Real-World AI Coding ("Dibe Coding")

Make AI Coding Predictable, Reliable, and Genuinely Productive AI tools can dramatically accelerate software development — but only if you use them in the right way. Most AI coding tutorials show magical demos: an app generated from one prompt, or a tool that seems to “just work.” In real engineering work, these examples rarely hold up. Projects are complex. Requirements shift. Codebases carry deep, implicit knowledge. If you rely on trial-and-error prompting, output becomes inconsistent, unpredictable, and hard to trust. This training fixes that. It is practical and applied: you’ll learn a step-by-step workflow you can immediately use in real codebases and real tasks — not theory, not hype, not academic abstractions. You’ll learn how to collaborate effectively with any AI coding tool — Copilot, Claude Code, Cursor, Cline, Windsurf, Theia IDE, and others — using a tool-agnostic workflow built for professional software development. This structured approach is called the Dibe Coding methodology: a practical, repeatable process for getting consistent, high-quality results from AI. Built on real projects. Refined with enterprise engineering teams. Proven to work. What participants are saying: “A didactic masterpiece.” — Dr. David Faragó, inventor of Vise-Coding “Now I’m at around a 90 % success rate. AI coding went from a lottery to a predictable, high-value tool.” — Senior Developer, Early-Access Participant “It bridges the gap between experimentation and reliable practice — exactly what we needed to turn AI from hype into day-to-day productivity.” — Engineering Manager, Enterprise Tools Group What You’ll Be Able to Do After This Course - Use AI coding with intention and structure, not guesswork - Decide when and how AI should help - Provide the right context for accurate results - Break down tasks into AI-friendly steps - Apply a repeatable workflow for reliable output - Review and refine AI results quickly - Integrate AI into existing, complex codebases - Use any AI coding tool more effectively and confidently - Everything taught is practical, tool-agnostic, and ready to apply immediately. What You Get - Step-by-step video lessons with live demos - Real-world examples from complex codebases - Tool-agnostic approach — works with any AI coding tool - Lifetime access including all future updates Why This Training Is Different Most AI courses: focus on a single tool, rely on unrealistic “one-prompt magic,” and ignore the complexity of professional development. This course is different because it: - Is practical and hands-on - Uses real-world coding scenarios - Was refined with professional engineering teams - Focuses on workflow, not fragile tricks - Works with all AI coding tools, now and in the future The goal is simple: Make AI coding predictable, controlled, and genuinely productive in real software projects. What’s Included - Step-by-step video lessons with real coding demos - A complete workflow you can use immediately - Examples from real, complex codebases - A fully tool-agnostic approach - Additional sections for tool-specific knowledge - Lifetime access, including all future updates Who This Course Is For Ideal for: - Developers who want consistent results from AI - Engineers working in real-world codebases - Tech leads adopting AI tools on their teams - Anyone looking for a practical, professional approach to AI-assisted development Level Up Your Skills Take the next step toward becoming an AI-native professional software developer — one who doesn’t depend on specific tools, but on a deep understanding of how to adopt AI efficiently and collaborate with it effectively. Start mastering AI coding today!

What you'll learn:

• Work effectively with any AI coding tool such as Copilot, Claude Code, Codex, Cline, Cursor, Windsurf, Theia IDE, etc.
• AI for professional software development on exiting code bases where quality matters and not just demos and proof-of-concepts are produced
• How to structure your AI coding workflow for consistent, high-quality results
• When and how to use techniques like context engineering and task engineering
• Practical follow-up actions to efficiently refine AI-generated code
• How teams can collaborate and share common AI workflows