Essential Skills of a Vibe Coder

The following is an excerpt from the forthcoming book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond by Gene Kim and Steve Yegge.

The world is trying to figure out what changes and what doesn’t change when every developer is using AI on everything they’re working on, and which skills are the most important in this new world.

Because tools will evolve rapidly, core traditional software engineering principles will play at least as large a role, if not larger. Thus, it’s essential to:

• Create fast and frequent feedback loops for validation and control.
• Create modularity to reduce complexity, enable parallel work, and explore options.
• Embrace learning in a world where everything changes fast.
• Master your craft to thrive in an environment where all knowledge work will be changing in a short timeframe.

Learning these techniques will be critical for everyone in knowledge work, not just developers and vibe coders.

Creating Fast and Frequent Feedback Loops

The faster a system goes, and the more consequential the risks of failure, the faster and more frequent feedback you need. When a system is slow-moving, and nothing too bad happens when you make a mistake, you can get away with feedback loops that are slow and infrequent. For instance, in most cases, no one minds if a software build takes a few minutes longer than usual, so we can tolerate longer feedback cycles. However, as you speed a system up, such as when we increase code generation speeds by 10x or more, we need feedback cycles to speed up just as much, if not more. Feedback loops are the stabilization force that allows us to stay in control and steer the system toward our goals.

Let’s compare two chefs: Chef Isabella runs her kitchen with a fanaticism for feedback. Thermometers are checked, dishes are tasted at every stage by multiple cooks, servers relay customer reactions instantly, and specials undergo trial runs before hitting the main menu. When a slightly off-putting aroma wafts from the paella, she catches it before it reaches a customer. Her kitchen adapts when things go wrong during every service. She experiments with menus throughout the season and maintains her restaurant’s stellar reputation.

On the other hand, Chef Vincent is equally skilled but operating in a feedback vacuum. Dishes go untested until they land on the table, cooks work in silos, and servers don’t bother giving feedback anymore. When that batch of questionable seafood makes it out, the results are predictable: unhappy (and unwell) diners, scathing reviews, and maybe a visit from the health inspector. Vincent’s failure isn’t one of skill but of process—a failure to build in (let alone act on) rapid feedback.

For instance, in our stories when AI-generated code generation spiraled out of control, we didn’t create fast and frequent enough feedback. Our old habits proved to be wildly insufficient. You keep things safe and under control by building incrementally, testing frequently, and validating relentlessly. By doing so, you build trust in your AI partner and minimize rework—that soul-sucking and most expensive type of work. It doesn’t mean progress has to be strictly linear. You can explore multiple paths in parallel, like an army of ants searching for the best route to food, but each path needs its own frequent checkpoints.

In fact, Gene and his colleagues Jez Humble and Dr. Nicole Forsgren found in The State of DevOps Reports—a cross-population study that spanned 36,000 respondents over six years—that fast feedback loops, through CI/CD, were one of the most significant predictors of performance.

In Part 2, we’ll give you practical techniques for:

• Creating fast feedback loops.
• Leveraging AI to perform validation tasks and making checks faster and less error-prone than manual review alone.
• Ensuring you’re building the right thing (validation) and building the thing right (verification).
• Using feedback to steer your project effectively, perhaps toward that elusive product-market fit.

To achieve FAAFO, you must have the skills and processes to build trust in what your AI collaborator creates. Trust us first: Going fast without feedback is dangerous.

Creating Modularity

While fast feedback provides a control mechanism for moving quickly and safely, modularity partitions our system. It allows us to do work in parallel, creating independence of action. It makes the system more resilient, and it enables the low-risk exploration of alternative solutions (i.e., options).

In high-pressure and high-intensity situations, modularity can be the difference between a well-run professional kitchen and utter pandemonium. It’s the principle that allows different parts of a system to operate and evolve independently, and it directly impacts whether your team thrives or burns out. 

Dr. Dan Sturtevant and his colleagues did research that showed how developers working in tangled, non-modular systems are 9x more likely to quit or be fired. And again, The State of DevOps Reports showed that a modular architecture was also a top predictor of performance.

Alexander Embiricos from the ChatGPT Codex team described how an engineer using AI tools achieved excellent “commit velocity” building a new system from scratch. But when they ported it “into the monolith that is the overall ChatGPT code base that has seen ridiculous hypergrowth” (that is, a system with architectural problems) the results changed dramatically. Despite having the “same engineers, same tooling,” their “commit rate just plummets.” This real-world example shows that even at OpenAI, architectural constraints affect developers using AI too.

Let’s revisit Chefs Isabella and Vincent. Isabella’s kitchen is a model of modularity. Each station—pastry, grill, sauce—is distinct, with its own space, tools, and responsibilities. Chefs work independently, experimenting within their domain without causing system-wide meltdowns. When the pastry chef tries a new technique, the grill chef isn’t dodging flying flour. Communication between stations is clear and standardized. This independence allows them to work in parallel, combining elements from different stations to create exciting new dishes reliably.

