Detect the dominant palette, salient region and focal point of an image
High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs
Linux x64 GNU native module for @ruvector/attention
Plugin for attention
High-performance attention mechanisms with 7 mathematical theories: Optimal Transport, Mixed Curvature, Topology, Information Geometry, Information Bottleneck, PDE/Diffusion, Unified Diagnostics
Signifier components and providers for attention system.
Windows x64 MSVC native module for @ruvector/attention
macOS Apple Silicon ARM64 native module for @ruvector/attention
Displays a dialog with a custom content that requires attention or provides additional information.
Plugin for attention
WASM bindings for ruvector-graph-transformer: proof-gated graph attention in the browser
Linux ARM64 GNU native module for @ruvector/attention
macOS Intel x64 native module for @ruvector/attention
Harness Attention. Orchestrate Agents. Ship.
High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs
## Attention
Self-learning vector database for Node.js — hybrid search, Graph RAG, FlashAttention-3, HNSW, 50+ attention mechanisms
High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs
Harness Attention. Orchestrate Agents. Ship.
Simple, tree-shakable browser audio notifications (success, alert, warning, attention, error) with short, medium, and long sounds.
Unified WebAssembly bindings for 18+ attention mechanisms: Neural, DAG, Graph, and Mamba SSM
A badge to notify that there is something that requires attention of the user.
Display attention-grabbing messages in the terminal
Attention management
Attention mechanisms for ruvector - geometric, graph, and sparse attention
Unified WebAssembly bindings for 18+ attention mechanisms: Neural, DAG, Graph, and Mamba SSM
Attention mechanisms for ExoGenesis Omega - 39 attention types for brain-like selective processing
High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs
Subquadratic O(N log N) sparse attention kernel for Rust LLM inference on edge devices, with optional FastGRNN salience gating for near-linear O(N) scaling
Complete Cartan matrix attention mechanisms with proper Lie algebra structures
Transformer-as-rules: Self-attention and FFN layers as einsum expressions
MPS Flash Attention for candle - O(N) memory attention on Apple Silicon
High-performance PostgreSQL vector database extension v2 - pgvector drop-in replacement with 230+ SQL functions, SIMD acceleration, Flash Attention, GNN layers, hybrid search, multi-tenancy, self-healing, and self-learning capabilities
Vision Transformer models and building blocks for Rust using tch.
Node.js bindings for ruvector-attention
Attention Residuals (MoonshotAI/Kimi) implementation in Rust using burn
Redis-based server awareness for distributed applications
LEX agentic attention domain: focus, salience, cognitive control
Selective attention filter for brain-modeled agentic AI
Tomasello's joint attention framework for LegionIO — shared focus between agents, mutual awareness tracking, referential communication, and collaborative attention management with focus decay and working-memory constraints.
Models attention as a scarce resource for brain-modeled agentic AI; allocates a limited attention budget across competing demands using economic principles
Spotlight model of attention (Posner + Eriksen zoom lens) for brain-modeled agentic AI
Models the cognitive cost of switching between tasks including residual activation, warmup time, context restoration, and practice effects.
Graziano's Attention Schema Theory for brain-modeled agentic AI — the agent maintains a simplified internal model of its own attention process, enabling awareness attribution, social attention modeling, meta-attention monitoring, and natural-language attention reports.
Attention regulation engine for LegionIO — spotlight model, zoom control, resource allocation, and salience-driven capture
This gem allows you to watch the jobs which suddenly dissappeared from redis without being completed by redis worker
My brain is too dumb to get it right alone.
[Please consider migrating to opengl-bindings2 (https://rubygems.org/gems/opengl-bindings2)] Ruby bindings for OpenGL - 4.6, OpenGL ES - 3.2 and all extensions using Fiddle (For MRI >= 2.0.0). GLFW/GLUT/GLU bindings are also available.
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