Yungblud has always wanted to take his festival, which started in Milton Keynes, abroad
接下来便是炒制。将余下的柏树灰倒入大锅，燃火，把灰烧热，放入在灰堆里睡饱了的灰豆腐，慢慢翻炒。我曾见过母亲炒制灰豆腐。锅铲在她手里，就像一条乌鱼在柏树灰与豆腐之间穿梭。伴随着此起彼伏的“噗噗”声响，豆腐在滚烫的柏树灰中逐渐鼓胀、圆润，方正紧实的豆腐块不一会儿就变成肥嘟嘟糯叽叽的豆腐果了。灰豆腐炒制完成，母亲的头上、肩上，也落满了细细的柏树灰。
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Fifth, join one or two communities where your target audience discusses topics related to your content. You don't need to be everywhere—pick platforms where you can genuinely contribute value and commit to participating regularly. Start by reading and understanding the community culture before posting, then gradually engage in discussions where your expertise adds value.
For example, if you're comparing different software tools, create an actual comparison table with columns for features, pricing, pros, and cons rather than describing each tool in paragraph form. If you're explaining a multi-step process, number the steps and use consistent formatting for each. If you're providing examples, use a predictable structure where each example follows the same pattern.
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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.