ruvnet

High-Dimensional Universe Simulation Kernel in Rust

This section provides a comprehensive Rust-style implementation of a simulation where "entities" (points) evolve on a dynamic submanifold embedded in a high-dimensional space. Each entity is represented by a high-dimensional state vector whose first 4 components are spacetime coordinates (time t and spatial coordinates x, y, z), and the remaining components are latent state variables (e.g. energy, mass, and other properties). We enforce that these state vectors lie on a specific manifold (such as a fixed-radius hypersphere or a Minkowski spacetime surface) via a projection step after each update. The update rule uses nearest neighbors with a Minkowski-like causal filter to ensure influences respect light-cone causality (no superluminal interaction
agemozphysics.com
). We also focus on performance by reusing allocations, aligning data to vector register boundaries, and supporting both single and double precision.

Data Structures and Parameters

We define a 

ruvnet

🧠 @ruvector/sona Integration Guide

Date: 2025-12-03 Status: ✅ READY FOR INTEGRATION Priority: HIGH Package: @ruvector/sona@0.1.1

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📊 Executive Summary

ruvnet

🎉 E2B Agent Testing & Optimization - COMPLETE SUMMARY

Date: 2025-12-03 Status: ✅ ALL TESTING COMPLETE Agents Tested: 66+ agents across 5 categories Total Tests: 150+ comprehensive test scenarios Success Rate: 95%+ across all categories

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ruvnet

Treat LFM2 as the reasoning head, ruvector as the world model and memory, and FastGRNN as the control circuit that decides how to use both.

• LFM2 as the language core (700M and 1.2B, optionally 2.6B). ([liquid.ai][1])
• ruvector as a vector plus graph memory with attention over neighborhoods.
• FastGRNN as the tiny router RNN that decides how to use LFM2 and ruvector per request. ([arXiv][2])

You can adapt the language and infra stack (Python, Rust, Node) without changing the logic.

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ruvnet

Latent Space Exploration: RuVector GNN Performance Breakthrough

TL;DR: We validated that RuVector with Graph Neural Networks achieves 8.2x faster vector search than industry baselines while using 18% less memory, with self-organizing capabilities that prevent 98% of performance degradation over time. This makes AgentDB v2 the first production-ready vector database with native AI learning.

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🎯 What We Discovered (In Plain English)

The Big Picture

ruvnet

GNN Attention Mechanisms for Vector Search: Comprehensive Research Analysis

Research Report Date: November 28, 2025 Researcher: AgentDB Research Team Focus: Graph Neural Network (GNN) attention mechanisms in vector search, query enhancement, and information retrieval

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Executive Summary

ruvnet

ruvector GNN Specification v0.1.0

Introduction

What is ruvector?

ruvector represents a fundamental shift in how we think about vector databases. Traditional systems treat the index as passive storage - you insert vectors, query them, get results. ruvector eliminates this separation entirely. The index itself becomes a neural network. Every query is a forward pass. Every insertion reshapes the learned topology. The database doesn’t just store embeddings - it reasons over them.

This convergence emerges from a simple observation: the HNSW algorithm, which powers most modern vector search, already constructs a navigable small-world graph. That graph structure is mathematically equivalent to sparse attention. By adding learnable edge weights and message-passing layers, we transform a static index into a living neural architecture that improves with use.

ruvnet

tensor-compress: Complete Technical Specification

Executive Summary

tensor-compress is a production-grade Rust library implementing quantum-inspired Tensor Train (TT) decomposition for neural network compression with distributed parameter serving. The library enables 45-60% model size reduction while maintaining <1% accuracy loss, with seamless integration into vector databases like ruvector for edge AI deployment scenarios.

Key Innovation: Combines classical tensor factorization with modern distributed systems architecture, enabling surgical knowledge editing and cost-efficient model serving.

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ruvnet

High-Performance Synthetic Data Generator: Complete SPARC Specification

Executive Summary

This comprehensive SPARC specification provides a production-ready blueprint for building a high-performance synthetic data generator in TypeScript, optimized for low latency as the primary metric. The system leverages both Gemini models and OpenRouter for intelligent routing, supporting 7+ data domains with streaming architecture.

Key Performance Targets:

• P99 latency: < 100ms per record
• Throughput: 4,000-10,000 records/minute
• Cost: $0.000022 per record (using Batch API + context caching)

ruvnet

Public Data Sources

Streaming Metadata

Watchmode API - Most accurate streaming availability for 200+ services across 50+ countries, includes web links, iOS/Android deeplinks, episodes, seasons, similar titles algorithm, and proprietary relevance scoring

Flix Patrol https://flixpatrol.com/about/api/

OMDb API - Long-standing favorite for title and episode data, returns plots, genres, release dates, ratings from IMDb/Rotten Tomatoes/Metascore, and poster URLs