Hacker News.love – 22 projects Hacker News didn't love

Original Article ↗ Hacker News Discussion ↗

Site UX and Presentation

  • Many found the site nearly unusable: scroll-jacking/autoscroll, snap-to-sections, and full-page clickable areas made it hard to read blurbs or open original HN links.
  • Criticism focused on “hijacking scroll” being especially bad on mobile and even on desktop, with users unable to stop between sections.
  • Some liked the mobile UX and aesthetics, but they were a minority voice.
  • Several noted small quality issues (default favicon, awkward light/dark toggle) as reinforcing a “low-effort” or rushed feel.

Cherry-Picking, Nuance, and Survivorship Bias

  • A major theme: the site is accused of cherry‑picking negative comments and presenting them as “HN didn’t love X,” while ignoring positive or nuanced replies in the same threads.
  • Commenters stressed that any sufficiently large discussion will contain both praise and skepticism; you can build the opposite narrative just as easily.
  • Multiple people called out survivorship bias: only successful outliers are shown, not the many similar ideas HN disliked that actually failed.
  • Some asked for an “inverse list” of heavily praised HN darlings that went nowhere.

Definition of Success and “VC Lens”

  • Many objected that the site equates success with valuation, acquisition, or funding, ignoring social costs or long‑term impact.
  • Critiques of Uber, Airbnb, Bitcoin, LLMs, and React are seen as still valid even if those projects are now large or profitable.
  • Some argue the page reads like a venture‑capital narrative: money made is treated as proof that early criticism was wrong. Others counter that markets can reward flawed or harmful products.

Specific Technologies and Products

  • Tailwind and React: several say early HN criticisms remain accurate despite widespread adoption; popularity doesn’t prove technical or UX merit.
  • DuckDuckGo: debate over whether the name hurt adoption; some think it’s silly and non‑verbable, others see it as no worse than “Google.”
  • LLM tools and OpenClaw: many feel it’s too early to treat them as settled “wins”; early negative comments about quality and hype are described as still valid.

AI / Automation Concerns

  • Several suspect the summaries/outcomes were generated by an LLM: repetitive style, oversimplified narratives, occasional factual overreach (e.g., Warp description later corrected).
  • This contributes to a sense that the whole thing is a snarky, low‑nuance “HN was wrong” piece rather than a thoughtful retrospective.