PostgreSQL Query Rewriting Techniques The previous articles in this series covered performance problems you fix by adding indexes, restructuring joins, or tuning memory. This one is about the queries where the plan is "fine" — every node is doing something reasonable — but the query itself is asking the wrong question, producing unnecessarily large intermediate results or forcing the planner dow
I watched 30 users talk to the same voice agent Same script. Same questions. The only thing I changed was the response latency: 300ms, 500ms, 800ms. At 300ms, people just talked. No awkward pauses, no confusion. One user didn't even realize it was an AI until I told her afterward. At 500ms, something shifted. Users started talking over the agent. They'd ask a question, wait half a second, then r
How intentional loading decisions keep your app fast at scale. Frontend performance is not a late-stage cleanup task. It’s not tech debt. It’s a set of decisions we make every day while we code — what we load, when we load it, and how we render it. The answer depends on the importance of the code, its size, and when the user actually needs it. Get that wrong, and the browser pays for everything
Is your website throwing 502 errors whenever an external API starts lagging? It is a common engineering grind where slow dependencies choke your server and kill your response times. The fix is not adding more resources. It is about changing how you handle work. Stop making users wait for external processes to finish. Offload heavy tasks to background jobs and queues. Distinguish between workers
Are you sure your server is performing at its peak? Understanding your server's capabilities is crucial for delivering a smooth user experience and preventing costly outages. This article will guide you through the essential tools and a practical methodology for benchmarking your server, ensuring it meets your application's demands. Before diving into the "how," let's solidify the "why." Benchmark
The first article on this blog explained how it was built in 30 minutes with Claude Code. Naturally, a blog needs comments. Same constraints: no database, no external dependencies, no Disqus tracking visitors. Just PHP + JSON files. Built in one session with Claude Code — the interesting part wasn't the code, it was the security audit that followed. A comment system without a database seems trivia
When building applications with large language models (LLMs), one of the most overlooked costs is how structured data is represented. Most systems use JSON. And JSON is inefficient for LLM input. KODA (Knowledge-Oriented Data Abstraction) is a schema-first data format designed to reduce token usage when sending structured data to LLMs. It works by: Defining structure once (schema-first) Encoding v
Dapper vs. Entity Framework When building data-driven applications in .NET, two of the most popular data access technologies are Dapper and Entity Framework Core (EF Core). While both serve the same fundamental purpose—interacting with databases—they take very different approaches. Choosing between them depends heavily on your performance needs, development style, and project complexity. Let’s b