Permissive licenses cover code, not brand. Here's why open-source forks get cease-and-desist letters and a practical step-by-step to rename one cleanly.
How to build reliable LLM classification pipelines for high-stakes decisions — fixing confidence calibration, output validation, and human escalation.
Matt Pocock's dictionary-of-ai-coding repo demystifies AI jargon. Here's why shared vocabulary matters and which terms you should actually learn.
Aviation solved the automation skill-decay problem 30 years ago. Here's how their framework applies to AI coding tools and what developers should do about it.
Senior Python interviews often test trivia over real skills. Here's how to handle the gotcha questions and what actually matters for preparation.
AI coding assistants can rebuild the mental context you lost on abandoned side projects. Here's a practical workflow for reviving stalled repos and actually shipping them.
Fix your AI coding assistant's generic outputs by building custom skills — modular instruction sets that give it the domain context it needs.
Why talented engineers write bad code at big companies, explored through the lens of authentication — plus a practical comparison of auth tools.
r/programming banned all LLM content, sparking a major debate about AI fatigue in developer communities. Here's what it means for how we evaluate tools.
AI-generated code creates comprehension debt that slows debugging. Here's a practical process for balancing AI tools with hand-written code.
AI agent orchestration code becomes unmanageable fast. Here's why general-purpose languages struggle with AI workflows and how DSL-based approaches solve it.
A step-by-step guide to safely migrating LLM integrations when new model versions release, with practical code examples for shadow testing and defensive parsing.
When AI generates most of your code, maintenance becomes the real challenge. Here's how to prevent context loss, inconsistency, and silent rot in AI-heavy codebases.
Stop drowning in theory. Build a working compiler with recursive descent parsing and a stack VM — no Dragon Book required.
AI-generated code often breaks in production due to hidden assumptions. Here's how to audit vibe-coded projects and build a workflow that actually holds up.
Stop reading code blind. These git commands give you a mental map of any codebase in five minutes — who works on what, where the bugs are, and which files matter most.
The 1999 satirical essay on unmaintainable code is still painfully accurate. Here's how to identify and fix the most common patterns it describes.
When vibe-coded projects break down, here's how to diagnose the mess, fix state spaghetti, eliminate duplication, and build maintainable code going forward.
A practical guide to migrating from AI-dependent to AI-augmented development, with real auth code examples and tool comparisons.
Feeling like AI tools killed your love of coding? Here's why it happens and a practical framework to bring back the satisfaction of solving problems yourself.
Step-by-step guide to diagnosing and recovering from Git repository corruption, from broken HEAD refs to damaged packfiles.
