Show HN: I built an interactive HN Simulator

Overall Reaction and Use Cases

• Many find the simulator hilarious, addictive, and uncannily on‑brand for HN; several call it one of the most memorable threads in a long time.
• People are already using it to “sanity check” Show HN posts, startups, blog posts, and even code, treating it as a cheap proxy for real HN feedback.
• Beyond novelty, some see it as a general “idea sounding board” and a pattern for products that simulate community reactions or help “fake it till you make it.”

Realism, Archetypes, and Tone

• The archetypes and moods are widely praised as “too accurate”: pedantic, condescending, nitpicky, “Ah, yes” / “oh great” openings, economist‑style overanalysis, etc.
• Users highlight how well it captures specific HN tropes: curl‑vs‑wget arguments, “this was done in the 80s,” “isn’t this just X with a pretty UI,” Linux distro cynicism, gripes about titles, and meta-complaining about posts.
• Some say they’d struggle to distinguish it from real HN; others note “something feels off,” citing overly uniform comment lengths, lack of one‑liners, few personal anecdotes, and less tangential wandering than real threads.
• Multiple users report a sense of “signal contamination”: after reading the simulator, real HN feels like more of the same generated archetypes.

Meta, Spam, and Moderation

• Users quickly push it to extremes: porn links, slurs, violent fantasies, bestiality AMAs, and 4chan‑style shitposting appear; several call this disturbing and urge better guardrails and censorship.
• There’s concern that anonymous submissions plus no moderation effectively create an unmoderated chatroom; others see the vandalism as “harmless fun.”
• Someone scripts spam to force an IP cooldown; the author responds with rapid fixes and acknowledges missing rate‑limits and moderation.
• Many note that while it simulates HN’s attitude, it omits HN’s moderation (“dang”) and voting dynamics; suggestions include simulated downvotes, hidden gray comments, archetypes like “title is wrong,” and arguments about systemd/Rust.

Technical and Implementation Notes

• Users like being able to inspect the prompt/model per comment and suggest labeling by archetype + model.
• The author describes iterating archetypes, moods, and “shapes” with multiple models to reach ~“90% accuracy,” and plans further tuning.