Updates, guides, and operational knowledge from the team.
We forbade an AI from using its training data and made it resolve real infrastructure alerts using only the guidance our own dashboard produces. Three gap patterns surfaced. All three fixed in the same week.
We retrained our drive-failure predictor on 2 years of Backblaze data (222M drive-days) on the CPU of our L4 inference server. Gemma stayed resident in VRAM. 59 minutes, no new compute, 5.8% inference overhead. Plus the feature-importance surprise: SMART 197 beat SMART 187.
Three tools, one philosophy: Dashboard (SaaS dashboard), Bench (MCP servers for AI agents), and Crucible (open-source agent). Built by operators with a decade of bare metal experience across 67 global locations.
Three-way benchmark of Gemma 4 26B-A4B, Qwen3.6 35B-A3B no-think, and Qwen3.6 35B-A3B thinking on a production infrastructure health analysis prompt. Real wall-clock numbers, VRAM footprints, and the quality-latency tradeoff that matters for narration.
A practical guide to monitoring IPMI sensors, SEL logs, and BMC health on Dell, Supermicro, and HPE servers. Covers kipmi0 CPU issues, vendor quirks, and what to alert on.
How we deployed Gemma 4 26B on an NVIDIA L4 for AI health analysis of bare metal servers. Covers model selection, why vLLM failed, quantization choices, and prompting for structured infrastructure output.
Most monitoring tools stop at the OS. Below it sits an entire hardware layer: disk firmware predicting its own failure, fans at 0 RPM, ECC memory correcting silent errors. Here is what to monitor and why.
Cloud monitoring tools were built for ephemeral workloads. They track HTTP latency and container restarts. But when you run physical servers, the failure modes are fundamentally different: drives wear out, DIMM slots develop bit errors, fans fail silently, and RAID arrays degrade without anyone noticing.
