#engineering

Darbit, the rabbit in Interlusion's logo, on being animated: bending ears, masked eyes, a hat that spins without moving, and one rule: stay the logo.

Why Interlusion builds its marketing site with Astro, and what a zero-JavaScript-by-default architecture means for performance, simplicity, and AI-native development.

A walkthrough of a three-layer design token architecture with Tailwind CSS v4's native @theme directive. No config files, no build tools, just CSS.

HSL promised human-readable colour and lied about lightness. OKLCH delivers: perceptually uniform colour for P3 displays, design tokens, and AI agents.

Design systems fail when design and code drift apart. Design tokens fix the interface: one source of truth that both sides can read, write, and ship from.

VectifyAI's PageIndex swaps vector search for tree reasoning and scores 98.7% on FinanceBench. The result is real. The question is what it actually proves.

Everyone obsesses over models and prompts. The real bottleneck in RAG is retrieval, and fixing it takes engineering, not bigger context windows.

A plain-language introduction to Retrieval-Augmented Generation: what it does, how it works, and why it is becoming the default way to use AI on your data.

sqlc generates type-safe Go code from plain SQL. No ORM, no runtime reflection, no surprises: your schema, your queries, and generated code that compiles.

Both are CNCF Graduated, and both do GitOps well. Their architectures disagree about who the primary operator is. That matters once agents enter the loop.

Angular spent years as the framework people loved to leave. Then it quietly shed everything that made it heavy, added everything that makes it modern, and became the frontend framework best suited for the age of AI.

Most architecture documentation is either absent or abandoned. Arc42 gives it structure that humans can navigate and AI agents can maintain. Here is how we use it and why it stuck.

Internal docs used to rot the moment they were written. Now AI writes them, maintains them, and uses them as context. The oldest chore pays for itself.

Context7 fixes the context pollution that makes agents hallucinate APIs. A supply-chain flaw raises the deeper question: who fills the context window?

Prompt engineering was the warm-up act. Context engineering decides what reaches the model, when, and how. That is what makes agents drift or compound.

The object-relational impedance mismatch is real, ORM query languages add complexity instead of removing it, and AI agents write better SQL than ORM code. Here are the alternatives.

When agents write and maintain API backends, the criteria shift: predictability, fast feedback and low ambiguity beat elegance. That points to Go.