Practical architecture reviews, failure-mode analysis, and operating models for teams building database-backed systems, cloud platforms, and AI-assisted engineering workflows.
New to the site? These posts are ordered as a reading path, while Latest Notes below is ordered by publication date.
Recent field notes and breakdowns across AI engineering, databases, cloud, and system design.
Datadog Database Monitoring can surface enormous detail — and bill for it. The skill is choosing the few signals that answer real cost and reliability questions, and not paying to collect noise nobody acts on.
Token spend behaves differently from compute and storage — it scales with usage and prompt design. Treating it like an engineering cost line, the way you treat a database bill, is how you bring it under control.
The skills that make a good cost-aware DBA — measuring usage, finding structural waste, balancing cost against reliability — transfer almost directly to AI workloads. Database engineers are unusually well positioned to own AI cost.
A practitioner walkthrough of the review method: what to look at, in what order, how to quantify an opportunity honestly, and how to turn findings into a prioritized 30/60/90-day plan.
Aurora cost hides in places the console doesn't foreground — I/O charges, oversized writers and readers, replica sprawl, and storage. A structured way to find and reduce each without hurting reliability.
Table and index bloat and unused indexes are well-known Postgres problems — and direct cloud-cost problems: wasted storage, write amplification, and extra I/O. How to measure both with read-only queries and remediate safely.
Browse by the production problems the notes are written around.
Agents, context engineering, harness design, MCP, evaluation, token efficiency, and AI-assisted engineering workflows.
PostgreSQL, Aurora, MySQL, Oracle, Cassandra, MongoDB, pgvector, replication, migrations, indexing, and database operations.
AWS, Azure, GCP, OCI, Terraform, Kubernetes, CI/CD, Cloudflare, developer platforms, and operational control planes.
Architecture reviews, scalability, failure modes, guardrails, distributed systems, reliability boundaries, and production tradeoffs.
Core engineering principles, debugging workflows, observability, performance basics, reviews, and practical operating habits.
Short practical observations, checklists, production lessons, debugging notes, and decision patterns from real engineering work.
Multi-post arcs that connect practical decisions across a topic.
14 posts · Start: AI Token Cost Overruns: Why AI Coding Assistants Are Becoming the New Cloud Bill Problem
Databases60 posts · Start: The Database Observability Baseline: What Every DBA Dashboard Must Show
Databases57 posts · Start: PostgreSQL Slow Query Triage Workflow