Safety Foundations in Distributed Systems
This day launches the distsys-safety phase by establishing core safety properties that later weeks will apply to AI workloads. Understanding these primitives early prevents downstream failures when scaling training or inference across unreliable networks. The learner will connect abstract guarantees to concrete engineering decisions in their own stack.
Resources
- 25 min
- 20 min
Deliverable
Journal entry listing three safety properties and one concrete failure scenario from the learner's current codebase
Quiz · 3 questions
1. Which property guarantees that if a value is chosen, no other value will ever be chosen?
2. Name one common misconception when applying consensus algorithms to ML training clusters.
3. Describe how a safety violation in a rate-limiter could cascade in a distributed brief-generation service.