A request fans out across 50 services and comes back slow. Every dashboard is green. Tracing is how we find out where the time actually went.

Three tools, one decision. Here's how to choose, and why it's usually not the one you think.

At-least-once, at-most-once, exactly-once. What do these actually mean, and which one do you really have?

Seven distributions that show up everywhere in systems and ML, and how to know which one you are looking at.

Your LLM will try to do things it shouldn't. Here's how to stop it.

One database isn't enough. Here's how to split your data without the pain.

Apply consumer theory to understand why LLM pricing works the way it does

Why two-phase commit fails in production, and how sagas use compensating transactions to build reliable cross-service workflows without distributed locking.

LLMs break traditional observability. Log tokens, trace multi-step chains, attribute costs, monitor quality with evals, and handle privacy deliberately.

Event sourcing is cool, until it isn't. Store events, not state but only when it makes sense.

Load balancing algorithms are resource allocation mechanisms in disguise.

Three seconds will still feel like forever to your users. Make sure they never have to wait that long staring at a spinner.

Locking across machines is harder than you think

Your LLM costs more than you think - here's the full picture

Fast producers + slow consumers = cascading failure

Unserstanding deeply what actually breaks in production

One copy isn’t enough. Here’s how databases stay synchronized.

RAG is the dominant AI application pattern. Chunking is the #1 pain point.

Your downstream service will fail. Not might - will. Learn how to tackle these partitions in production.

Distributed agreement is fundamentally hard. Here’s how Raft actually works

Getting reliable structured data from LLMs is harder than it looks

Everyone quotes CAP theorem, but no one really understands it.

“Just retry” only works if your operation is idempotent

128K tokens doesn’t mean you should use 128K tokens

Wrong timeout settings cause more outages than bugs