Contrast this to Chef Vincent’s kitchen, which is a war zone of entanglement. Shared tools vanish, cooks bump elbows, and chefs and servers collide. A simple task requires navigating a maze of dependencies. Forget parallel work; chefs literally wait in line, blocked by others. His talented team is hampered not by lack of skill, but by the sheer friction of the system. Yes, sometimes new “dishes” emerge, but usually by accident when ingredients crash into each other. We’ve seen code bases like this, where developers (and their AI partners) can’t touch anything without triggering explosions elsewhere.

We want modularity in our code and projects, because it enables the independence of action for coding agents (and people) to work in parallel. We want to have them work on different tasks—refactoring a module, implementing a feature, writing tests—without causing horrendous merge conflicts (or worse, subtly) or breaking unrelated functionality. 

Good modularity also builds resilience. Like cloud software designed to handle failing disks, a modular system contains failures; if one module has a problem, the blast radius is limited. You can often isolate or replace it without taking down the whole system.

Modularity also unlocks optionality, a cornerstone of FAAFO. It allows you to explore different solutions in parallel. If you want to try three different caching strategies, you can build them as alternative modules. If you need to experiment with a new UI component, you can develop multiple versions. Keeping your system modular gives you freedom.

In Part 2, we’ll describe techniques such as:

• Task decomposition and breaking complex problems into smaller, manageable components with clear interfaces.
• Working with multiple agents simultaneously to enable work to happen in parallel without creating interference, or worse, giant merge conflicts.
• Branch management and version control strategies to explore multiple options.
• Agent contention detection to discover when agents are interfering with each other’s work.
• Enabling experimentation and exploration by creating modules, where you can try a bunch of things, mix and match, and pick the best combination.

Later, we’ll touch on a formula (NK/t) that helps quantify this power of parallel experimentation. And naturally, the faster your feedback loops, the more experiments you can run, increasing your chances of finding the best approach. In short, modularity helps achieve more in all of the FAAFO dimensions.

Embrace (or Re-Embrace) Learning

We’ve already talked about the importance of architecture and fast feedback loops in your AI-assisted kitchen. But there’s a third, equally crucial element that underpins everything, especially when your sous chef is an AI: You have to become re-accustomed to learning. AI is changing so rapidly that it is going to take constant learning and practice, at least for a while, to develop the good judgment you need—by taking risks, learning from mistakes, and adapting.

Think about our chefs again. Chef Isabella brings in new sous chefs, complete with their eccentricities, who are often challenging to wrangle. However, she knows that this is the future and becomes a relentless learner. She experiments (which can result in surprises or failures), does controlled trials, and seeks out other head chefs who are on their own journey. And with her new team, she learns to create ever more ambitious dining experiences that meet her customers’ increasingly demanding tastes. And somehow, it’s more fun than before.

On the other hand, Chef Vincent tries working with these new sous chefs a couple of times. One overcooked the fish, one deflated the soufflé, and one accidentally set their dish on fire. Vincent posts pictures of these culinary calamities on social media, ridiculing these strange new chefs, earning him his fifteen minutes of internet fame. But in time, he finds himself left behind as the culinary and dining world changes rapidly around him.

You might be surprised to learn that learning is learnable. You can improve your ability to learn at any time in your life. It’s coachable, teachable, and you can make your brain become more neuroplastic and adaptable through focus and lifestyle changes. Personally speaking, we have learned more in the last year or two than we have at any point in our careers—at an age, to be frank, when learning isn’t as easy anymore.

Learning means doing. It means tackling problems that seem insurmountable. It means taking risks, patiently wading through your mistakes, pushing until you get the outcomes you want, and troubleshooting creatively when things go wrong. Your willingness and indeed eagerness to improve how you learn will give you constant leverage in the next few years as AI ascends to touch all knowledge work.

Here’s an example. When Gene first started vibe coding with Steve, Gene was convinced that the then-new OpenAI o1 model would be great at ffmpeg and could help him overlay captions onto video excerpts. That is to say, subtitles on YouTube clips. Two hours later, Gene ran around in circles, typing increasingly complex ffmpeg commands. 

The AI was more than wrong; It was confidently wrong. Thinking about that particular Sunday afternoon still causes Gene to clench his jaw. But he learned an important lesson on when to give up on using AI to solve certain types of problems. It was a crummy experience, but he learned from it because it was a crummy experience. You learn by doing.

Cultivating a learning mindset has nothing to do with innate genius. Learning is about deliberate and intentional practice, much like Dr. Anders Ericsson described for mastering any complex skill.⁵ 

You need:

• Expert coaching: Leverage mentors, peers, and AI itself (asking it to explain concepts or critique approaches).
• Fast feedback: Build those tight verification loops we discussed, so you immediately see the results of the AI’s work and your prompts.
• Intentional practice: Consciously work on skills, like prompt refinement or evaluating AI suggestions in unfamiliar domains. Chop wood, carry water—or rather, vibe code, review output.
• Challenging tasks: Push yourself slightly beyond your comfort zone, using AI for problems you couldn’t solve alone yesterday.

