<em>Perspective</em>: Multi-shot LLMs are useful for literature summaries, but humans should remain in the loop

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We leveraged this same approach here using the same OpenAPI JSON to generate boilerplate C# code for registering and wiring up all endpoints within the serverless codebase. Interfaces are generated for each controller with a function for each action. Functions only need to be implemented as necessary for the systems that are required in the offline game.

Google ann雷电模拟器官方版本下载是该领域的重要参考

It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.

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