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Most AI courses teach you how to use one specific tool. But AI is changing every week. Everybody is using Claude today, but tomorrow they might switch to the latest ChatGPT model. That’s why our course focuses on how to work with AI for the long term - no matter the tool you choose.
Learn moreArtificial intelligence no longer means just ChatGPT to most people. On the contrary, at one point ChatGPT is the best choice, then it’s Claude, then Gemini. One model writes better copy, another excels at analysis, and a third at programming. It’s all changing so fast that relying on just one tool simply isn’t enough anymore.
And that’s exactly why we’ve created the course “The Splendors and Miseries of Vibe coding.” Why learn to work with just one AI tool when you can master the technology itself?
We’ll teach you how to think like AI, how to properly formulate tasks, how to recognize the strengths and weaknesses of individual models, and how to switch between them depending on what you need to solve at any given moment. As a result, you’ll have no trouble working with GPT, Claude, Gemini, or any other tool that comes along in the next six months.
Just a few years ago, developing software took weeks or even months. Today, a single person can, in the course of an afternoon, create an app prototype, analyze thousands of lines of data, or build a simple AI agent. The pace at which technology is advancing is absurd. And that’s precisely why the way of thinking is becoming more important than the tool itself.
The problem with modern AI systems is often not that they seem stupid. On the contrary. They seem all too capable. They respond fluently, generate professional-looking outputs, and create the impression that “it makes sense, after all.” But that’s precisely when the greatest risk arises. People stop verifying, start trusting the system, and the error quietly shifts from the screen into reality.
This can lead to situations like:
All these situations have something in common. The problem doesn’t arise because AI can’t do something. It arises because people believe too quickly that the result is correct. That’s why in this course we won’t just focus on how to create faster with AI, but mainly on how to think critically about its outputs, verify them, and not be fooled by their convincing presentation.
In this course, you’ll experience a modern AI workflow: from initial prompts through vibe coding to working with agent-based systems, skills, or MCP connectors. At the same time, you’ll also see the limitations of current models, the ways they fail, and the moments when speed creates a false sense of certainty.
Don’t expect hours of theory without context. You’ll try things out, break them, fix them, analyze them, and discuss them using real-world scenarios. You’ll see how AI applications are built, why some work great, and why others fail the moment they encounter reality.
You’ll see how quickly a simple application, analytical tool, or automated workflow can be created. At the same time, however, we’ll observe where trust in the results begins to falter. When a model makes assumptions on its own. When code only works at first glance. When an agent takes a step you didn’t expect. And when you need to slow down, verify the logic, and take back control.
‍In this course, you’ll try your hand at, for example:
The course content also includes specific case studies from projects we’ve worked on. We’ll explore voicebots, analytical systems, AI workflows for corporate documents, and working with biological and health data.
The difference, however, is that we won’t just look at the final result. We’ll go into detail. We’ll show you what happened between the initial idea and the fully functional solution—including the problems, dead ends, and bad decisions. Because that’s where we learn the most from experience.
You’ll build your own mini-project, prototype, or workflow that you can continue to develop even after the course ends. Whether it’s a simple app, automation, data processing, or an AI assistant for documents, the goal is to take home something you’ve actually created with your own hands.
The course is structured as a combination of short blocks, practical examples, interactive demos, and collaborative analysis of real-world problems. One moment you’ll be watching AI create a functional app in just a few minutes; the next, you’ll be dissecting a software disaster that cost hundreds of millions of dollars.
We’ll switch back and forth between hands-on practice and explanations of what’s happening beneath the surface—from historical mistakes and statistical paradoxes to vibe coding, and on to working with modern AI tools, agent-based workflows, and real-world projects.
The course also includes open discussions and consultations on participants’ own ideas. You can bring a problem you’re currently tackling at your company. Together, we’ll break it down—from the initial idea to potential risks, dead ends, and realistic applications of AI.