Let's properly analyze an AI article for once

Overall reaction to the blog post

• Many commenters found the critique of the GitHub CEO’s article sharp, funny, and overdue, especially around inflated claims about AI and developers.
• Others argued the author undercut their own credibility with weak statistics and at least one apparent misquote about Miyazaki, accusing the piece of its own form of “hallucination.”

CS fundamentals vs “Baby’s First LLM”

• Strong defense of traditional CS fundamentals: data structures, algorithms, performance, security, reliability, systems understanding.
• Several argue AI makes fundamentals more important, because weak engineers using LLMs can create huge volumes of bad code and become team liabilities.
• Pushback: much day‑to‑day work is CRUD/JSON API plumbing; people question whether deep CS is necessary for the majority of jobs.

Whiteboard interviews and hiring

• One view: whiteboard problems train and test general problem‑solving and communication, not literal job tasks, and correlate positively (if imperfectly) with job performance.
• Counterview: they function as hazing, filter out anxious candidates, and lack transparent, scientific validation; proprietary internal data is distrusted.
• Shared sentiment: whiteboard alone is insufficient; real hiring should also test actual programming and debugging.

Academia vs vocational skills, bootcamps, and “software as a trade”

• Ongoing tension between CS as a science vs software development as a trade:
  • Some say universities should focus on theory; tooling (git, Docker, CI, SSH, IAM, CSS quirks, etc.) is vocational and belongs in on‑the‑job training or separate programs.
  • Others insist basic tooling and collaborative workflows are “day 1” essentials and academia systematically fails students by ignoring them.
• Bootcamps: praised for producing teachable, lower‑cost “front‑end/trade” workers; criticized as creating “assembly line” coders with no path up.
• Several propose clearer separation: CS (science) vs Software Engineering / trade‑school style programs.

Math and statistics disputes

• Commenters challenge the blog’s sample‑size criticism: sample size depends mainly on desired confidence/margin of error and effect size, not population size; 22 can still be informative but with large error.
• Some see the whole GitHub piece as pure marketing, not worth serious statistical analysis.
• Side debate on calculus vs statistics in CS curricula: some argue stats is more practically useful; others reply stats itself rests on calculus and both are widely applicable.

AI’s “plausible nonsense,” metrics, and technical debt

• Multiple comments resonate with the idea that LLM marketing prioritizes “somewhat plausible” output and quantity (e.g., “% of lines generated by AI”) over correctness or value.
• People note that AI‑generated code can inflate line counts while providing zero or negative productivity if developers must debug broken generations.
• Concern that “vibe coding” plus weak fundamentals will massively increase technical debt, creating future demand for consultants and debuggers.

AI art, misquotes, and honesty in representation

• Several criticize AI food and marketing images as dishonest, contrasting them with traditional “enhanced but real” food photography; others note food imagery has always been heavily faked.
• The Miyazaki quote is flagged as misused: commenters provide context that he criticized a specific grotesque AI demo as an “insult to life,” not AI art in general.

Broader skepticism about AI hype and propaganda

• Strong sentiment that CEO/AI‑vendor content is sales copy aimed at executives and investors, not developers.
• Some see continuity with prior hype waves (crypto, “FOMO capitalism”), where truth and rigor matter less than narratives that justify valuations and control.