The rapid proliferation of AI-generated code is shifting software development from a challenge of mechanics to one of principles and ownership. While LLMs make it cheap to output code, they often produce "AI slop"—low-quality, unverified contributions that have already forced major open-source projects like Tildraw and Curl to restrict external pull requests. This trend towards abstraction and automation frequently hides fragility, as seen when a developer’s Vercel hosting bill spiked from $30 to $2,000 due to an aggressive Meta crawler hitting the site 11 million times. True software excellence now depends on understanding the underlying systems rather than just increasing output. This necessity for deep technical context is mirrored in the 1969 Apollo 11 mission, where an engineer’s intimate knowledge of the computer's priority scheduler prevented a mission abort caused by a faulty radar spamming the system with garbage data.
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