The question being asked in r/kubernetes this week is whether to feed observability data to coding agents. Traces, metrics, logs, the whole stream the system already emits — should the agent that writes and fixes your code get to read it? The answer is yes. The agent that can see

Internal platforms used to be the exotic option. Now they are the default. A December 2025 Platform Engineering survey of 518 organizations found that nearly 90% of enterprises run internal platforms, with a dedicated platform-product-manager role appearing in a quarter of them. The interesting part is not the adoption curve.

The companies with the most control over chip supply on the planet still rent across three cloud providers. That is the fact that should reset how a platform team thinks about AI infrastructure. If a frontier lab with custom silicon deals and over a million of its own accelerators cannot

The 1M-token context window is here. Opus 4.8, 4.7, and 4.6, plus Sonnet 4.6, now carry the full million-token context on the Claude API, Amazon Bedrock, and Vertex AI — no surcharge, generally available. A single request can hold up to 600 images or PDF pages. The

A LinkedIn post circulating this week made an observation worth sitting with. AI write-throughput in a serious engineering team can hit fifty pull requests a day. The team's merge-throughput is two. The author's own bug-finder product had hit the same wall on the customer side — initial

The cloud-runtime question for enterprise Claude is settled. AWS shops route through Amazon Bedrock. Google Cloud shops route through Vertex AI. Direct deployments now sit inside the customer's perimeter via self-hosted sandboxes and MCP tunnels that shipped this month. The question "where should Claude run" has

The May 15 piece on access-rents drew a line through every services industry. On one side: access-rents — work that consists of operating an interface the customer cannot operate themselves. On the other: integrated expertise — work that consists of telling the customer which question to ask. AI eats both, with opposite

Every working stack is the residue of failures the operator did not see coming. The Saturday piece showed the architecture as it stands now. This piece is the inverse — the five specific incidents that produced the current shape. Each one started as a quiet bug and ended as a permanent

Most "AI is expensive" discourse is vague. The pieces that quote real numbers usually quote enterprise tier list prices for tools the writer does not personally run. The pieces that talk about personal use rarely quote any numbers at all. This is the ledger for a working personal