A program executes instructions. A system must retain state, coordinate components, and remain understandable when something fails. Systems Lab explores those decisions through models, experiments, and operational lessons.
The starting point is to follow a request from arrival to its result: which components take part, what commitments they make, and how to verify that the work is complete. The lab’s four axes examine that journey from different angles.
Three paths to follow an idea
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System Design — architecture decisions about data, capacity, communication, and recovery. It starts from requirements and invariants and compares what each mechanism protects.
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Homelab — Kubernetes and infrastructure operations: deployment, networking, storage, security, and system signals. It takes those decisions into configuration, verification, and real failures.
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Platform Engineering — reproducible infrastructure, verifiable delivery, bounded permissions, and costs with explicit assumptions. It connects system contracts with the work of teams building and operating their platform.
The paths complement each other. A model explains what must remain true; operating it requires knowing where to observe that behavior and how to recover it. The four axes below connect these paths.
State and persistence
State records what the system knows and what has already happened. Its location and lifetime determine what can be recovered after an interruption and which components can modify it.
This axis covers memory and storage, transactions, consistency, isolation, and data evolution. The central question is what must remain true when a process stops or several components act at the same time.
Communication and coordination
Components need to exchange information and agree on who does each piece of work. Crossing a process or network boundary introduces latency, uncertainty, and new ways to fail.
This includes API contracts, synchronous calls, queues, events, message ordering, and delivery guarantees. The goal is to understand what each participant knows and what it can assume about the others.
Failures and recovery
One component can stop while others continue working. A request may have produced an effect even if its response never arrives. Recovery requires distinguishing pending work from what has already happened.
This axis brings together timeouts, retries, idempotency, fault isolation, degradation, and restoration. Each mechanism must explain which failure it addresses, what it costs, and how recovery is verified.
Observability
Operating a system requires reconstructing its behavior from signals. Logs record events, metrics show trends, and traces connect a request’s journey across components.
This includes instrumentation, latency, errors, saturation, and service objectives. The question is how to detect a problem, locate it, and verify its impact on the outcome the user expects. In Homelab, the notes on logs and metrics and health help examine platform signals alongside that outcome.
Starting points
- Start Naive, Then Make It Better — start with a simple process and learn what is worth automating.
- Multi-Tenant AI Agents on Kubernetes — decisions about state, isolation, and architecture costs.
- Would You Use Tailscale in Production? — connectivity, remote access, and homelab continuity.
- Agentic Workflows Presuppose Determinism — a discussion of expected agent behavior and the probabilistic nature of their models.
Working in the lab
Each note or experiment starts with a concrete problem, makes the system boundaries explicit, and compares decisions. It then introduces a failure or adverse condition and shows how to observe the outcome. Learning is complete when what happened can be explained and verified again.