I write about data platforms, small infrastructure, software boundaries, and using agents in real work. Most entries begin with a system, decision, or failure and then ask what should survive the next change.
Projects show what exists and when I last checked it. Notes record the rules, mistakes, and design choices I expect to need again.
Start here
Four places to begin, across agent work, software assurance, operations, and data systems.
A durable agent application keeps permissions, records, checks, and recovery outside the model, so changing models does not also replace the system's memory or rules.
A reading map for building AI systems that can show what they were asked to do, what they did, why a result should be trusted, and how failures are contained. It starts with established assurance practice and ends with a dated watch list of open work.
Project history · Guiding claim · Cluster operations
Migrating a Talos cluster from a legacy subnet to its own VLAN while it kept serving. The design was mostly an ordering problem, plus one bootstrap loop: the network controller lives inside the network it manages.
A working family of sprint, queue, knowledge, audit, and cockpit tools now composes into one served layer, without giving up explicit state ownership or machine-local execution.
Homelab AnalyticsThe household data and decision platform that owns long-lived semantics, scenarios, policies, and approvals. keeps household reporting, planning, simulation, policy, trust, and agent-facing retrieval in one semantic model. Home Assistant is the device-facing partner, not the system of record for household reasoning.
AppserviceThe private GitOps repository that holds desired state, recovery rules, and operational evidence for the cluster. is the operations repository for a Talos-based Kubernetes cluster. Reconciliation, encrypted secrets, recovery gates, upgrades, and incident evidence live with the desired state instead of in operator memory.
Jev, Claude Haiku 4.5 and keyword rules triaged 100 agent work items from ticket text, and none was reliably better. The ranking moved with the judge, the question's wording and the price of each error, so the next tests vary the router around the model.
A hobby game AI is a cheap laboratory for deciding which decisions should stay with an LLM, which should become learned, and which should become ordinary code, settled by replayable contests rather than diagrams.
A missing report, an empty field, an unverified value, and an inapplicable reading need different actions even if a layered system displays them alike.
A missed date can disprove a career forecast without settling whether the underlying work is still worth pursuing. Ownership, recognition, and market response provide the next decision tests.
A passing retry experiment supports a decision under its tested conditions. When the implementation or replay window changes, the old result remains true but its use in the new decision needs reassessment.