This is great! Thanks for sharing this and discussing all the lessons learned. Amazing that you were able to pull this off in ~4 days and I appreciate your discussions on trade-offs, cheap vs. smart llms. Incredible!
AI’s limitations in engineering often mirror human ones. Trained on human-written code, models get tripped up by the same things that slow down good engineers—unclear abstractions, excessive context, and accumulated tech debt. Optimizing a codebase for AI is really just applying the same practices that make it easier for humans to work in, and those investments improve maintainability for both.
This is one of the most insightful and grounded takes I’ve read on AI in software engineering. Totally agree that the real value isn’t replacing engineers, but reshaping how we work with smarter frameworks. Thanks for sharing this!
Thank you for the insights! You captured the key points perfectly.
This is great! Thanks for sharing this and discussing all the lessons learned. Amazing that you were able to pull this off in ~4 days and I appreciate your discussions on trade-offs, cheap vs. smart llms. Incredible!
AI’s limitations in engineering often mirror human ones. Trained on human-written code, models get tripped up by the same things that slow down good engineers—unclear abstractions, excessive context, and accumulated tech debt. Optimizing a codebase for AI is really just applying the same practices that make it easier for humans to work in, and those investments improve maintainability for both.
This is one of the most insightful and grounded takes I’ve read on AI in software engineering. Totally agree that the real value isn’t replacing engineers, but reshaping how we work with smarter frameworks. Thanks for sharing this!
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