A practical framework for choosing whether to build, adopt open source, or buy data and AI technology based on strategy, cost, control, and operational capacity.
Modern DataTools uses page caching, targeted invalidation, and shared data caches to reduce repeated work without allowing important information to remain outdated - in the article we cover our key lessons for these mechanisms.
Practical lessons from using coding agents on a growing codebase: repository rules, changelogs, scripts, testing, and context.
How Modern DataTools redesigned Stack Recommender with interactive customization, shareable URLs, evidence quality, integration visibility, and clearer methodology.
How to add AI on top of a modern data stack: the practical layers, tools, and guardrails for building useful agents, retrieval, and internal copilots.
A three-layer playbook — lifecycle policies, usage-based cleanup, and team rituals — to stop warehouse storage from compounding silently.
After scoring 500+ data tools on a 100-point framework, clear patterns emerge. Here are the ten that separate great tools from forgettable ones.
Local LLMs are practical for content generation, legal document processing, and internal knowledge bases. I benchmarked five Qwen models on my MacBook Pro. Qwen 3 14B scored 91/100 avg vs 62 for Qwen 2.5 14B -- same size, dramatically better. Newer models performed worse.
A practical 2026 guide to starting an AI-assisted software project — tools, agent orchestration, Git rules, baselining, documentation, and lessons learned.
How specs make AI coding reliable—and redefine the manager's role
Tech front lines to AI era: real stories on leading teams, testing tools, and data engineering wins—short, human-crafted lessons for your daily grind.