Thread - Nostr Hypermedia

ᴛʜᴇ ᴅᴇᴀᴛʜ ᴏꜰ ᴍʟᴇᴋᴜ me@mleku.dev 1 week ago

ok, wavelets are not deterministic, they are more composable for natural forms than sine waves because of their decay, thus allowing more flexibility in overlaying them to compose the image. sine waves let you do circles, but wavelets make elliptical, asymmetric forms easier. however, it seems to me like you could derive a more deterministic pattern out of them. as a child, when i got my first computer, the way that you convert a circle into a pixel grid was fascinating. some are like squares, some are like octagons, and as you increase the size, the sequence of the number of pixels approximating the curve gradually progresses towards the limit between discrete and continuous. circles are a concept, and the pixels represent the discretized version. it's not the most expensive quantization, but it is a quantization. it's funny, i hadn't really thought about the meanings of the words, when i read "this model is quantized" and this makes its model smaller, that's what it's talking about - down-sampling from effectively infinite precision down to a size that you can actually generate algorithmically faster than you need to paint it on the screen. for this task, it's about how to take what is effectively discrete data, not sampled like a pixel bitmap, but like a mesh or network. so, the path to this effective algorithmic, algebraic decomposition maps to the process of quantization on a graph. i have been doing some work with the Brainstorm WoT stuff, and i gave them a database driver for neo4j because the main dev @david was familiar with it, but i understand graph data representations well enough that as i repeatedly have said to them, to zero response, is that i can make algorithms that optimize for their queries, if they just define the queries. i already implemented a vertex table that has the property of "adjacency free" which is a fancy word for "searching the table does not require the use of unique keys forming nodes", but rather, that the nodes are in a list and your iteration would use something like a bisection search to get to the start of one node, and then iterate that to find the second one, bisection to find the second (all done by the database index iterator code) and then you can traverse, effectively, in "one" iteration, which is actually just a random scan across a sorted linear index, where each node and its vertexes to other nodes is found in a section of the table. i want to impress upon everyone i've linked into this conversation, that building geometric and algorithmic traversals for data that has an easy visual representation is my super power. i can SEE what i'm doing, which many people who are better at linear stuff, parsing out the code, i'm better at writing it, than reading it. once you clarify the description, i can SEE the pattern as i scan around the visual representation my brain generates from the linear description. the simple fact that i can see how all of these things boil down to graphs, is something that i think is harder for everyone else to do than me. not in theory, but in practice.

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asyncmind asyncmind@asyncmind.xyz 1 week ago

I like where you're going with the circle-to-pixel analogy. The key thing I’m trying to separate is this: Quantization reduces precision. Canonicalization removes ambiguity. When you rasterize a circle, you’re approximating a continuous form in a discrete grid. That’s lossy, but predictable. What I’m interested in is slightly different: Given a graph (already discrete), how do we produce a representation that: Is deterministic Is idempotent Is invariant under isomorphic transformations Removes representational redundancy So instead of down-sampling infinite precision, we’re collapsing multiple equivalent discrete forms into one canonical embedding. Your “adjacency-free vertex table” intuition is actually very aligned with this. What you described — using sorted linear indexes and bisection traversal instead of explicit adjacency pointers — is essentially treating a graph as a geometric surface embedded in an ordered index space. That’s extremely interesting. Where I think your superpower fits perfectly is here: You see graph geometry. You see motifs and traversal surfaces. What I need in collaboration is: Identification of recurring structural motifs Definition of equivalence classes of graph shapes Proposal of canonical traversal orderings Detection of symmetry and invariants The difference between LLM-style quantization and what I’m building is: LLMs quantize parameters to approximate a surface. I want to deterministically decompose graph state into irreducible geometric motifs. No curve fitting. No parameter minimization. Just structural embedding. If we keep it abstract: Let’s take arbitrary graph shapes and: Define what makes two shapes “the same” Define canonical traversal orders Define invariants that must survive transformation Define minimal motif basis sets If you can see the geometry, that’s exactly the skill required. Your ability to visualize graph traversal as a surface in index space is actually very rare. Most people stay stuck in pointer-chasing adjacency lists. If you’re keen, we can start purely abstract — no domain constraints — and try to build a deterministic canonicalization pipeline for arbitrary graph motifs.

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david david@bitcoinpark.com 1 week ago

This is a very interesting discussion that I’m going to have to digest. I’m going to ponder whether what you’re doing with ECAI and what I’m doing with tapestry theory are complementary. Our Brainstorm product has two parts: the Grapevine, the prototype of which is what we’re currently building with you Mleku, and the knowledge graph (or sometimes I call it concept graph). The knowledge graph encodes not just mundane content, facts and information, but also ontologies, semantics, etc that we can in principle build individually but prefer to build collaboratively — which is what the grapevine is for. The knowledge graph supports queries like: give me the list of all widgets in my local graph database with characteristics A, B, and C. These are well defined, generalizable and extensible path queries that can be written using cypher. It requires the uuid for the node that represents the concept of widgets; if we don’t have that uuid, we can find it using the same cypher query. Mleku are those the kinds of queries you’re talking about when you say you can optimize for them?

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