Is Show HN dead? No, but it's drowning

Original Article ↗ Hacker News Discussion ↗

Perceived Decline of Show HN

  • Many see Show HN as “drowning” in volume, with far more posts stuck at 1 point and fewer standout discussions.
  • Users describe a sharp drop in signal-to-noise: more shallow or repetitive tools (LLM wrappers, social-media utilities, generic SaaS clones).
  • Timing and randomness still matter a lot: near-identical projects can get wildly different responses depending on when they’re posted and what else is on the front page.

AI, “Vibe Coding,” and Loss of Effort as a Filter

  • A central theme: LLMs and agents have dramatically lowered the effort needed to ship something that looks finished.
  • Previously, effort acted as a de facto filter: spending weeks/months/years on a project implied deep engagement with the problem.
  • Now many Show HNs are seen as “vibe coded” – quickly assembled by prompting, with authors unable to explain or defend core design/implementation choices.
  • Some distinguish:
    • AI-as-tool used by experts who still understand the system.
    • AI-as-substitute where the author has no mental model and can’t own the work.

Impact on Community Value

  • Several posters say the best part of old Show HN was learning from people who’d thought hard about a niche problem; that’s rarer when posts are shallow.
  • Repeated experiences: viral Show HN ≠ product success; conversely, some projects that flopped on HN later made substantial revenue or large user bases elsewhere.
  • HN’s tastes (OSS, technical depth, no-signup demos) are seen as unrepresentative of broader markets.

Proposed Fixes and Structural Ideas

  • Separate categories: “Vibe HN,” “Slop HN,” “Show AI,” or explicit [NOAI]/[HUMAN] tags.
  • Gating Show HN: require account age, karma, or prior thoughtful comments; or a “review queue” where experienced users help shape submissions and vouch them out.
  • Cultural norms: normalize flagging low-effort posts, discourage AI-written descriptions/comments, and emphasize explaining why the project exists and what it does.
  • Alternative venues: more use of “What are you working on?” threads and other platforms (blogs, Fediverse) for discovery and discussion.

Deeper Concerns and Counterpoints

  • Broader worry: AI-generated “slop” is flooding not just HN but GitHub, Reddit, books, and media, breaking old filtering mechanisms and pushing us toward reputation and curation.
  • Some foresee LLMs training on their own output and degrading over time; others propose “poisoning” AI training data as resistance.
  • A minority push back: more people building more things is inherently good; Show HN isn’t “dead,” just busier and more democratic, and effort/quality can still shine through with better curation rather than AI bans.