sliproad
CoinBLAS
sliproad | CoinBLAS | |
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3 | 3 | |
9 | 21 | |
- | - | |
0.0 | 1.8 | |
almost 3 years ago | almost 3 years ago | |
Go | Jupyter Notebook | |
GNU General Public License v3.0 only | - |
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sliproad
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Spacedrive – A File Explorer from the Future
I tried something similar with a project of mine[1], with a focus on API rather than frontend so I could build tailored apps for each device/user. At some point I should get around to finishing it -- I mostly focused on S3 and local filesystem compatibility, but being able to explore filesystems from other devices (maybe through an agent) is a neat idea.
[1] https://github.com/gmemstr/sliproad
- Show HN: Sliproad 2.1 – Merge Filesystems Together
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Ask HN: What Are You Working On?
Rewriting my storage application from Go to Rust, since it offers some better ergonomics for what I want it to do - Ideally I want to merge a bunch of different file and object storage providers together into one API/web interface: https://github.com/gmemstr/sliproad/tree/3.0-rust-rewrite
Also working on reading more books :)
CoinBLAS
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The "missing" graph datatype already exists. It was invented in the '70s
When you consider that a graph and a matrix are isomorphic, doing vector matrix multiplication takes a vector with a set value, say row 4, and multiplies it by a matrix where row 4 has values present that represent edges to the nodes that are adjacent to it (ie "adjacency" matrix). The result is a vector with the next "step" in a BFS across the graph, do that in a loop and you step across the whole graph.
A cool result of this is, for example, taking an adjacency matrix and squaring it is the "Friend of a Friend" graph. It takes every node/row and multiplies it by itself, returning a matrix that are adjacent to the adjacencies of each node, ie, the friends (adjacencies of the adjacencies) of friends (adjacencies) of the nodes.
Deeper traversal are just higher nodes, a matrix cubed are the friends of the friends of the friends.
A picture is worth a thousand words, see figure 7 of this paper:
https://arxiv.org/pdf/1606.05790.pdf
Also check out figure 8, this shows how incidence matrices can work to represent hyper and multi graphs. An pair of incidence matrices reprsent two graphs, one from nodes to edges and the other from edges to nodes, these are n by m and m by n. When you multiply them, you get a square adjacency matrix that "projects" the incidence into an adjacency. This can be used to collapse hypergraphs into simple graphs that use different semirings to combine the multiple edges.
For some pretty pictures of this kind of stuff, check out CoinBLAS (note I am not a crypto-bro, it was just a very handy extremely large multi-graph that I could easily download in chunks to play with):
https://github.com/Graphegon/CoinBLAS/
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Ask HN: What Are You Working On?
Python wrapper around The GraphBLAS API:
https://github.com/michelp/pygraphblas
For an upcoming paper we've open sourced using pygraphblas to analyse the bitcoin graph using the GAP benchmarks on a server with 1TB of RAM:
https://github.com/Graphegon/CoinBLAS
- Show HN: CoinBLAS – Bitcoin Analysis with the GraphBLAS
What are some alternatives?
spacedrive - Spacedrive is an open source cross-platform file explorer, powered by a virtual distributed filesystem written in Rust.
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mp4-inspector - A Web-based MP4 File Inspector. Powered by Rust, Vue and Web Assembly! :crab:
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