the same can't be said for non relational (excluding columnstore/kvp)

interesting that it's more like mongo than neo

of course. there are a what we call SQL (relational) databases and NoSQL (non relational) databases. SQL is pretty set theoretic with joins, unions etc across tables, NoSQL is more open with four types:

• document store
• key value
• column store
• graph

neo4j is a graph database. this is based off of graph theory and handles every object as nodes and edges. this was kind of pioneered by the sparql movement awhile back, but wikidata uses graph databases as their backend for many things.

the reason this is important is bc mongo isn't particularly difficult to implement and i wouldn't call it a graph. frankly, it's just not that useful for scalable applications imo. i have not looked at urbit's yet implementation though so i can't judge

i havent really looked into urbit's graph store yet, but i just don't think mongo is that interesting or useful for scalable systems. i hope that there is more to it than that tbqh

if you really care about graph like data, geospacial indices within relational systems are probably one of the most interesting methods you could use to scale

document store databases allow you to "basically store anything". this freedom makes it extremely difficult to analyze the results, especially with large amounts of data. graph databases handle wildly recursive relationships, but that often isn't good

thats koala to you

this is also an amazing video on sphere packing in high dimensions
https://youtu.be/zwAD6dRSVyI

he's referring to maximal compression in 8 dimensions, proved here (h/t @1623) https://arxiv.org/abs/1603.04246

Lie theory
https://youtu.be/r_w408y_3rw