An in-depth walkthrough of a Python curses-based application for manually marking Quranic verse boundaries in MP3 files, persisting marker state, and exporting split files. The article covers architecture, UI decisions, undo/redo, guided marking, and export pipelines, with an honest evaluation of its place among existing tools.

A deep‑dive into building a reproducible, animated Matrix‑style digital rain effect in pure SVG. Covers the architecture, implementation, configuration, and integration with a modular banner generator for Pelican static sites.

A deep dive into a Python tool that combines, purges, minifies, and redeploys CSS bundles for Pelican themes using a recipe-based pipeline. Learn how it records every build step, automatically replays the entire chain, and drops the result into the output folder – no full site rebuild required.

A practical engineering framework for designing LLM agent prompts as deterministic, version-controlled specifications. This article breaks down prompt architecture into composable layers — tone specification, structural constraints, output format enforcement, and metadata extraction — treating LLM agents as programmable infrastructure rather than conversational tools. Engineers and developers will learn how to build prompts that produce consistent, machine-parseable outputs suitable for automated pipelines.

A production-grade walkthrough for generating PDFs from Jinja2 templates using WeasyPrint. This article covers template design, CSS print styling, page layout control, and integration into automated document pipelines — replacing deprecated wkhtmltopdf workflows with a modern, reproducible Python-native approach.

Learn how to structure your Pelican content directory, define a rich metadata vocabulary, and treat Markdown files as queryable data records. This foundation enables all future dynamic‑like features without plugins.

This article explores projcat, a Python CLI tool that generates structured project manifests—directory trees and sliced file contents—optimized for feeding into LLMs. It covers architecture, advanced slicing features, and integration into developer workflows, emphasizing deterministic, reproducible context injection for AI-assisted coding.