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Strawman Mockery

Creating a fictional naive person to mock rather than engaging with actual participants in the discussion.

  • '"But I bought it!" - naive customer somewhere'
  • "Meanwhile some developer thinks this is revolutionary."
  • "Cue the 'works on my machine' crowd."
  • "I can already hear the 'just use Rust' people."

Why It's Unproductive​

Mocks an imaginary person who isn't part of the discussion instead of engaging with what people are actually saying. It might feel cathartic and others might relate to the frustration, but it shifts the conversation from substance to performative sarcasm. Often stems from genuine frustration with common bad takes, but the mockery doesn't help anyone learn or change their mind.

The Better Move​

Name the actual frustration or pattern you're reacting to. If a bad take is common enough to mock, it's common enough to address directly. Say what the real problem is instead of performing a skit about an imaginary person who holds that view.

Why It's Better​

People engage with observations about a problem. They tune out sarcastic theater aimed at a character who isn't in the room.

Examples​

OP: "Microsoft's cloud integration locked me out of Notepad due to an account bug." Antipattern: "'But I bought it!' - naive customer somewhere" Better: "The gap between 'buying' software and what that legally means trips people up constantly. Losing access to Notepad over an account bug is a good example of why."

OP: "New benchmark shows AI skills don't transfer well across domains." Antipattern: "I'm sure news outlets will report this with appropriate caution and nobody will misunderstand it." Better: "Worth noting they tested self-generated skills, not learned-from-experience ones. That distinction will probably get lost in the coverage."

OP: "Study finds AI agents violate ethical constraints 30-50% of the time when pressured by KPIs." Antipattern: "Cue the 'just regulate it' crowd who have never shipped a product in their lives." Better: "The KPI pressure angle is interesting. That's a deployment design problem more than a model problem, and it's probably fixable."