AI Coding Training Now Available: Learn the Dibe Coding Methodology

Over the past two years, AI coding has exploded — with tools and demos promising to transform how we build software. Yet many teams, especially in enterprise environments, still struggle to move beyond experimentation. The challenge isn’t access to AI—it’s mastering how to work with it effectively.

Most tutorials, videos, and articles focus on individual techniques, tools, or prompt tricks. Many follow what we call ‘vibe coding’—loosely trying things until it works—or showcase highly polished demo videos that create FOMO and expectations that rarely hold in real projects. But AI coding fundamentally changes the developer’s role. Instead of playing every note by hand, you now conduct the orchestra—guiding, structuring, and shaping the outcome while the AI performs the execution.

It’s an inspiring shift, but also a demanding one—and it’s unrealistic to expect developers to master it on the fly through scattered tutorials and trial and error while still delivering in their daily projects. What’s been missing is a structured, end-to-end approach—one that shows how to work with AI productively and reliably within real projects and existing codebases.

At EclipseSource, we explored every major tool, technique, and workflow from the very beginning—adapting existing solutions and even building our own where necessary. Through systematic experimentation, shared experience across real-world projects, and numerous workshops, we discovered what truly matters: not more tools, but a pragmatic, tool-agnostic methodology.

That structured approach became Dibe Coding—and today, it’s available as a comprehensive training for developers and teams.

TL;DR — Dibe Coding in a Nutshell

Dibe Coding is a structured, tool-agnostic methodology for professional AI-assisted development. It replaces ad-hoc prompting and trial-and-error with a clear, repeatable process for delegating, defining, reviewing, and refining work with AI—across any tool or tech stack.

The new AI Dibe Coding Training teaches this process step by step, turning AI coding from experimentation into a reliable, professional practice.

🎯 Ideal for: developers, teams, and organizations adopting AI coding in real projects and existing codebases.

It’s delivered entirely online, so you can learn anytime, from anywhere, and progress completely at your own pace.

Now available in early access at 50% off the regular price. Early access participants receive lifetime access to all future updates and extensions at no additional cost.

👉 Get the training now

“The most immediate change I saw was my success rate. Before the training, AI coding felt like a lottery—maybe 1 in 10 tasks worked well for me. Now, with the Dibe Coding process, I’m consistently at around a 90% perceived success rate for my daily tasks. It’s transformed AI coding from an experiment into a predictable, high-value tool.”
— Senior Developer, Early Access Participant

Why Dibe Coding

AI coding has arrived—but many developers are still navigating it without a map, because no real one exists yet. The resources available online are diluted with exaggerated promises, vibe coding click-baits, or even suggest to replace developers entirely. With Dibe Coding, we take a realistic approach that is rooted in our daily practice in complex, real-world development projects, and our urge to deliver sustainable, predictable results and not just demos and proof-of-concepts.

This chapter explains why Dibe Coding was created and what makes it different. If you’ve ever watched a polished AI demo and wondered, “Why doesn’t it work like that for me?”, you’re not alone.

We’ll walk through the real challenges teams face when applying AI in actual projects, how developer roles are evolving, and why structure—not more tools—is the key to unlocking AI’s potential. Whether you’re leading a team or coding solo, this will help you see the value of a clear, shared methodology in the age of AI.

1. A Realistic Approach — Beyond Shiny Demos

Most AI tutorials and videos show perfect success stories—flawless prompts, instant results, and impressive demos. But real development doesn’t look like that. In everyday projects, you rarely get the ideal scenario. The model misses or misinterprets context, generates partial code, or produces results that simply don’t fit your architecture and would erode it over time.

If everything works perfectly, you have little to do—but the reality is that you often spend your time reacting to what didn’t. And yet, nobody tells you what to do when that happens. YouTube videos and product demos usually show the “perfect shot”—where everything goes as expected, the prompt works on the first try, and the output fits neatly into a toy project. But real-world development is far more complex; and often much messier.

That’s where Dibe Coding starts. It doesn’t promise magic—it provides a clear, repeatable process for dealing with real-world unpredictability. You learn what to do when things go wrong, how to recover quickly, and how to make every iteration productive instead of frustrating.

2. The New Role of the Developer

AI coding isn’t just another tool or feature—it changes the very nature of development.

Instead of writing every line yourself, you now orchestrate the process: defining what the AI should do, curating the right context, and making deliberate decisions about what to keep, refine, or redo.

