I’m working on a purpose-built memory platform for autonomous AI agents.

Right now, agent memory is stuck between two hohum options: RAG (which loses relational topology) and Graph Databases (which require massive pointer chasing and degrade under heavy recursive reasoning).

I'm building an alternative using Vector Symbolic Architecture (Hyperdimensional Computing). By mathematically binding facts, sequences, and trees into fixed-size high-dimensional vectors (D=16,384), we can compress complex graph traversals into O(1) constant-time SIMD operations…and do some quasi brain-like stuff cheaply, that is, without GPUs and LLMs.

The design is maturing nicely and strictly bifurcated to respect mechanical sympathy:

• The Data Plane (Zig): Pure bare-metal math. 2GB memory-mapped NVMe tiles via io_uring. Facts are superposed into lock-free 8-bit accumulators strictly aligned to 64-byte cache lines. Queries are executed via AVX-512 popcount instructions to calculate Hamming distances at line-rate. Zero garbage collection.

• The Control Plane (Gleam): Handles concurrency, routing, and a Linda-style Tuplespace for external comms. It manages the agent "clean-up" loops and auto-chunking without ever blocking the data plane.

• The Bridge: A strict C-ABI / NIF boundary passing pointers from the BEAM schedulers directly into the Zig muscle.

There is no VC fluff here, and I'm not making wild claims about AGI. I have most of spec, memory layout invariants, and the architecture designed. Starting to code and making good progress.

I’m looking for someone who loves low-level systems (Zig/Rust/C) or highly concurrent runtimes (Erlang) to help me build the platform. This is my second AI platform; the first one is healthy and growing.

If you are interested in bare-metal systems engineering to fix the LLM context bottleneck, I'd love to talk: email me at acowed@pm.me.

Cheers, Kendall