Engineer Builds Vector Symbolic Memory Platform
Kendall is developing a purpose-built memory platform for autonomous AI agents using Vector Symbolic Architecture with 16,384-dimensional vectors to bind facts, sequences, and trees into fixed-size representations. The bifurcated design pairs a Zig data plane—2GB memory-mapped NVMe tiles, lock-free 8-bit accumulators, io_uring, and AVX-512 popcount Hamming-distance queries—with a Gleam control plane and a C-ABI/BEAM bridge; the project seeks low-level systems engineers.
Key Points
- 1Implements vector symbolic architecture binding facts into 16,384-D vectors for fixed-size, compressed graph representations
- 2Enables constant-time O(1) SIMD Hamming-distance queries via AVX-512, reducing pointer-chasing and recursive-traversal costs
- 3Offers low-latency, GPU-free agent memory; requires expertise in Zig/Rust/C, AVX, and concurrent BEAM/Gleam runtimes
Scoring Rationale
Technically novel and actionable with specific implementation details, limited by single-source, early-stage announcement and validation.
Sources
Public references used for this report.
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