This shift is profound — it can’t simply happen by introducing new tools or sprinkling AI into existing workflows. It demands a redefinition of the developer’s role, one that must be learned and practiced deliberately.

Unlike other approaches that treat AI as just another tool, Dibe Coding places this evolving role at the center. It focuses on the developer’s main responsibilities in AI collaboration — guiding, deciding, and maintaining control throughout the process — ensuring that structure and human judgment remain the driving force.

AI executes the swing. We still define the task, provide the context, and take responsibility for the result.

Dibe Coding gives developers a framework for this new role. It helps you stay in control, confident in how you delegate and how you decide—so you’re not replaced by AI, but amplified by it.

3. Structure Enables Learning and Optimization

There’s no shortage of AI coding content out there—countless tutorials, prompt patterns, and workflow tricks appear every week. But most of these focus on isolated aspects of the process. Without a unifying foundation, it’s nearly impossible to connect the dots or build a reliable, long-term practice. Developers end up collecting techniques without a framework to apply, compare, or improve them systematically.

At the same time, the field itself moves incredibly fast. New models, tools, and best practices appear constantly, and each organization faces its own unique project structures, coding standards, and compliance constraints. A good AI coding approach must therefore be adaptable—able to evolve as technology and team needs change. That kind of continuous improvement simply isn’t manageable without a solid foundation where new insights can fit in naturally and existing practices can be optimized over time.

Most teams experiment with AI coding in isolation. One developer discovers a great trick, another finds a workflow that works, but there’s no shared foundation—no way to compare, learn, or improve together. Without structure, every success stays local, and every mistake is repeated.

Dibe Coding provides the missing backbone: a shared process that turns isolated experiments into collective progress. Once everyone speaks the same process language, teams can share insights, measure what works, and continuously optimize—just like in any mature engineering practice. That’s how AI coding moves from “trying things out” to a scalable, professional discipline.

What Dibe Coding Is

At its core, Dibe Coding is a structured, repeatable process for collaborating effectively with AI coding agents.

It’s not a new tool or framework, but a methodology — a way to organize how developers think, delegate, and decide when working with AI.

Where most tutorials stop at “how to write a good prompt,” Dibe Coding provides a complete workflow that covers every stage of AI-assisted development — from deciding what to delegate to AI to reviewing, acting on, and refining the results until you’re truly done.

The Meaning Behind the Name

“Dibe” stands for Drive & Decide, a direct contrast to the more passive “vibe coding” many developers fall into when experimenting with AI.

It emphasizes active control — staying in the driver’s seat, guiding each step of the process with intention and judgment.

The Process of Dibe Coding

Dibe Coding is built around a set of well-defined, practical steps — not a rigid formula, but a clear structure you can adapt to any project or tool. The list below highlights the key steps of the process in action:

1. Decide — Assess whether a task should involve AI at all — or how it might need to be rethought before even creating the context. Sometimes the key is redefining the problem, not delegating it.

2. Define — Shape the task through precise Task and Context Engineering, turning abstract goals into structured, AI-ready inputs. While Context Engineering is widely known, Task Engineering—introduced in the Dibe Coding methodology—focuses on structuring and dividing work so AI can execute it effectively. It’s key to turning vague goals into clear, actionable steps.

Excerpt from the Dibe Coding process — after every AI result, the developer reviews, decides, and chooses the next step.

3. Invoke & Await — Once the context is ready, feed it to the AI clearly and deliberately. Know exactly what you’re sending, why, and what you expect in return. Then use the waiting time productively — review related materials or plan next steps — so progress continues even while the AI “thinks.”

4. Review & Decide — When the AI delivers results, that’s not the end — it’s the decision point. Evaluate the output critically and choose the next move: refine, redo, break it down, or move forward. This is where most materials stop, but Dibe Coding continues — teaching how to turn AI output into reliable progress, not just one-off results.

5. Follow Up — Apply, refactor, or extend the result. This includes a wide range of practical actions: integrating code, improving documentation, reshaping the original task, or capturing reusable prompts and generic prompt optimizations. It’s where iteration turns into improvement — the step that closes the loop and builds momentum.

Each step comes with concrete, practical guidance — showing you not just what to do, but how to think and decide when working with AI in real-world conditions.

The result is a workflow that feels natural to developers, works across tools, and scales from solo work to full enterprise teams. You can learn more about the complete process in our article AI-Native Coding Process: Dibe Coding as well as in our breakout on Task engineering.

