Help for Vibe Coders

I’ve provided the resources below to assist vibe coders. If you still have questions or want to talk about your plans or project, set up a free 30 minute consultation with me!

In February 2025, Andrej Karpathy coined the term “vibe coding” to describe a new way of building software: describe what you want to an AI, let it generate the code, and focus on whether it works rather than how it works. By November, Collins Dictionary had named it their Word of the Year.

The appeal is obvious. Vibe coding democratizes software creation. It lets people with ideas but without traditional programming backgrounds build functional prototypes in hours instead of months. It removes barriers that have historically kept non-developers on the sidelines. For rapid experimentation and personal projects, it delivers something genuinely new: the ability to turn an idea into working software through conversation.

But something else happened in 2025. As vibe-coded prototypes moved toward production, patterns emerged. Codebases grew beyond what their creators could understand. Small changes broke unrelated features. The same bug fixed three times kept coming back. What worked perfectly in demos failed unpredictably with real users.

By September, Fast Company reported that senior engineers were describing work on AI-generated codebases as “development hell.” Stack Overflow’s 2025 survey found that 66% of developers said AI-generated code is “almost right, but not quite.” A new term appeared in tech circles: “vulnerable developers,” describing people who can ship a first version entirely through vibe coding but struggle to debug, extend, or secure what they created.

This is not a failure of vibe coding itself. It is a gap in foundational knowledge.

The engineers who thrive with AI coding tools share something in common: they understand the principles that make software reliable, maintainable, and secure. They know why idempotency matters when an API call might retry. They recognize when state management will become a problem. They plan for errors before errors happen. These concepts are not difficult to learn. They have simply never been taught to the people now building with AI.

That is what this series is for.

“Engineering for Vibe Coders” bridges the gap between prototype and production. Each article introduces one foundational concept that professional software engineers rely on every day. You will not find lengthy code tutorials or computer science theory. Instead, you will find practical explanations, pre-prototype checklists, and the questions you should ask before writing your first prompt.

The goal is not to turn you into a traditional developer or to slow you down with unnecessary complexity. The goal is to give you the engineering instincts that make vibe coding sustainable. When you understand these principles, you stop being vulnerable to your own creations. You gain the ability to grow your prototypes into real products, collaborate with engineering teams, and build software that works not just today but six months from now.

Whether you are a founder building an MVP, a product manager prototyping a feature, or a curious creator exploring what AI makes possible, these fundamentals will serve you at every stage.

Engineering for Vibe Coders Series

Foundations

• Separation of Concerns – Organizing your system so each part has one clear responsibility
• Requirements Thinking – Defining what the system must do before generating code
• System Design – Deciding how parts of your application fit together
• Trade-Offs & Constraints – Making conscious decisions between competing goals and limits​​​​​​​​​​​​​​​​
• Component Architecture – Breaking your application into clear, purposeful pieces that work together
• Frontend vs Backend – Knowing what runs in the browser versus what runs on your server
• State Management – Tracking what your app knows and preventing confusion
• Data Validation – Ensuring inputs and outputs meet your expectations
• Data Structures – How to organize information so your prototype stays manageable
• Edge Case Thinking – Anticipating the unusual scenarios that break your prototype

Reliability

• Error Handling – Planning for when things go wrong
• Idempotency – Making operations safe to repeat
• Event Handling – Designing for systems that react to what happens
• Retry Strategies – Deciding when to try again and when to give up
• Rate Limiting – Controlling how much usage your system accepts before it breaks
• Timeouts & Circuit Breakers – Stopping failures from spreading when dependencies hang or break
• Graceful Degradation – Designing systems that bend instead of break when something fails

Execution

• Sync vs Async – Choosing how your code runs
• Threading & Parallelism – Doing more than one thing at a time
• Caching – Storing results to avoid repeated work and reduce costs
• Background Jobs – Running work separately from the user request
• Race Conditions – Preventing bugs that only happen when timing goes wrong
• Batching Operations – Grouping work together to reduce overhead and improve throughput
• Long-Running Processes – Handling operations that take longer than a single request can wait

Infrastructure

• Development Environments (IDEs) – Choosing and configuring tools that catch mistakes while you code
• Database Types – Picking the right place to store your data
• Version Control Basics – Tracking changes without losing your work
• Configuration Management & Secrets Basics – Handling settings and credentials safely
• Deployment Targets – Understanding where your code can run and what each option requires
• CI/CD & Deployment Discipline – Automating the path from code change to running software
• Messaging Queues – Managing communication between components without blocking or losing messages
• Backups & Data Recovery – Protecting your data and planning for recovery when things go wrong

Testing & Quality

• Debugging – Systematically finding and fixing problems instead of guessing
• Automated Testing – Verifying your code works without manual checking every time
• Performance & Scalability Testing – Finding out how your prototype behaves under real-world load
• Regression Testing – Protecting what already works while you keep building
• Mock External Services – Building and testing without depending on systems you do not control
• Testing in Production – Using feature flags and gradual rollouts to validate changes safely with real users

Observability & Security

• Logging & Observability – Understanding what your app is actually doing
• Authentication vs Authorization – Controlling who can access what
• Security-First Coding – Building with protection in mind from the start
• Prompt-Injection – Defending your AI features against malicious inputs
• Performance Monitoring – Tracking how fast your system is running for real users
• Alerting & Error Notifications – Getting notified when something needs attention
• Auditing – Recording who did what so you can answer questions later

Start anywhere. Each article stands alone. But if you work through the series, you will develop the engineering mindset that separates prototypes that grow from prototypes that collapse under their own weight.

For additional assistance, check out AI Builders!