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
Introduction Picture two doctors updating the same patient record at the same time - one in São Paulo, the other in London. Both are offline. When connectivity returns, whose changes prevail? This is not a hypothetical. It is the everyday reality of distributed systems: multiple nodes, no shared clock, no guaranteed network. The conventional answer has long been locking - one node waits while an
Introduction Some code works. Some code lasts. The difference rarely comes down to typing speed, syntax mastery, or how many nights you're willing to push through. It comes down to how you think about a problem before you write a single line. Big-O notation is a mathematical framework that describes how an algorithm performs as its input grows. In plain terms, it answers one question:
If you use ChatGPT, Claude, Grok, Copilot, or Gemini daily, it feels like you're talking to a person. It remembers what you said three messages ago. It references the project details you shared yesterday. It feels like the model has a persistent brain that is learning about you. But it’s a lie. From an architectural standpoint, an LLM is the most "forgetful" piece of software you will ever use. Ev
Most symbolic systems rely on multiple primitives. Addition, multiplication, exponentials, logarithms — each plays a different role in structuring expressions. But what happens if you force everything through a single operator? This idea becomes concrete with the EML operator: eml(x, y) = exp(x) − ln(y) In theory, this operator can express all elementary functions. But theory doesn’t tell us what