RAG Explained Simply: How Retrieval-Augmented Generation Really Works
A plain-language guide to RAG, why it matters, where vector search fits, and how to think about retrieval quality before building an AI assistant.
Live · Shipping in public, 24/7
Where AI meets the ◆ DBA mindset. Built by a builder.
Notes from the intersection of artificial intelligence, database engineering, product strategy, and the messy reality of building something from zero.
[AI] Multi-agent systems are eating SaaS
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[DBA] Postgres 17 ships incremental backups ↑
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[PRODUCT] Stop building features, start removing them
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[ENTREPRENEUR] Bootstrapped > venture-funded for 90% of ideas
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[AI] Vector indexes in production: lessons learned
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[AI] Multi-agent systems are eating SaaS
◆
[DBA] Postgres 17 ships incremental backups ↑
◆
[PRODUCT] Stop building features, start removing them
◆
[ENTREPRENEUR] Bootstrapped > venture-funded for 90% of ideas
◆
[AI] Vector indexes in production: lessons learned
◆
Four pillars
What you'll find here.
Hard-won lessons from a decade across the four disciplines that shape modern software businesses. Honest, technical, no fluff.
01
Artificial Intelligence
LLMs, RAG, agents, and AI shipped to production.
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02
Database Engineering
Postgres, Oracle, Cassandra, Mongo, Yugabyte — replication, performance, and surviving 3am pages.
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03
Product Management
Discovery, prioritization, and shipping what people use.
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04
Entrepreneurship
Bootstrapping, niches, pricing, and the real reality.
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Latest writing
From the notebook.
Long-form essays and quick technical notes. Roughly one a week.
AI Agents for DBAs: What They Can Help With and Where to Be Careful
A practical look at AI agents for DBA work: monitoring, runbooks, SQL explanation, incident triage, and the guardrails needed before automation.
Oracle 26ai Tablespace and Space Management: A Practical DBA Starter Guide
A DBA-friendly introduction to Oracle tablespaces, data files, autoextend, free space, temp usage, undo, and the SQL checks every Oracle admin should know.