In Part 2, we’ll describe how you can:

• Master the “count your babies” technique to systematically verify that AI delivers everything you asked for, preventing silent omissions that can break your systems.
• Develop your “warning signs detector” to spot AI’s subtle shortcuts and confidently challenge it when something feels suspicious.
• Use AI as a world-class consultant on topics you don’t fully understand or want to learn about.
• Craft suitably sized tasks that fit AI’s attention span, preventing the corner-cutting that happens when its context window gets overwhelmed.
• Implement strategic checkpointing rhythms to create a safety net of recovery points throughout your development process.
• Deploy “tracer bullet testing” to validate whether AI can handle tightly scoped technical challenges before investing significant time.

In short, achieving FAAFO becomes an exercise in “being a great learner.” Your commitment to continuously learning how to interact with, guide, and validate AI is what enables you to go faster, confidently pursue ever-more ambitious outcomes, whether working alone or as part of a team, and explore more options.

Mastering Your Craft

At this point, we’ve equipped your kitchen with AI-powered sous chefs. You’ve heard some stories, and by now you’re somewhat aware of both their potential upside and their potential dangers. We’ve hinted that you’re now the head honcho in your new role as a software developer, and we’ve repeatedly assured you that vibe coding will be more fun than any kind of software development you’ve ever done.

But we haven’t addressed the elephant in the kitchen: None of it matters if you don’t like cooking.

Chef Isabella thrives because she loves cooking. She may not be an expert in all the techniques or latest tools, but she has a vision for what she wants, she knows what’s important to her in the moment, and she can manage sous chefs who may know specific areas better. 

Chef Isabella lives to cook, while Chef Vincent cooks to live. He stopped learning any new techniques ages ago. He’s satisfied as long as the food tastes “decent.” As a result, few people wind up going to Chef Vincent’s restaurant because…well, his food is not that great.

Building things you love, or at least setting a determined vision and goals for yourself, will help you find and acquire the skills you need. Especially with AI there to help. All you need is the desire.

In Part 2, you’ll:

• Develop an intuitive understanding of the limitations and strengths of these AI tools, just as great chefs know when to trust their equipment and when to intervene.
• Get an overview of how AI code generation works, enabling you to use AI to build things in languages you haven’t used before.
• Learn how to pick things you love to work on, which will naturally drive the right learning behaviors, unlike following trends without purpose.
• Transform coding from a solitary activity into a collaborative dialogue that deepens your understanding with each iteration.
• Build a creator’s mindset that focuses on meaningful outcomes rather than getting lost in tool obsession or technical trivia.

Our advice: The more you throw yourself into vibe coding, the more you’ll master your craft of creating software—and that’s the high-level goal, isn’t it? Cook things you love, and cook different cuisines, which will force you to learn new tools and techniques. And of course, achieve ever-higher levels of FAAFO.

Conclusion

We began this journey exploring Dr. Erik Meijer’s striking declaration that “the days of writing code by hand are coming to an end.” It’s a provocative statement, to be sure. But it’s probably the simplest way to describe the fundamental transformation happening in software development. What started with ChatGPT and other AI assistants, at first seemed like a toy, but has evolved within two years into professional vibe coding, a new approach that’s reshaping how we create software.

In Part 1, we’ve examined the five dimensions of value that vibe coding creates: writing code faster, being more ambitious about what you can build, doing things autonomously or alone that once required teams, having more fun, and exploring multiple options before committing to decisions. These benefits combine to create a step change in what’s possible for developers at all levels. The economics of what’s worth building have opened up, and projects once eternally deferred are now within reach.

For both of us, these benefits have transformed our lives in deeply personal ways. Steve, after watching his beloved game Wyvern languish with over thirty years of unfixed bugs and aspirations, saw a path forward. For Gene, vibe coding reopened doors to coding that had seemed closed since 1998, enabling him to write more code in 2024 than in any previous year of his career. 

Hopefully we’ve convinced you why vibe coding is important. Now we’re ready to move into the kitchen and start cooking. In Part 2, we’ll hand you the knives, fire up the stoves, walk you through your first vibe coding sessions, and then step you through the theory and fundamentals to do it well.

Stay tuned for more exclusive excerpts from the upcoming book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond by Gene Kim and Steve Yegge on this blog or by signing up for the IT Revolution newsletter.

- About The Authors

Gene Kim

Gene Kim has been studying high-performing technology organizations since 1999. He was the founder and CTO of Tripwire, Inc., an enterprise security software company, where he served for 13 years. His books have sold over 1 million copies—he is the WSJ bestselling author of Wiring the Winning Organization, The Unicorn Project, and co-author of The Phoenix Project, The DevOps Handbook, and the Shingo Publication Award-winning Accelerate. Since 2014, he has been the organizer of DevOps Enterprise Summit (now Enterprise Technology Leadership Summit), studying the technology transformations of large, complex organizations.

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Steve Yegge

Steve Yegge is an American computer programmer and blogger known for writing about programming languages, productivity, and software culture for two decades. He has spent over thirty years in the industry, split evenly between dev and leadership roles, including nineteen years combined at Google and Amazon. Steve has written over a million lines of production code in a dozen languages, has helped build and launch many large production systems at big tech companies, has led multiple teams of up to 150 people, and has spent much of his career relentlessly focused on making himself and other developers faster and better. He is currently an Engineer at Sourcegraph working on AI coding assistants.

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