From AI Factory to AgentMinder: bringing AI workloads into the private cloud.
VMware AI Factory, models as a service, token economics, and agent security explained through my AgentMinder lab, with the roles of Tanzu, vDefend, and Avi.
The journal
Firsthand homelab build logs: ESXi storage, nested vCenter, vSphere Kubernetes Service, Tailscale and the failures between the instructions.
The lab is where an architecture diagram meets the hardware I actually own. These field notes follow ESXi, vCenter and Kubernetes builds through licensing gates, storage limits and networking mistakes, with the error strings and unfinished steps left in.
Start with the state of the homelab for the physical layout and its limits. The vCenter and VKS posts then form a sequence: establish the management plane, work through prerequisites, and enable the service. Pi-hole is a smaller example of a useful workload running on that infrastructure.
VMware AI Factory, models as a service, token economics, and agent security explained through my AgentMinder lab, with the roles of Tanzu, vDefend, and Avi.
VMware Explore 2026 and VCF 9.1.1, explained: private AI, agent controls, platform operations, and what is available now.
My homelab is great at everything except the one thing AI needs: a real GPU. So I rent one by the hour from a neo-cloud, pull it into the tailnet, and tear it down in a finally block. The buy-versus-rent math, priced honestly.
A hands-on build log of enabling vSphere Kubernetes Service end to end — the wizard, the networking, the gate everyone forgets — and an honest case for why VKS fits if you already run vSphere.
The GPU is the part everyone budgets for and the part I never needed. An open model runs privately on CPUs you already own — so the real decision is the platform underneath it.
An agent-driven ESXi upgrade that succeeded, a restore step that failed on a precondition nobody checked, and a rollback that retried the exact action that had just failed. What I got wrong.
Two new engines run frontier Mixture-of-Experts models on consumer hardware by paging experts off SSD instead of holding them in memory. Why that works, why it is slow, why C is not the reason, what a GPU does and does not fix, and what it means for air-gapped estates and hardware refresh planning.
An agent with a kubeconfig is not an information system, it is an operator. Part 1 of a series on why least privilege, RBAC and audit logs all quietly assume the actor is deterministic, and how VCF and VKS primitives bound something that is not.
How I added DNS-level ad-blocking to my whole Tailscale network with Pi-hole, running it as a container with its own Tailscale identity instead of on a Raspberry Pi, why the tailnet is the right network for it, and the honest state of a build that is 44 minutes old.
A mid-2026 state-of-the-homelab: one Dell workstation running Proxmox, a Raspberry Pi, a bare-metal ESXi box, and twelve Tailscale devices running real websites and a fleet of agents, with no public IP and one deliberate single point of failure.
A build log for putting VKS (vSphere Kubernetes Service) on a resource-constrained nested vSphere lab: the license gate, a fresh vCenter 9.1, three prerequisite failures, and why Workload Management is still unenabled.
A build log for Supra, my personal AI system: skill dispatch via tool use, a proactive judge stuck in cold-start deadlock, and a self-improvement loop that mined a test fixture.
What it took to let an agent restart VMs and delete snapshots on real infrastructure: the tier model, the guardrails in code, and the bugs found along the way.
A build log for deploying VCSA 9.1 onto a mixed nested/bare-metal ESXi lab: hybrid ISO extraction, thin-provisioning prechecks, pyvmomi inventory, and licensing walls.
Bare-metal ESXi 9.1 on an EliteDesk 800 G6: the bundled SK hynix BC511 reports an all-zero NGUID, so ESXi never creates a device. What actually fixed it.