| Introducing Iterative Code Generation: increase the success rate of your vibe coding! One big problem with AI code generation today is it ‘throws the baby out with the bathwater’ and doesn’t work like software engineers actually write code. Iterative Code Generation actually increases the success rate of AI code generation/Vibe coding, and mitigates the "generate and pray" problem. ## Why current AI code generation falls short: Most LLM coding assistants today generate complete solutions in one pass and hope they work. But programming isn't a one-shot process — it's iterative debugging and refinement. When code inevitably fails, users face a frustrating cycle of:
1. Getting an error message
2. Manually interpreting what went wrong
3. Asking the AI for a fix
4. Repeating until it works... or giving up ## The iterative execution approach: Instead, this new approach writes code iteratively like real software engineers: 1. Generates a complete solution first
2. Visually executes it line-by-line (like a debugger)
3. Intelligently identifies real errors vs. expected partial execution artifacts
4. Automatically feeds errors back to the AI for targeted fixes
5. Shows the reasoning behind each fix The results? More robust, reliable code solutions with dramatically fewer iterations. ## Why this matters: *For developers/Vibe coders:* This approach mirrors how we actually write code — incrementally with constant feedback loops. *For AI systems:* It creates a tighter execution/feedback loop for models to learn from their mistakes in context. Go from 1 bit resolution in data to N bit, which should also improve training data and code generation models themselves with more valuable data. This plus the idea of direct 'latent space languages' should unlock previously impossible software across the solution space that we couldn't have imagined before. |