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

Your LLM application will hit rate limits. Here's how to handle it gracefully with exponential backoff, token buckets, and priority queuing.

Production decision-making checklist to understand if a vector database is really required

How models learn semantic relationships in language

The survival of your AI app depends on your token economics

The Invisible Tax of AI Tokenization: Why Non-English Speakers Pay More

How your prompt becomes vectors, attention, and text in milliseconds