Exploring the echoes reverberating through time left by the technology of yesterday as we embrace the technology of tomorrow.
Most home AI setups die after the novelty wears off. The ones that survive into year two share a small set of operational properties, boring, durable, owner-friendly. Worth being explicit about what makes a stack worth keeping rather than just worth building.
Retrieval-augmented generation works well when the corpus fits on one machine. The honest version of what to do when the corpus outgrows that, without rebuilding the whole stack on cloud, is more interesting than either the all-local or all-cloud framings suggest.
Qdrant vs Weaviate vs Milvus on K8s. The foundation question for retrieval. StatefulSets, persistent volumes, replication, the operational reality. RAG indexing patterns at homelab scale on engine-01, and the decisions that change shape at fleet scale.
MCP solved the agent-to-tool plumbing. The next layer up, routing across many MCP servers, scoping access per agent, observing what's happening, is where MetaMCP and a small cluster of similar tools have started showing up. Worth being plain about why the layer exists.
Two years past the encoding-a-person framing. The thesis held in the parts I expected and bent in the parts I didn't. Worth being honest about what survived contact with the actual technology and what was just well-aged speculation.
Helm values.yaml is where Decisions as Code lives in Kubernetes. Centralized business decisions, schema-validated, composed via library charts, projected into every workload. The approach is the contribution. The tool changed.
A month past the OPA-renaissance piece, with more shops actually shipping agent-policy stacks. Worth getting concrete about what the production deployments look like, the patterns, the policy bundle structure, and the interfaces that survive contact with reality.
The market for PII-detection-and-redaction tools is growing fast. The right answer for most teams isn't a tool, it's a pattern that lives at the prompt-assembly layer and uses small, cheap models to do the work.
Most AI governance frameworks I've seen are written by people who don't ship code, for people who don't ship code, and applied to people who do. The result is friction, attrition, and shadow AI. The frameworks that actually work share a few specific properties.
The Apple Neural Engine doesn't get featured the way the GPU and unified memory do. It's the part of the chip that does the most for the on-device personal-AI story Apple is supposedly betting on. Worth being plain about why it matters and why nobody talks about it.
Spent a weekend doing actual training work on the Mac Studio rather than the usual inference-only experimentation. MLX is meaningfully more capable for training than it was six months ago. Worth being plain about what works, what doesn't, and what it means for the Apple Silicon training story.
Three months past WWDC 2025. The Apple Intelligence pitch was real on the demo stage. The shipped version is more constrained and the timeline gap is widening rather than closing. Worth being concrete about what landed and what didn't.
