// Core Thesis
Exploring the intersection of traditional storage engines and accelerated AI compute layers.
Researching the low-level plumbing required to keep the world’s most expensive GPUs from sitting idle.
Vector 01 / Kernel Elements
Transactional KV for GPU Tiers
Vector 02 / Memory Boundaries
Snapshotable Context Trees
Vector 03 / Data Pipelines
Disaggregated Flash Streaming
[ About BufferNaut ]
BufferNaut Labs is an independent engineering notebook and research space dedicated to tracking the structural evolution of hardware-centric storage architectures. As machine learning workloads demand unprecedented memory bandwidth and continuous context streaming, traditional storage primitives must be re-imagined from the metal up.
This space documents deep-dives into exploratory ideas that bridge reliable, deterministic storage with highly parallelized accelerated compute layers.