Ask HN: Do you still use search engines?

Hacker News Discussion ↗

Growing Use of LLMs as “Search”

  • Many respondents now default to ChatGPT/Claude/Perplexity/Gemini for open-ended questions, exploration, or “rubber-duck” clarification.
  • Common use: describe a fuzzy problem, get terminology, then use a traditional search engine with those better keywords.
  • Programming help, CLI usage, config snippets, language/tech explanations, travel ideas, and summarizing long documents are frequent LLM use cases.
  • Some use Kagi’s “?” or similar AI modes to get an instant summary plus links; others use AI just to generate concise recipes, checklists, or how‑to steps.

Where Traditional Search Still Dominates

  • Directly finding a specific site, official documentation, APIs, and authoritative specs, papers, or legal/regulatory texts.
  • Local queries: businesses, maps, events, shopping, product reviews, and vendor comparisons.
  • Image/video/search within domains (YouTube, Reddit, Stack Overflow, GitHub, etc.).
  • Retrospective or niche factual research where citation chains and provenance matter.

Trust, Verification, and Hallucinations

  • Strong skepticism toward LLMs for factual or high‑stakes queries (health, law, finance, history). Many insist on reading original sources.
  • Recurrent complaints: made‑up APIs, deprecated solutions, wrong technical details, fabricated citations, and brand‑polished but misleading answers.
  • Some see LLMs as “convenient but shallow”, “people‑pleasing”, or biased toward positivity; others say they’re fine for low‑risk or easily verifiable tasks (code you can compile, recipes, small math, translations).

Perceived Decline of Search Quality

  • Widespread frustration with Google’s ads, SEO spam, AI “blobs” atop results, and weakened query semantics (“-B”, exact matches, date filters, etc.).
  • Many report switching to Kagi, DuckDuckGo, Brave, Startpage, Qwant, Searxng, or self‑hosted meta-search, often paying for Kagi.
  • Several note that LLMs can sometimes be less hallucination‑prone than wading through AI‑generated SEO slop in the web results.

Hybrid Patterns and Future Worries

  • A common pattern: use LLMs to clarify and narrow, then search engines to verify and go deep.
  • Some actively avoid AI in search, seeing black‑box summarization as “spoon‑feeding” that erodes skills and hides context.
  • Many anticipate increasing ad/paid influence inside LLM answers and fear a future where both web search and AI are polluted and untrustworthy.