10 Vibe Coding Mistakes That Quietly Destroy Beginner Careers
Vibe coding is simple: you describe the task in plain English, the model spits out code, you run it, and it works. Sometimes. For a while.
Then you land an interview and they ask you to explain your own project. You freeze and mutter, “Well… it kind of… here, I’ll just show you the prompt.”
My name is Sergey Kurilenko. I’m an ML developer, co-author of the “Neural Networks for Work” course, and reviewer for “Neural Networks for Business” at Yandex Practicum. In the past year I’ve seen the same story play out dozens of times: someone masters vibe coding, ships projects at lightning speed, and suddenly realizes they’ve built a career on a house of cards instead of solid ground. The pattern is so common I decided to write it up as a list of “harmful advice”—fun to read, awkward to recognize.
Table of Contents
1. Copy Code Without Reading It. The Model Is Smart, Right?
If the LLM gave you the code, it must be correct. Why learn what async does if it runs anyway? Why dig into useEffect when you can just ask it to “make the data load”?
I did this myself. Spent two days vibe-coding a React app that did exactly what I needed. On day three I had to change one button and spent 40 minutes hunting for where it rendered—because I was navigating my own project like a tourist in Tokyo subway.
Interviews make it even worse. They ask you to add a feature to your own code and you sit there like a student who bought a term paper.
Antidote: Before pasting anything, read it. See something unfamiliar? Ask the model: “Explain this line like I’m 10 years old.” Takes a minute. In a week you’ll start catching bugs with your eyes instead of your fifteenth prompt.
2. Tests Are for Cowards and Bureaucrats
The project works locally. Screenshots for the README are done. You show it to the client. They type their name in Cyrillic—the field you only tested with Latin letters. The app crashes with a white screen and a wave of existential dread.
Or you build an API, everything looks perfect on localhost:8000/docs, you deploy… and the first user sends null to a required field. Postgres throws a constraint violation, the frontend shows “undefined is not a function.” Classic.
Antidote: Ask the model to write tests right in the prompt: “Write the function and unit tests, including edge cases—empty strings, null, Unicode, numbers outside the range.” Run them every single time. Tests aren’t bureaucracy; they’re seatbelts.
3. Documentation Is for Nerds. You’re a Vibecoder
Why read docs when you can just say “connect Stripe for me”? The model surely knows every API by heart.
Real story: someone asked for a Telegram Bot API integration. The model delivered confident code with answerCallbackQuery and inline buttons—using syntax from a library that doesn’t exist. It Frankenstein-ed python-telegram-bot and aiogram imports. The developer spent three hours debugging their own logic before realizing the module was imaginary.
Models hallucinate with a straight face and professor-level confidence.
Antidote: Before any integration, open the actual documentation, copy the relevant section, and paste it into the prompt: “Here’s the Stripe Checkout API docs from 2025—write the integration strictly according to this.” Context changes everything.
4. Security Is Future You’s Problem
API key in the code? It’s only localhost. SQL injection? Who cares about my to-do list? CORS disabled? It works. Passwords in plain text? Only three users, all my friends.
Real timeline: Monday you push to GitHub with an OpenAI token in config.py. Fourteen seconds later a scanner finds it. Tuesday morning your account is billed $200 for someone else’s GPT-4 requests. Tuesday afternoon GitHub flags the repo and every future employer sees the warning.
Optimistic scenario. Pessimistic one involves a data leak.
Antidote: Run a quick checklist before every commit: secrets in .env and .gitignore, parameterized database queries, input validation. Prompt the model: “Audit this code for OWASP Top 10 vulnerabilities.” It won’t catch everything, but it closes the obvious holes—the ones that usually get you.
5. Don’t Set Boundaries for the Task. Let the AI Figure Out What You Wanted
“Make a finance tracking app.” Period. Database? Roles? Authentication? Let the model decide.
You wanted modern UX. You got a cyberpunk nightmare. The model invented its own crypto implementation, slapped together a half-baked ORM, and produced a 2,000-line monolith where business logic was mixed with button rendering.
I once asked for a “CRM” with zero details. It returned a Flask + SQLite app that stored customers, notes, tasks… and a weather forecast. No one knows why the weather was there—including the model.
Antidote: Break the task down before you open the chat. “REST API on FastAPI, CRUD for expenses table, PostgreSQL, Alembic migrations, Pydantic schemas. Skip auth for now—that’s the next step.” Vibe coding isn’t “delegate and forget.” It’s “set the task and control the result.” You are the product manager of your own code.
6. Error? Write “Fix.” Repeat 15 Times
Error in console. Copy. Paste. “Fix.” New error. “Fix.” Code doubles in size, works worse than before, and your git log (if it exists) reads like a cry for help: “fix,” “fix2,” “fix pls,” “fix final,” “fix final real.”
