Why it matters
EdgeMind explores what local AI infrastructure looks like when privacy, memory and connectivity are first-class constraints rather than deployment details.
Architecture
SYSTEM / EVIDENCE LENS
RuntimeC++17 local inference boundary
Model layerGGUF models through llama.cpp
TargetLow-memory ARM64 / Android / Termux environments
PrinciplePrivate, local-first execution
Current evidence
Repository scope centers on local AI runtime work rather than hosted inference.
ARM64, low-memory and Termux-oriented operation are treated as design targets.
C++17, llama.cpp and GGUF form the documented implementation direction.
Experimental
Broader runtime packaging, model management and performance tuning are areas of ongoing exploration.
Known limitations
Device-specific performance varies with model size, quantization, memory and CPU capabilities. No universal benchmark is claimed here.
Roadmap
Expand model/runtime workflows, improve edge deployment ergonomics and publish reproducible measurements as they become available.
Last updated: 30 September 2026. This page separates observable implementation from research and roadmap work. No unsupported performance, security, adoption or production-readiness claims are made.