How Vibecoding Led Me to Depression — and Then to Vibe Engineering

In early 2025, the term *vibecoding* stormed into developer circles. Coined by Andrej Karpathy, it described a new paradigm where you describe a feature in plain English, an AI generates the code, and you accept it — almost blindly. The promise was seductive: 10x productivity, zero boilerplate, instant prototypes. By mid-2026, the hangover had arrived. I was one of those who crashed hard. This is the story of how vibecoding drove me into a productivity depression — and how I clawed my way out by discovering what I call *vibe engineering*. ## The Hype That Broke Me In late 2025, I adopted vibecoding wholesale. Using Claude 4 and GPT-5, I rewrote entire microservices by describing them in natural language. For two months, my output was astonishing: I shipped a full CI/CD pipeline in three days, a data pipeline in a week. But then the cracks appeared. | Metric | Before vibecoding (Q3 2025) | During vibecoding (Q1 2026) | |--------|-----------------------------|------------------------------| | Lines of code generated per day | 200 | 2,500 | | Bugs per 1,000 LOC | 5 | 42 | | Time spent debugging | 30% | 70% | | Confidence in code quality | High | Near zero | My velocity was fake. I was generating mountains of code I didn't understand. Every third PR introduced a subtle race condition, a wrong API call, or a security hole. I spent more time debugging AI-generated code than I ever spent writing my own. Impostor syndrome hit hard. I stopped trusting my own reviews. I was, effectively, a prompt jockey — and a bad one. ## The Breaking Point April 2026 was my worst month. I was tasked with integrating a payment gateway — Stripe. I prompted the AI: "Create a Stripe checkout session handler with idempotency." The AI returned 400 lines of beautiful TypeScript. I merged it without full review. Two weeks later, we discovered that under high load, the idempotency key logic was flawed: duplicate charges hit 12 customers for $499 each. The total loss was $5,988. That was my rock bottom. I realized that vibecoding without structured oversight is not coding — it's gambling. ## What I Learned: The Four Pillars of Vibe Engineering After weeks of research and painful refactoring, I developed a methodology I call **vibe engineering**. It's not anti-AI — it's pro-structure. Here are the four pillars: ### 1. Mandatory Unit Tests Before You Prompt Before I let AI write any logic, I write the unit tests myself. This forces me to define the contract. The AI then generates code to satisfy those tests. Failure rate dropped from 42 bugs per 1,000 LOC to 6. ### 2. Context Windows Are Not Memory Many developers assume the AI remembers the entire codebase. It doesn't. I now keep a strict `CONTEXT.md` file that I paste before each prompt: current architecture, constraints, and three examples of correct patterns. This single change reduced hallucinated API calls by 60%. ### 3. Diff Review with a Checklist I stopped accepting AI diffs blindly. I created a 12-point review checklist: - Are all error paths handled? - Is there any dead code? - Are environment variables validated? - Is the logic idempotent where required? This added 40 minutes per PR but eliminated production incidents. ### 4. The 80/20 Rule for AI Code I now let AI generate only 80% of a function — the boilerplate, the obvious logic. The remaining 20% — the tricky edge cases, the performance-critical loops — I write by hand. This hybrid approach gave me back my confidence. I'm no longer a prompt jockey; I'm an engineer who uses AI as a tool, not a crutch. ## The Results | Metric | Pre-vibe engineering (Q1 2026) | Post-vibe engineering (Q2 2026) | |--------|-------------------------------|--------------------------------| | Production incidents per month | 7 | 1 | | Average bug fix time | 4.2 hours | 1.1 hours | | Developer satisfaction (1-10) | 3 | 8 | | Code review confidence | 2/10 | 9/10 | I'm not alone. A recent discussion on Habr highlighted how many developers are hitting the same wall. One engineer reported that after adopting structured AI workflows, their team's rework rate dropped by 70%. The key takeaway: vibecoding without engineering discipline is a highway to burnout. ## Conclusion Vibecoding nearly broke me. It promised speed but delivered chaos. Vibe engineering — structured prompting, pre-written tests, strict review — saved my career and my sanity. The lesson is simple: AI can write code, but only you can ensure it's correct. If you're feeling the same depression I felt, step back. Define your process. Test before you generate. And never, ever trust a prompt blindly. For those looking to build robust AI-integrated workflows, ASI Biont supports connecting custom tools and APIs to build your own vibe engineering pipeline — with full control and auditability. You can learn more at asibiont.com/courses. The future of coding is not vibecoding. It's vibe engineering. [Source](https://habr.com/ru/articles/1054342/?utm_campaign=1054342&utm_source=habrahabr&utm_medium=rss)

How Vibecoding Led Me to Depression — and Then to Vibe Engineering

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