Two years of coding with AI every day: what actually saves time, what creates hidden debt, and why your fundamentals are your only real defense against hallucinated correctness.

Rebuilding multi-stage Docker images on every code change wastes minutes and cache. How to decouple build dependencies from runtime containers for instant feedback.

Learn how to build production-ready Python background workers that scale under load. Covers worker pools, queue architecture, retry strategies, idempotency, and graceful shutdown.

How CPU word alignment and struct field ordering silently inject memory padding in Go. Learn field reordering to cut heap memory allocations and cache misses.

Aider is my favorite AI coding assistant. It lives in the terminal, commits directly to Git, and actually understands my codebase. But it was missing one thing: the ability to save and restore chat sessions. So I built it. The PR never got merged, but I've been running my fork for months now. Here's the story.

Can ClickHouse replace dedicated vector databases like Pinecone, Weaviate, or Milvus? Explore HNSW indexing, vector similarity search performance, and how to store embeddings with SQL in ClickHouse.

When thinking about performance, it's easy to focus on Big O notation. But in Go, the difference between the Stack and the Heap is often the difference between a service that scales and one that chokes on GC pauses. This post explores escape analysis, the "Pointer Myth", and why passing by value is often 40x faster than passing by pointer.

"The database that runs your app cannot be the database that analyzes your app". It's a hard lesson learned at scale. Early on, Postgres does it all. But as you hit massive scale, your analytics queries start killing your login APIs. This post breaks down the physics of Row-oriented (Couchbase) vs Column-oriented (ClickHouse) databases, and how to bridge them using Change Data Capture (CDC) for a robust, lag-free architecture.

How Go's concurrent tri-color mark-sweep collector works under the hood. Learn pacer triggers, GOGC tuning, and zero-allocation memory pool patterns.

CouchLens is a browser-based tool for analyzing Couchbase N1QL query performance. It parses system tables, extracts execution plans, and generates insights to help database administrators find performance bottlenecks without sending data to external servers. This post explains what it does, how to use it, and what features are coming.