I Got Tired of Training Jobs Crashing at Hour 6 — So I Built Veriflow You know the feeling. You kick off a training job before bed. 8 hours of compute. You wake up, grab your coffee, open the terminal — and see it crashed at hour 6. No checkpoint. No retry. No clue why. Restart from zero. That pain is what led me to build Veriflow — a checkpoint-aware, fault-tolerant job orchestrator for AI training workloads on Kubernetes. The Problem With Existing Tools Most job runners treat AI training like a simple script: "Run it. If it fails, restart it." But training jobs are not simple scripts. They are: Long-running — hours or days, not seconds Stateful — they produce checkpoints as they run Expensive — GPU time costs real money Distributed — they touch storage, databases, and compute simultaneously Restarting from zero every time a job fails is not just annoying — it is wasteful and often unacceptable in production.…