While building multi-agent systems, I kept running into "ghost bugs"moments where an AI would retrieve the correct context on my local machine, but hallucinate when deployed to a different environment. I realized this wasn't an AI alignment problem; it was a physics problem. Almost all modern vector databases (Pinecone, Qdrant, Milvus) use standard f32 floating-point math. Because floating-point operations are non-associative, the math physically drifts depending on the underlying CPU architecture (e.g., an x86 cloud server vs. an ARM Raspberry Pi). This drift causes rank-swapping in vector search. Your agent literally makes different decisions simply because the hardware changed. If we are going to deploy autonomous agents and robotics swarms, "approximate" memory isn't safe enough. So, I built Valori. Zero Floats: We stripped out all floating-point math. Q16.16 Fixed-Point: All vector geometry runs through a custom deterministic integer pipeline. no_std Rust Kernel: It runs anywhere, from bare-metal edge devices to WebAssembly sandboxes, guaranteeing the exact same bit-stream output. Think of it as a mathematically provable flight recorder for AI. We recently published the underlying research on arXiv, and the Python prototype (pip install valori) just crossed 2,600 downloads. I’d love for you to try it out, read the repo, or run the eval-quant tool against your current vector database to see how much "slippage" you are losing to quantization noise. I’ll be here all day answering questions about systems engineering, Rust, and the physics of AI memory. Let me know what you think!

Valori Systems

Physics-grade deterministic memory for AI agents.

Physics-grade deterministic memory for AI agents.