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1,195 stories
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Story | January 27, 2026
There are compromises that don't feel very important when we make them. Take the job for now. Put the project aside until things settle down. Stay where you are because moving would be expensive. Say yes because somebody is depending on you. Do the practical thing and come back to the other thing later. Often this is good advice. Adults have obligations,
Story | January 27, 2026
We have reasonably good ways of recognizing things that already work. A person develops a useful skill and someone hires them to use it. A business sells something people already know they want. A school teaches subjects whose value has been established by generations of experience. Institutions develop roles, procedures and expectations around activities t
Story | January 26, 2026
We like breakthroughs to have names attached to them. It makes history easier to remember. Instead of carrying a network of experiments, arguments, failed attempts, technicians, institutions and incremental improvements, we attach the change to a person. Someone invented this. Someone discovered that. Someone saw what everybody else missed. Sometimes that'
Story | January 26, 2026
The first essay on Tone Process Monism described a way of looking at persistence through change. I used terms such as tone, compression and return to ask what gets carried forward when a person, institution or other process changes without becoming entirely unrecognizable. That left an important question mostly untouched. If every representation carries on
Story | January 25, 2026
Tone Process Monism says reality is a continuously learning pattern, not a finished thing—and coherence comes from how patterns are lived, compressed, and returned to over time.
Story | January 25, 2026
Once a Kubernetes cluster can lose a worker node without turning the event into an emergency, it's tempting to call the cluster redundant. Some of it may be. That's a useful distinction to make before adding anything else. Kubernetes can reschedule a workload after a node disappears, but that tells us only that another node is capable of running the workl
Story | January 25, 2026
A single-node Kubernetes cluster can teach you quite a lot. You can learn how workloads are deployed, how services find each other, how ingress works, where configuration lives and what happens during an ordinary restart. You can build something useful enough that Kubernetes stops feeling experimental. Then you add another machine and discover how much of
Story | January 25, 2026
The easiest way to make Kubernetes difficult is to begin with the architecture you eventually want. Multiple nodes, redundant storage, automated failover, GitOps, remote disaster recovery and hybrid infrastructure are all reasonable things to think about eventually. They also introduce enough interacting parts that a first installation can fail in several p
Story | January 25, 2026
Once the local cluster can survive the failures we've actually designed it to survive, the next question isn't how to move it into the cloud. It's what the cloud could do for us that another machine in the building can't. That's a narrower question, and I think it produces a better architecture. Ten servers in one building can provide considerable redunda
Story | January 25, 2026
Occam's Razor is usually introduced as a preference for simpler explanations. Given two explanations that account for the same observations, prefer the simpler one. That's useful as a first approximation, although it can easily turn into a preference for whichever story contains fewer words, fewer parts or less apparent complexity. Reality has no obligati
Story | January 25, 2026
Alignment emerges from continuous learning under consequence, not from satisfying fixed specifications, and safety mechanisms that suppress learning signal risk long-term brittleness.
Story | January 25, 2026
This is the third essay in a series about AI alignment. The first two essays looked at guardrails and the cost of learning. I was working toward a distinction that still seems important to me, although I'd frame it somewhat differently now. We can test an AI system before deployment. We can establish constraints, evaluate known failure modes, document expe