Dibe Coding Evolves With the Field

While Dibe Coding defines a default, structured way of performing every step in the AI development process, it is not a static framework. The world of AI coding evolves daily—new models, techniques, and tools emerge constantly. The strength of Dibe Coding lies in how naturally it accommodates these innovations.

Because it provides a solid foundational process, new ideas can be integrated seamlessly and systematically, rather than through scattered trial and error. This allows teams to adopt improvements in a controlled, transparent way—without disrupting established workflows.

Just as importantly, a shared structure makes it easy to exchange insights and best practices across teams or even entire organizations. When everyone speaks the same process language, progress becomes collective: every experiment contributes to refining the whole.

Inside the AI Dibe Coding Training

To help developers and teams apply this process in practice, we created the AI Dibe Coding Training—a hands-on, tool-agnostic program built on the same principles we’ve refined across countless real projects.

At EclipseSource, we adopted AI coding from the very beginning. Every time a new tool appeared—Copilot, Cursor, Windsurf, Theia IDE, and many others—we tried it. Every technique we could find, we applied. Over time, this deep, hands-on exploration shaped the practical foundation of Dibe Coding itself—and ultimately this training.

The training walks you step by step through the Dibe Coding process, using live demonstrations and practical examples that mirror real-world development challenges. You’ll learn how to decide what to delegate to AI, how to structure and guide your collaboration effectively, and how to turn unpredictable AI behavior into a predictable, productive workflow.

The training also includes an introduction to how today’s major AI coding assistants actually work, what they can do, and how to use their strengths effectively within the Dibe framework. This understanding is essential for creating better context and for choosing action for each situation, ultimately getting consistent, high-quality results from AI.

From Vibe to Dibe — what the training teaches.

It’s not theory—it’s the result of years of experimentation, workshops, and real-world collaboration with teams adopting AI in their daily development. Everything you learn applies equally to other tools like Copilot, Cursor, Theia IDE, Claude Code, Cline, Roo, Windsurf, or any similar solution.

With this foundation in place, it’s time to take the next step — to learn Dibe Coding hands-on and become part of the growing community of developers mastering AI collaboration in real projects.

Get the AI Dibe Coding Training Now

The AI Dibe Coding Training is now available in early access at 50% off the regular price. Whether you’re working full-time, studying, or leading a team — the training fits your schedule. It’s delivered entirely online, so you can learn anytime, from anywhere, and progress completely at your own pace. Early participants receive lifetime access to all future updates and extensions at no additional cost.

👉 Get the online AI Dibe Coding Training now!

For group or organizational packages, contact [email protected] to discuss options.

The training has been extensively field-tested with early-access participants, from individual developers to entire engineering teams — and the feedback has been overwhelmingly positive. Participants describe it as both technically enlightening and personally empowering, helping them turn AI from an experimental curiosity into a reliable, daily companion in real projects.

💬 What Participants Are Saying

💬 “A didactic masterpiece.”
— Dr. David Faragó, inventor of Vise-Coding on the Dibe Coding Training

💬 “The Dibe Coding Training provides exactly what most developers have been missing — a structured, realistic way to work with AI. It goes far beyond prompt tricks and shows in detail why certain approaches work. For the first time, I really understood how to guide AI effectively instead of just trying things out. It’s clear, pragmatic, and deeply empowering.”
— Software Engineer, Early Access Participant

💬 “What impressed me most is how the training not only teaches the technical process but also addresses the emotional side of AI adoption. It helps experienced developers feel secure in their role and shows how their creativity and structure remain central. It turned skepticism into curiosity and gave our team a shared sense of direction.”
— Team Lead, Industrial Software Division

💬 “We’ve tested the Dibe Coding Training across our teams and received outstanding feedback. The structure, examples, and process explanations prepare both newcomers and senior engineers for real AI coding work. 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

Conclusion — From Experiments to a Professional Practice

AI coding changes how we work—but the developer’s value remains: clarity, structure, judgment.
Dibe Coding puts that value front and center, giving you a clear way to collaborate with AI—reliably, repeatably, and at team scale.

Start today:
✅ Learn the method
✅ Apply it in real projects
✅ Share it with your team
✅ Stay in control

👉 Get the online AI Dibe Coding Training now!

👉 For group or organizational packages, contact [email protected]

Be among the first to master Dibe Coding.

Your structured path to professional AI-assisted development starts here.

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