Nuke Insta Slop
Жители Санкт-Петербурга устроили «крысогон»17:52
。关于这个话题，51吃瓜提供了深入分析
Some programming languages, like Rust and Zig, classify many errors as expected. Others, like JavaScript and Python, classify them as unexpected. For example, when you parse JSON in Go, the compiler makes you handle the error; not so in Ruby. I tend to prefer stricter compilers for production software and looser languages for scripts and prototypes, in part because of their philosophy about errors. (The Rustaceans among you probably notice that this whole post is very similar to Rust’s error philosophy.)
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.
，详情可参考体育直播
developers (and other contributors) speak. Of course this is still seen。关于这个话题，夫子提供了深入分析
Follow topics & set alerts with myFT