The model doesn’t fix root causes without context. Every “fix” layers a workaround on top of the previous workaround until the code becomes architectural lasagna.
My personal record: 11 straight “fix” iterations, after which the model started undoing its own earlier fixes. The bug completed a full circle and returned home with souvenirs—three extra try/except blocks and a pointless time.sleep(2).
Antidote: Read the traceback first. It’s a map, not profanity. Identify the exact line and what broke. Then prompt specifically: “TypeError on line 42: function expects list, gets None. Looks like fetch_data() returns None on empty response. What’s the best way to handle this edge case?” One strong prompt beats fifteen blind “fix” commands.
7. Git Is for Corporates
You’re solo. You have Ctrl+Z and a folder full of app.py, app_backup.py, app_old.py, app_DO_NOT_DELETE.py. Who needs Git?
You do.
First practical reason: you ask the model to refactor a file, it rewrites 200 lines, everything breaks, and Ctrl+Z only undoes the last five changes. The previous version is gone forever.
Second career reason: recruiters check GitHub. An “Initial commit” with 50 perfect files screams either “I generated everything in one go” or “I don’t know Git.” Clean commit history with meaningful messages shows you actually thought about the code.
Antidote: git init is the first command on any project. Commit in small, meaningful pieces: “Add expense validation,” “Fix Unicode handling in CSV parser,” “Refactor DB queries to parameterized statements.” That’s not busywork—that’s professionalism.
8. One Model for Every Situation in Life
You fell in love with ChatGPT. Great. Use it for code, SQL, DevOps, résumés, even grandma’s birthday card. Why try anything else?
This is first-love trap. You hit the model’s limits and assume that’s an AI limit in general. GPT struggles with long context → “AI can’t handle big projects.” One model writes weak tests → “AI sucks at testing.”
Meanwhile CLI agents like Claude Code, Copilot in the IDE, and Cursor each shine in different contexts.
Antidote: Treat models like an orchestra. One excels at refactoring, another at generating tests, a third at holding full-project context. A good vibecoder isn’t a fan of one tool—they’re the conductor.
9. You Don’t Need a Portfolio. Your Prompts Are the Skill
Why maintain a portfolio when you can demo prompt mastery live in the interview? They’ll love the future, right?
They won’t.
Scenario A: test task with no AI access. You sit in front of a blank editor like a pianist who only learned with auto-accompaniment.
Scenario B (worse): test task with AI allowed, but they ask you to explain every decision. “Why useCallback instead of useMemo?” “Uh… because the model wrote it that way.” Curtain falls.
Antidote: Build projects you can explain inside and out. In the README, don’t just say what it does—explain why you made each choice. “Chose SQLite over PostgreSQL because this is a single-user desktop app and I don’t need network database access.” That one sentence is worth more than 500 lines of generated code.
10. Fundamentals Are So Last Century. Now It’s All About Prompt Engineering
Algorithms, data structures, networks, design patterns—who needs that in 2026? The real skill is writing good prompts. Why learn how a hash table works when the model can make any dictionary?
Because without fundamentals you can’t tell good generated code from bad. The model suggests a nested loop over two 100,000-element arrays? O(n²) right there, but you miss it. It picks bubble sort because “bubbles sound cute”? Production collapses when real data arrives.
Same with the SQL query that runs in 5 ms on 100 rows but 40 seconds on 100,000. If you know indexes and EXPLAIN, you catch it in review. Otherwise you catch it at 3 a.m. when monitoring calls.
Antidote: Use the model as a tutor, not a replacement. Ask: “Why is a set better than a list here for membership checks?” “What’s the time complexity and can we do better?” “What’s the difference between a JOIN and a subquery in this case?” AI is a great teacher. But you still have to learn.
Building a career on a 'house of cards' through vibe coding often leaves beginners vulnerable, sparking a vital discussion around whether it truly replaces the foundational knowledge junior developers need in the long run. Learn more about Vibe Coding vs Junior Developers: Is Learning Code Still Worth It?
Instead of a Conclusion
Vibe coding is a fantastic tool. But “I can generate code” is roughly the same as “I can order food through Delivery Club.” What separates a real developer from a prompt operator is technical literacy, architectural understanding, and the ability to read and own the code—even the code the model wrote “for” you.
An LLM is a junior developer with encyclopedic knowledge and zero accountability. It will write whatever you ask. It won’t say “stop, this is a bad idea.” It won’t ask “are you sure?” That’s your job—to think, verify, and decide.
The sooner you start doing that, the faster you stop being “the guy who vibecodes” and become the one they actually hire.
And if you think none of these ten points apply to you… go back and reread number 6. It definitely does.
Source: habr.com
