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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Arbitrary rejection or suspension without clear justification
“手搓经济”的快速发展是市场创新活力的体现，但这一新兴业态仍面临现实挑战。生成式AI降低开发门槛的同时，也使得复制产品的成本极低，市场上已开始出现同质化“僵尸应用”。同时，AI辅助下的创作成果如何界定与保护产权等问题，仍缺乏明确规则。个人开发者在运营、维权等方面能力相对薄弱，也令产品的持续发展基础不够稳固。