WASM bindings for ruvector-verified: proof-carrying vector operations in the browser
WASM bindings for ruvector-graph-transformer: proof-gated graph attention in the browser
Proof-gated graph transformer with 8 verified modules — physics, biological, manifold, temporal, economic graph intelligence via NAPI-RS
High-performance vector database with HNSW indexing - 50k+ inserts/sec, built in Rust for AI/ML similarity search and semantic search applications
Self-learning vector database for Node.js — hybrid search, Graph RAG, FlashAttention-3, HNSW, 50+ attention mechanisms
Self-learning LLM runtime — TurboQuant KV-cache (6-8x compression), SONA adaptive learning, FlashAttention, speculative decoding, GGUF inference
Graph Neural Network capabilities for Ruvector - Node.js bindings
Portable WASM embedding generation with SIMD and parallel workers - run text embeddings in browsers, Cloudflare Workers, Deno, and Node.js
RuVector Format Node.js native bindings
Complete WASM toolkit for edge AI: vector search, graph DB, neural networks, DAG workflows, SQL/SPARQL/Cypher, and ONNX inference - all running in browser
RVF self-learning temporal solver — Thompson Sampling, PolicyKernel, ReasoningBank
High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs
DiskANN/Vamana — SSD-friendly billion-scale approximate nearest neighbor search with product quantization
RuVector Format — unified TypeScript SDK for vector intelligence
RuVector Format Node.js native bindings
Native Node.js bindings for RuVector Graph Database with hypergraph support, Cypher queries, and persistence - 10x faster than WASM
RuVector Format WASM microkernel for browser and edge vector operations
WASM bindings for RuvLLM - browser-compatible LLM inference runtime with WebGPU acceleration
RuVector Format Node.js native bindings
Self-Optimizing Neural Architecture (SONA) - Runtime-adaptive learning with LoRA, EWC++, and ReasoningBank for LLM routers and AI systems. Sub-millisecond learning overhead, WASM and Node.js support.
V3 Embedding Service - OpenAI, Transformers.js, Agentic-Flow (ONNX), Mock providers with hyperbolic embeddings, normalization, and chunking
RaBitQ 1-bit quantized vector index in WebAssembly — 32× embedding compression with high-recall rerank, for browsers, Cloudflare Workers, Deno, and Bun
Neural router for AI agent orchestration - FastGRNN-based intelligent routing with circuit breaker, uncertainty estimation, and hot-reload
Ultra-fast MicroLoRA adaptation for WASM - rank-2 LoRA with <100us latency for per-operator learning
Formal verification layer for RuVector: proof-carrying vector operations with sub-microsecond overhead using lean-agentic dependent types
WASM bindings for ruvector-verified: proof-carrying vector operations in the browser
Proof engine with 3-tier routing for the RuVix Cognition Kernel (ADR-087)
Unified graph transformer with proof-gated mutation substrate — 8 verified modules for physics, biological, manifold, temporal, and economic graph intelligence