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I mostly agree with this. Part of the confusion with the discourse around AI is the fact that "software engineering" can refer to tons of different things. A Ne
by bloppe 7mo ago
I mostly agree with this. Part of the confusion with the discourse around AI is the fact that "software engineering" can refer to tons of different things. A Next.js app is pretty different from a Kubernetes operator, which is pretty different from a compiler, etc.
I've worked on a project that went over the complexity cliff before LLM coding even existed. It can get pretty hairy when you already have well-established customers with long-term use-cases that absolutely cannot be broken, but their use-cases are supported by a Gordian Knot of tech debt that practically cannot be improved without breaking something. It's not about a single bug that an LLM (or human) might introduce. It's about a complete breakdown in velocity and/or reliability, but the product is very mature and still makes money; so abandoning it and starting over is not considered realistic. Eager uptake of tech debt helped fuel the product's rise to popularity, but ultimately turned it into a dead end. It's a tough balancing act. I think a lot of LLM-generated platforms will fall eventually into this trap, but it will take many years.
- 9rx 6mo ago> It can get pretty hairy when you already have well-established customers with long-term use-cases that absolutely cannot be broken LLMs are often poor at writing tests that provide useful information to human readers and poor at writing tests that can survive project evolution. To be fair, humans are also poor at these tasks if done in hindsight, after all the information you normally want to capture in tests has been forgotten. That boat has been missed for the legacy code no matter how you slice it. But LLMs are quite good at writing tests that lock in existing functionality in the rawest way. It seems like LLM-generation is actually the best hope of saving such a project?