The headline AI pattern of 2025 is agentic-everything. The pattern that's quietly doing more useful work for actual production systems is distillation, taking a big expensive model's behavior and getting most of it from a much smaller cheaper one.
Tech debt was the unfunded liability of an engineering team for two decades. Context drift, the slow erosion of what the AI tools think you're doing, is the equivalent for AI-augmented teams. Same shape, different substrate.
Anthropic ships Haiku 4.5, then drops Skills 24 hours later, two releases pulling on the distributed-AI argument. OpenAI-Broadcom go 10 GW custom silicon. NotebookLM gets real chat. The AI-layoff drumbeat keeps building. My take.
KServe, vLLM, Triton, BentoML, four different answers to the same question. Each layer trades operational complexity for serving features. Worth being concrete about which layer is right for which workload, tested on a single GTX 1080 Ti.
DevDay week. OpenAI ships GPT-5 Pro in the API, Sora 2 in the API, AgentKit, the Apps SDK, ChatKit, Codex GA, and signs a six-gigawatt AMD compute deal, all on Monday. Then the Sora app starts producing the deepfakes everyone predicted. My read on a heavy week.
Auditors are starting to ask about AI use in SOC 2 cycles. The shops running local LLMs have a different story to tell than the shops running cloud, and the auditors mostly haven't internalized the difference. Worth being explicit about what evidence actually answers the questions.
The atomic unit of an AI workload isn't the model call, isn't the request, isn't the user. It's the conversation. The architecture decisions that follow from that, caching, billing, governance, ops, all get cleaner when you start there.
California signs SB 53, the first US frontier-AI law. Anthropic ships Sonnet 4.5 with an Agent SDK. OpenAI ships Sora 2 with a biometric-scan social app. The AI-layoff narrative consolidates. My take on a heavy news week.
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.
