paul@stagner:~$ cat ~/blog/*.md
Blog
Notes from the orchestration trenches — agents, automation, architecture, and the craft of keeping teams on signal.
Orchestrating Coding Agents at Scale: Lessons from ClusterClaw
A lone agent solving a ticket is a demo. A coordinated fleet that ships, verifies, and operates real systems is a product. Here is how ClusterClaw turns Claude Code, Codex, OpenClaw and Hermes into dependable infrastructure.
Deterministic Agentic Workflows: Why Daeloom Bets on DAGs over Autonomy
Most production LLM workloads are not open-ended quests — they are pipelines: classify, enrich, route, generate, validate. Daeloom treats orchestration as a deterministic, observable, cost-bounded system instead of a swarm of self-directed agents.
Keeping Developers on Signal, Not Infrastructure Noise
Most developer pain is not the problem itself — it is the noise around the problem. Drawing on R&D work at Vortexa, here is how I design AI architecture to filter it out.
CI/CD Automation That Actually Earns Trust
Automation only helps if people trust it. A field guide — grounded in Daeloom Cloud and ClusterClaw — to building pipelines and agent workflows engineers are happy to hand the keys to.
// from Medium
Longer-form essays published under the byline Paul Stagner.
The Curve of AI and the Future of Us
How humanity changes when intelligence becomes shared — capability, adoption, and integration curves, and why those who can orchestrate AI systems gain compounding advantage.
How to Know If You Have a Multidimensional Mind — and How to Train It
Some people think in straight lines; others think in dimensions, systems folding into systems. A field guide to recognizing and training multidimensional thinking.
Introducing MCIK: Micro-Cause Influence Kernels for Real-World Systems
Modern systems — from financial markets and rendering engines to control planes and medical models — rarely behave linearly. MCIK is a lens for the small causes that move them.
The Butterfly Effect of Code: How Small Decisions Shape Everything
Why the smallest engineering decisions compound — and how to make the ones that leave systems better than you found them.