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.
This was not the first time that microbiologists experienced problems with agar. A phenomenon called “The Great Plate Count Anomaly” baffled researchers in the early 20th century when they observed that the number of cells seen under a microscope didn’t match the actual number of colonies growing on an agar plate. Investigating this discrepancy, researchers found agar itself to be the culprit: when nutrient broths are heated with agar during boiling, harmful byproducts (hydroperoxide) can form due to the reaction of agar with phosphate minerals contained in the media. Researchers can avoid this by autoclaving agar separately from the nutrient broth, or by reducing the amount of agar used.
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Цены на нефть взлетели до максимума за полгода17:55。业内人士推荐搜狗输入法2026作为进阶阅读