Yandex Practicum identifies 10 vibe coding anti-patterns that can derail an early career

Yandex Praktikum published a column on Habr about how vibe coding helps quickly assemble projects, but can just as quickly undermine a career start. ML developer Sergey Kurilenko collected ten typical mistakes of beginners and showed why speed without understanding easily turns into a house of cards.

Why this topic matters

The author describes a familiar scenario for 2026: a person launches an LLM, assembles an interface, API or bot in an evening, and then cannot explain what exactly is happening in their own code. The problem is not with vibe coding itself, but with treating the tool as a replacement for engineering thinking. In the short term it gives a magical effect, in the long term it creates a fragile project that is hard to maintain, debug and show to an employer.

The material is formatted as a set of bad advice, but essentially it is a checklist of career risks. Kurilenko is not attacking beginners who use AI, but rather the habit of delegating everything to the model at once: reading code, checking architecture, security, diagnosing errors, and even choosing tools. In such a scheme, a person remains a chat operator, not a developer, and this, according to the author, is most noticeable in interviews and test assignments.

Where beginners make mistakes

The most frequent failures are not related to one technology, but to basic development discipline. The author reduces them to a repeating pattern: the model writes confidently, the user trusts it blindly and notices the cost of this confidence too late. Because of this, errors look not like isolated mistakes, but like a chain of habits that first accelerates work, then breaks the project, portfolio and impression at an interview for a beginner.

• Copying code without reading and trying to understand unfamiliar constructs
• Refusing tests and checking edge cases like null, Unicode, and empty strings
• Ignoring relevant documentation and trusting library and API hallucinations
• Storing keys in code, weak data validation and other obvious security holes
• Vague tasks for the model and endless loops of "fix it" commands instead of proper debugging

The author separately goes through Git, portfolio and model selection. If a developer cannot commit in small steps, cannot explain solutions in README, and builds the entire process around a single model, AI begins to mask rather than strengthen weak points. The final, most painful point is abandoning fundamental knowledge. Without a foundation in algorithms, data structures, SQL and architecture, it is difficult to understand where the model saved time and where it imperceptibly laid the groundwork for a future failure in production.

What to do instead

The author's practical advice is simple: use LLM as an accelerator, not a crutch. Before generation, tasks need to be narrowed to a specific scope, stack specified, constraints and readiness criteria set. After generation — read the code, run tests, check against live documentation, verify vulnerabilities and commit changes to Git. If an error occurred, it is more useful to first parse the traceback and formulate a hypothesis than to send the model the same message fifteen times in a row.

LLM is a junior developer with encyclopedic knowledge and zero accountability.

From this logic follow even stronger recommendations: try different tools for different tasks, build a portfolio only from projects you can defend verbally, and use AI as a tutor to fill gaps in your foundation. That is, ask not only "do it", but "explain why it's like this", "what are the risks", "how is this approach better than alternatives". In such a mode, vibe coding remains fast, but stops being blind.

What this means

For the market, this is another signal that the era of AI development does not nullify the profession, but raises the bar. Anyone can now quickly assemble a prototype, but value increasingly shifts toward those who can verify, explain and bring generated code to a working product. For beginners, this provides an unpleasant but useful conclusion: a career is not broken by vibe coding itself, but by the habit of letting it do the thinking for you in the coming years.