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Technical perspectives on building production systems. No hype—just practical lessons from real projects.
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How Agentic AI Actually Works in Production (Not the Demo Version)
Most teams don't fail at AI because the models are bad. They fail because the surrounding system is poorly designed.
Why Most Analytics Dashboards Fail — and How to Build Ones Teams Actually Use
The problem isn't the visualization tool. It's that nobody agreed on what the metrics mean.
From MVP to Scalable SaaS: Engineering Decisions That Matter Early
The technical debt you accumulate in month 1 will cost you 10x to fix in year 2. Here's what to get right from the start.
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When NOT to Use AI: A Practical Guide for Founders and CTOs
AI isn't always the answer. Sometimes a well-designed workflow or a simple rule engine is more reliable, cheaper, and easier to maintain.
RAG Architecture Patterns: What Works in Production
Retrieval-Augmented Generation sounds simple. Getting it to work reliably at scale requires careful attention to chunking, embeddings, and reranking.
The Semantic Layer: Why Your Data Team Needs One
A semantic layer sits between your data warehouse and your dashboards, ensuring everyone uses the same metric definitions.
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