Data Engineering
How to Build an AI-Ready Data Platform
AI projects rarely fail because of the model. They fail because the data underneath isn't trustworthy, accessible or governed. Here's what an AI-ready foundation actually needs.
· 8 min read
Insights
Practical thinking from engineers in the field.
Notes on AI adoption, data platforms, cloud architecture, system design and engineering leadership — written for people who build.
Topics we write about
- AI adoption
- GenAI architecture
- Data engineering
- Cloud architecture
- DevOps
- System design
- Engineering leadership
- Data analytics
- Product engineering
- Technology strategy
All articles
Generative AI
RAG vs Fine-Tuning: What Should Your Business Use?
Both techniques customize how an LLM behaves, but they solve different problems. A practical framework for choosing — and why the answer is often both, in the right order.
· 7 min read
Data Analytics
Designing Data Platforms for Real-Time Analytics
Real-time is a spectrum, not a switch. How to decide what actually needs sub-second freshness, and how to architect streaming without doubling your operational burden.
· 9 min read
System Design
How to Modernize a Legacy Backend Without Rewriting Everything
Big-bang rewrites are the riskiest project in software. The strangler pattern lets you modernize incrementally while the business keeps running.
· 8 min read
Generative AI
Building a Production-Ready GenAI Application
The gap between a GenAI demo and a GenAI product is evaluation, guardrails, observability and cost control. A checklist for crossing it.
· 10 min read
Cloud & DevOps
Reducing Cloud Costs Without Sacrificing Reliability
Cost optimization doesn't have to mean fragile systems. The biggest savings usually come from visibility, rightsizing and architecture — not from cutting corners.
· 7 min read
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