v.j.k.

Traditional AI coding metrics (lines of code per prompt, time saved) are like judging a chef by ingredient count—they miss what matters. The **CAICE framework** (pronounced "case") measures AI coding effectiveness across 5 dimensions: Output Efficiency, Prompt Effectiveness, Code Quality, Test Coverage, and Documentation Quality. Real case studies show that developers with high traditional metrics often create technical debt, while those with strong CAICE scores build maintainable, team-friendly code. It's time to measure what actually matters for sustainable development velocity.

TechDogs

Vibe Coding flips the script—just talk to AI and it builds your code! 🚀 This casual-convo workflow,...

Javeed Ishaq

Gemini acts as a conversational AI agent with direct filesystem access and multi-step automation, contrasting with Cursor's IDE-integrated real-time suggestions and Claude's general-purpose web-based chat for conceptual tasks.

Javeed Ishaq

Use AI to code smarter—not lazier. Discover a balanced workflow that strengthens your skills while leveraging the power of AI.

Victor Leung

Software development is undergoing a major transformation. With the rise of large language models...

Artem Dudarev

How I am building SpeechDown — a Python CLI that turns voice notes into searchable Markdown, using DDD, Hexagonal architecture, and AI-assisted coding.