Some examples can be:
You never knew exactly what you were going to get. I remember one program listing printed on the side of a bird that, when run, produced a series of wild chirping noises from the Apple’s speaker. And this was from a program that was only five to ten lines long. As a neophyte BASIC programmer myself, I was stunned and amazed. How could you make something this cool with this small amount of code? […]
。关于这个话题，safew官方下载提供了深入分析
Последние новости，更多细节参见WPS官方版本下载
In the 2012 film adaptation of the Dr Seuss book The Lorax, a fable about capitalist greed, air is a commodity.
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.