Learn the System, Not Just the Tool.
Modern technology changes quickly, but the durable skill is understanding where data, hardware, software, people, and operations meet.
Field Guide for the Modern Technology Era
Practical Knowledge for People Building with Modern Systems.
A technical field guide for understanding AI infrastructure, hardware systems, developer tooling, operations, and product delivery through practical lessons that survive beyond any single tool cycle.
Technical Terrain
AI infrastructure
Supercomputing systems
GPU clusters
Hardware validation
RDMA fabrics
Storage systems
Kubernetes platforms
GitOps control loops
Virtualization
Nix deterministic runtimes
Rust tooling
AI knowledge layers
System observability
Acceptance testing
Regulated environments
Infrastructure ROI
Start Here
Knowledge Paths
Public notes for understanding how modern technology behaves in the field: what matters, what breaks, and what evidence is worth trusting.
AI InfrastructureUnderstand AI InfrastructureGPU classes, serving paths, schedulers, storage, networks, cooling, and validation evidence for AI systems.Read PathSupercomputingLearn Supercomputing System BehaviorTopology, RDMA, MPI and distributed training behavior, storage, benchmarks, queue policy, and acceptance testing.Read PathOn-Prem AIRun AI Near Company DataSecurity, source authority, model placement, update paths, evaluation, and operator review for enterprise environments.Read PathVirtualizationOwn the Compute SubstrateQEMU/KVM, bare metal, VM lifecycle, Linux, orchestration, metering, and billing beneath Kubernetes and AI platforms.Read PathNix SystemsBuild with Deterministic RuntimesUse Nix to make developer environments, build inputs, host configuration, and infrastructure automation repeatable.Read PathKubernetes & GitOpsOperate Platform Control LoopsUse Kubernetes and Argo CD as operating layers for rollout safety, drift control, workload health, and platform evidence.Read PathAccelerator DiversityDesign Beyond a Single GPU StackEvaluate how CUDA, ROCm, SYCL, XLA, model runtimes, kernels, and packaging choices shape market reach as accelerator ecosystems open up.Read Path
Why This Exists
Technology Should Make People More Capable.
Modern systems get complicated fast. This site explains practical lessons plainly: what to learn, what to watch, what to validate, and how to build judgment that survives the next tool cycle.
Grow from Evidence.
Good technical judgment comes from logs, benchmarks, incidents, customer feedback, and the discipline to revise your assumptions.
Keep the Path Practical.
The goal is not to chase novelty. It is to help people and teams use technology in ways they can explain, operate, and improve.
Ground Truth
Lessons from Systems That Had to Work.
Hardware and GPU Fleet Bring-Up
HardwareRustKubernetesRedfishH100
Built and led automation paths that moved bare metal GPU infrastructure into operational clusters with less manual sequencing and clearer acceptance criteria.
Hardware, Fabric, and Storage Validation
HardwareInfiniBandRoCENVMe-oFWeka
Validated high-bandwidth networking and storage behavior for AI and supercomputing systems, including topology, congestion, workload placement, and benchmark interpretation.
Enterprise Hardware and Regulated Environments
ServiceNowFedRAMPIL5Ceph
Worked through hardware, firmware, storage, networking, and reliability concerns in environments where operational discipline mattered more than novelty.
Startup Infrastructure Products
pre-seedSeries Acustomer-specificplatform
Helped fast-moving teams turn infrastructure ideas into customer-facing systems, internal platforms, and delivery paths that could survive real adoption.
Rust Systems Automation
RustRedfishfleet toolingvalidation
Build practical Rust tools for infrastructure discovery, host automation, validation workflows, and operator-facing systems where correctness and portability matter.
AI-Ready Knowledge Layers
retrievalpermissionsworkflowsreadiness
Help pre-AI companies turn documents, workflows, customer context, and operator judgment into governed knowledge layers that AI systems can use without losing ownership or control.
AI System Observability
evaluationtelemetryincidentscost
Design observability paths for AI products and infrastructure so teams can inspect model behavior, latency, cost, retrieval quality, operator actions, and failure modes after launch.