The core problem we set out to solve was the reliability gap in local data logging. In our experience with autonomous systems, the most critical sensor logs often get lost when a robot loses power because that data is still sitting in the OS page cache waiting to be flushed. This often forces teams to rely on expensive real time cloud streaming just to ensure data safety.
We built an engine that solves this by bypassing the Linux kernel cache entirely and streaming data directly to NVMe. This approach unlocks several key capabilities for edge devices.
First it guarantees that every frame is physically persisted to disk the instant it is captured with under 1 microsecond write latency. Second it significantly increases your available RAM capacity by preventing the kernel from filling memory with write buffers allowing you to run larger models locally. Finally it allows you to reduce cloud dependency and bandwidth costs because you can actually trust your local storage to survive a hard power cut.
We put together a short video to demonstrate this in real time showing a side by side comparison of a standard logger versus our engine during a hard power cut.
This is likely most useful for engineers working on autonomous vehicles, industrial robotics, or any edge application where losing the last few seconds of data before a crash makes debugging impossible.
If you are running into these bottlenecks or just want to stress test the claims drop us a message and we will send you the binary to play around with.