End-to-end systems — not the model in isolation, the full pipeline. Most of the value (and most of the bugs) lives between the boxes on the architecture diagram. Tradeoffs that matter to the business — Lakebase vs Lakehouse, batch vs streaming, RAG vs fine-tuning. These show up in latency, cost, and risk, not just engineering preference. The unglamorous production work — eval harnesses, observability for non-deterministic systems, drift, guardrails. The stuff that separates a demo from something you can trust on a Tuesday morning.
- Four Vibe Coding Anti-Patterns
- Can AI Really Learn from Experience?
- Business Insights with Databricks AI/BI Genie: A Custom Approach to Trusted Analytics
- OLTP for Data Apps and AI Agents: Leveraging Lakebase and Lakehouse Architecture
- Understanding Agentic AI, Model Context Protocol, and Agent-to-Agent Communication
- Lakebase—Database for Intelligence
- How to Create a Flexible Data Pipeline with Delta Live Tables
- Delta Lake Simplified: Bridging Batch and Streaming for Modern Data
- Data Fabric Decoded: Principles and Practical Exploration
- TypeScript Just Got 10x Faster: What It Means for Developers




