Knowledge Center
Resources for AI Infrastructure, Product Systems, and Operators.
Guides for understanding supercomputing systems, AI infrastructure, accelerator diversity, virtualization, on-prem AI, Nix runtimes, hardware lifecycle, observability, diligence, and AI readiness in real company environments.
AI infrastructureSupercomputingVirtualizationOn-prem AIEvidenceDeterministic systems
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Knowledge Center
Modeled as a practical resource center: start with the layer you care about, then move into public guides or protected implementation context.
Layer 1
Systems Foundations
Start with the technical layers that shape AI infrastructure, supercomputing behavior, virtualization and compute substrate lifecycle, Kubernetes operations, and on-prem deployment constraints.
AI InfrastructureAI Infrastructure Basics for GPU Clusters and Model-Serving SystemsA practical map of the layers that shape AI behavior in production: workload shape, GPUs, topology, schedulers, storage, networks, cooling, serving patterns, batching, and validation evidence.Supercomputing SystemsSupercomputing Systems, Fabrics, Storage, and Acceptance TestingHow to reason about high-performance systems where topology, InfiniBand or RoCE fabrics, RDMA, storage, queue policy, thermals, acceptance tests, and workload behavior interact.Hardware AutomationIn-Band Hardware Lifecycle Automation for Real InfrastructureA practical operating model for taking ownership of cloud, NeoCloud, or hosted bare-metal Linux systems when you have root on the disk but no Redfish, iDRAC, iLO, or BMC access.Hardware AutomationHardware Report: Open-Source Inventory Evidence for Real InfrastructureHow the open-source hardware_report Rust crate turns Linux and macOS host facts into structured TOML/JSON evidence for observability, cluster acceptance testing, CMDB population, and bare-metal lifecycle automation.Systems FoundationsVirtualization, Bare Metal, and VM Orchestration as Product InfrastructureHow QEMU/KVM, libvirt, OVS, KubeVirt, Firecracker-style microVMs, Linux, bare metal lifecycle, VM orchestration, metering, and billing become the compute substrate beneath larger platform ecosystems.Kubernetes & GitOpsKubernetes, GitOps, and Slurm on KubernetesHow to use Kubernetes, Argo CD, and Slurm-on-Kubernetes patterns for rollout safety, drift control, GPU workload scheduling, and platform evidence without hiding failure domains.On-Prem AIOn-Prem AI Deployment Constraints and Operating ModelWhy regulated, defense, industrial, healthcare, and financial teams may keep AI close to controlled data instead of trusting a third party to host AI and ML workflows.
Layer 2
Product and Readiness
Connect AI product decisions to existing customer systems, accelerator ecosystems, company workflows, data authority, and adoption constraints.
AI ProductsBuilding AI Products That Stay Close to Infrastructure and CustomersA practical operating model for AI products that must connect model capability, customer workflow, industry pressure, data authority, infrastructure, and measurable adoption.AI ProductsAccelerator Diversity and the CUDA Moat BreakdownHow product teams can avoid locking themselves into one accelerator ecosystem as AMD, NVIDIA, Intel, Google, and open runtimes compete for AI workloads.AI ReadinessTurning Pre-AI Companies into AI-Ready CompaniesHow to create useful AI workflows by mapping ownership, source authority, permissions, evaluation, and operator review before rebuilding the company around AI.
Layer 3
Infrastructure Evidence
Use hardware, datacenter, observability, and diligence signals to decide whether systems will work under real load.
Hardware InfrastructureHardware, Infrastructure, and Datacenter Systems for AI WorkloadsHow physical infrastructure, GPU platforms, cooling, firmware, PCIe, fabrics, storage, Kubernetes lifecycle, and acceptance gates shape AI workload reliability.Hardware AutomationHardware Report: Open-Source Inventory Evidence for Real InfrastructureHow the open-source hardware_report Rust crate turns Linux and macOS host facts into structured TOML/JSON evidence for observability, cluster acceptance testing, CMDB population, and bare-metal lifecycle automation.ObservabilityAI System Observability and Incident ReviewHow teams can inspect model behavior, retrieval quality, tool use, infrastructure state, latency, cost, operator actions, and customer-visible failure modes after launch.DiligenceTechnical Diligence for AI and Infrastructure CompaniesA framework for reviewing architecture, infrastructure economics, AI claims, team signals, implementation risk, and follow-up questions.
Layer 4
Implementation Practice
Apply systems programming, deterministic runtimes, and automation patterns that make infrastructure work easier to build, inspect, and repeat.
RustRust Systems Automation for Infrastructure WorkWhere Rust fits for host agents, validation tools, control-plane glue, hardware interfaces, and reliability-sensitive automation.Hardware AutomationHardware Report: Open-Source Inventory Evidence for Real InfrastructureHow the open-source hardware_report Rust crate turns Linux and macOS host facts into structured TOML/JSON evidence for observability, cluster acceptance testing, CMDB population, and bare-metal lifecycle automation.Hermetic BuildsHermetic Builds as a Reproducible Runtime LayerHow hermetic build practices make development environments, host configuration, validation checks, and infrastructure automation reproducible across machines and teams.Hardware AutomationIn-Band Hardware Lifecycle Automation for Real InfrastructureA practical operating model for taking ownership of cloud, NeoCloud, or hosted bare-metal Linux systems when you have root on the disk but no Redfish, iDRAC, iLO, or BMC access.
Private Deep Dives
Non-Public Material for Implementation Context.
Private resources hold source-code context, implementation notes, architecture diagrams, diligence templates, and operator runbooks for approved visitors.