When someone says they hate your product

Handling Negative Feedback

• Many commenters endorse the article’s core advice: don’t take criticism personally, listen, and look for the actionable part of the complaint.
• A recurring theme: “feedback is a gift.” Complaints signal that people see potential and are frustrated the product isn’t delivering. Indifference is worse than hate.
• Several argue that the correct public response to harsh reviews is almost always some form of apology, acknowledgment, and offer to help; arguing back looks unprofessional and scares off other customers.
• One tactic highlighted: extract the invariant (“workflow is brittle,” “pricing feels dishonest”) from the theatrics, address only that, and possibly ask a specific follow-up.

Haters, Users, and Signal vs Noise

• Stories: angry users often become valuable contributors and advocates if they feel heard, but there are also “pathological” haters who cannot be satisfied and should be disengaged from.
• Suggestions to distinguish:
  • Frustrated user (fixable issues)
  • Casual troll (for laughs)
  • Malicious hater (bad-faith, community-poisoning)
• Warnings that optimizing for loud complainers can harm the broader user base; haters are rarely representative.
• Some see “squeaky wheel gets the grease” as a bad incentive structure that trains people to scream.

The CodeRabbit Incident and Apology

• Many view the CEO’s original defensive response and subsequent “apology” as poor examples of leadership: framed as protecting the team, reasserting user numbers, and subtly blaming the critic.
• Others note the critic’s tone was dickish but still argue the power imbalance means the company must hold itself to a higher standard.
• Several readers say this episode alone is enough to avoid the product, and compare it to monopolistic products widely hated yet entrenched.

Broader Reflections

• Negative feedback can fuel innovation if you use frustration as a diagnostic, not a personal attack.
• Public replies should be written for the observing audience, not to “win” against the critic.
• Discussion touches on generational shifts away from “customer is always right,” the burden of mandatory workplace tools, and discomfort with calling people “users” instead of “customers” or “people.”

AI Anthropomorphism Tangent

• Some push back on phrases like “Claude gets it,” insisting LLMs don’t “understand” or “think” and that anthropomorphizing them is misleading.
• Others counter that we lack clear definitions of “thinking” and cannot easily prove or disprove machine understanding.