SIMD-accelerated distances, dot products, matrix ops, geospatial & geometric kernels for 16 numeric types — from 6-bit floats to 64-bit complex — across x86, Arm, RISC-V, and WASM, with bindings for Python, Rust, C, C++, Swift, JS, and Go 📐
The explainable, local-first memory engine for AI agents. One ~9 MB binary fuses vector + graph + columnar under VelesQL; why() returns the evidence path behind every recall. No cloud, no glue code — runs on server, browser, mobile and desktop.
A high-performance C library for Longest Prefix Match (LPM) lookups, supporting both a multi-bit trie of 8-bit stride for IPv4 and a wide multi-level 16-bit stride for IPv6, featuring runtime dynamic SIMD dispatching (SSE2, SSE4.2, AVX, AVX2, AVX512F) for optimal performance and throughput on any CPU architecture.
Hardware-accelerated whole-slice SIMD vector primitives for Go (AVX-512, AVX2, NEON, SVE2, RVV) — zero cgo, zero dependencies, 100% portable Go fallback.
Accelerate SHA256 computations in pure Go using AVX512, SHA Extensions for x86 and ARM64 for ARM. On AVX512 it provides an up to 8x improvement (over 3 GB/s per core). SHA Extensions give a performance boost of close to 4x over native.
A few classes for extremely fast json parsing/serializing in modern C++. Possibly the fastest json parser in C++. Possibly the fastest json serializer in C++.
Accelerate aggregated MD5 hashing performance up to 8x for AVX512 and 4x for AVX2. Useful for server applications that need to compute many MD5 sums in parallel.