Articles
Worth reading
A short take on pieces worth your time — architecture, AI, system design, and engineering careers — each one linked back to its original source.
6 articles
How Netflix Simplified Batch Compute with Kueue
A concrete look at how Netflix simplified its batch compute stack by adopting Kueue, the Kubernetes-native job queueing system, instead of maintaining a bespoke scheduler layer on top of it. It's a good case study in a pattern I see a lot in mature platform teams: replacing homegrown infrastructure with an increasingly capable open-source primitive once the ecosystem catches up to what you built out of necessity years earlier. Useful if you're running batch or ML workloads on Kubernetes and wondering whether you still need your own queueing layer.
How we prepare Shopify for BFCM
Kyle Petroski and Matthew Frail's inside look at the nine months of capacity planning, chaos engineering, and load testing at 150% of projected peak that goes into Shopify surviving Black Friday/Cyber Monday, a weekend that hit 489 million requests per minute across 90 petabytes of data in 2025. What stands out is the organizational structure as much as the technical one: capacity planning, infrastructure roadmap, and readiness testing run as three parallel tracks for most of a year. A good reminder that scaling for a known traffic spike is a program, not a sprint right before the event.
Building Meta's GenAI Infrastructure
Meta's infrastructure team walks through the two versions of their 24,576-GPU training cluster built to train Llama and support GenAI research at a scale most of us will never operate at directly, but the design tradeoffs around networking, storage, and power show up at a tenth the size too. What I appreciated most is the honesty that GenAI workloads broke assumptions their existing infrastructure had baked in for years, forcing real architectural changes rather than just adding more machines. Good grounding for understanding what "AI infrastructure" actually means below the model layer.
Caching challenges and strategies
Matt Brinkley and Jas Chhabra lay out the uncomfortable truth about caches: they don't just add latency and cost benefits, they change your system's failure modes, because a cold or unavailable cache can dump surge traffic straight onto a downstream service that was never sized for it. The taxonomy here, local vs. external, inline vs. side, and how each fails differently, is one I still reach for when reviewing a caching design. Worth reading before you add a cache "for performance" without thinking through what happens when it goes away.
Timeouts, retries, and backoff with jitter
Marc Brooker's entry in the AWS Builders' Library is the clearest explanation I've read of why naive retry logic makes outages worse, not better: synchronized retries from thousands of clients can turn a brief blip into a self-inflicted thundering herd. The fix, jittered exponential backoff paired with timeout budgets tied to real latency percentiles, is simple to state and easy to get wrong in practice. I reference this every time I review a service's failure-handling code.