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Key Takeaways Vibe coding transcends small projects, with tech giants competing to master multi-file AI-augmented programming tools.
Structured maintenance and smart input crafting result in significant time and cost savings in complex machine learning projects.
We tend to think vibe coding is only for people who can’t code, or that it’s limited to small projects. But it’s hard to talk about hindsight when the story only began earlier this year. Right now, OpenAI, Anthropic, Meta and Google are locked in fierce competition, investing billions of dollars to launch revolutionary AI tools that are all designed to handle multi-file projects.
As an engineer applying AI tools to complex projects, especially in machine learning, I’ve managed to cut both time and costs with three key solutions.
Related: I ‘Vibe Coded’ an App Using AI — and Got a Working Product in Under 5 Minutes for Free. Here’s How I Did It.
GitHub as the repository
The temptation with vibe coding is to accept whatever the AI generates and move on. That can lead to “zombie code” and untraceable bugs. The safest practice is to keep all source files and tests in a managed repository. In that case, I use GitHub to automatically run my tests on each commit and highlight indentation errors and broken functions. If a test fails, GitHub’s AI assistant points me to the offending line and offers a suggested fix. In my experience, this saves hours of searching through misaligned tabs.
To make this work, you should set up continuous integration from day one. Choose a default branch (usually main), configure automated tests and enforce pull‑request rules in GitHub. When a test fails, ask the AI assistant to explain the conflict before accepting any changes. This approach keeps AI in its proper role — as an assistant, not an overlord — and ensures your codebase stays clean and maintainable.
OpenAI’s agent mode as the crafter
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