A guide to writing effective AGENTS.md files for AI coding agents, based on research data and best practices.
Discussion on MCP servers, apps, and WebMCP for AI agents to perform structured actions on websites.
Guide to Azure cost optimization using the Well-Architected Framework, focusing on reducing waste without sacrificing quality.
Explores Bitter Lesson Engineering, advocating for AI systems that discover solutions autonomously rather than relying on human-coded logic.
A developer shares how using Claude Code enabled them to build 17 diverse side projects, including TUIs, games, and tools, in just two months.
A skeptical take on AGI hype, arguing against monolithic 'God models' and advocating for pragmatic AI development.
Guide to migrating from self-managed DNS to Azure Private DNS with PowerShell automation.
Explains the difference between an AI agent's inner loop (verifying work within a task) and outer loop (learning across tasks).
A tech author realizes she wrote the wrong book for engineers, then rewrites it based on community advice about software buying.
A guide to the core principles and systems thinking required for data engineering, beyond just learning specific tools.
A guide to designing reliable, fault-tolerant data pipelines with architectural principles like idempotency, observability, and DAG-based workflows.
Argues that data quality must be enforced at the pipeline's ingestion point, not patched in dashboards, to ensure consistent, reliable data.
Explains idempotent data pipelines, patterns like partition overwrite and MERGE, and how to prevent duplicate data during retries.
Explains how to safely evolve data schemas using API-like discipline to prevent breaking downstream systems like dashboards and ML pipelines.
A guide to choosing between batch and streaming data processing models based on actual freshness requirements and cost.
Explains data partitioning and organization strategies to drastically improve query performance in analytical databases.
Explains the importance of automated testing for data pipelines, covering schema validation, data quality checks, and regression testing.
Explains the importance of pipeline observability for data health, covering metrics, logs, and lineage to detect issues beyond simple execution monitoring.
A practical, tool-agnostic checklist of essential best practices for designing, building, and maintaining reliable data engineering pipelines.
A comprehensive guide to data modeling, explaining its meaning, three abstraction levels, techniques, and importance for modern data systems.
