Kagi Update: AI Image Filter for Search Results

Original Article ↗ Hacker News Discussion ↗

Overall sentiment on Kagi as a search engine

  • Many commenters are enthusiastic users, calling it the best current alternative to ad-driven search and “like Google from 10 years ago.”
  • Key value: fewer junk/SEO results, no ads, domain blocking/downranking, and customization; some say it’s the only engine that reliably finds obscure technical posts.
  • Others see only marginal improvement over DDG/Google and don’t feel it justifies the ~$10/month subscription, especially for light or casual search use.
  • Some users cancelled due to cost or being between jobs, but miss it and consider resubscribing.
  • A subset worries about all searches being tied to one login and about future shifts in business model.

Local and maps search limitations

  • Several note weak performance for local queries (restaurants, services, maps/routing) and often fall back to Google (!g).
  • There is mention of optional location sharing, but discoverability and granularity (beyond country-level) are unclear and possibly incomplete.

Search sources and ecosystem

  • Kagi is said to aggregate results from multiple providers (e.g., Bing, Brave, Mojeek, possibly Google).
  • Some dislike any association with Brave due to its crypto/ads angle; others frame it as a mere API integration, not a deep partnership.
  • Mojeek’s role in powering some “organic” results is noted and praised by a few.

Image search baseline quality

  • Multiple users say image search is Kagi’s weakest area: poor relevance for specific queries, filters (especially minus-filters) not respected, and mediocre reverse image search for source-finding.
  • Others report substantial improvement over the last year, or find Google Images worse due to indirection and UX.
  • Feedback flow via kagifeedback.org exists but is perceived by some as slow or fragmented; separate login is a minor annoyance.

Reactions to the AI image filter

  • Strong interest from people seeking drawing/photo references and wanting to avoid “AI slop” overwhelming results.
  • The current approach downranks domains with lots of AI imagery rather than analyzing each image; commenters highlight this as a major limitation, especially for mixed UGC sites (Reddit, social media, stock sites).
  • The “baby peacock” example shows that AI images replicated in legitimate articles still slip through; several note this as evidence of how hard cleanup will be.
  • Some consider the feature “dumb” and argue images should be judged purely visually; others counter that for realism, factual reference, or valuing human effort, knowing an image is AI vs real is crucial.
  • There is demand for both modes: some want AI images filtered out; others want them prioritized for licensing ease and as prompt inspiration.
  • False positives/negatives and bugs (e.g., include/exclude behavior appearing inverted in one test) are reported; users ask for feedback mechanisms and even reward schemes for corrections.

Broader concerns about AI content and labeling

  • Commenters worry about AI-generated media as a kind of “non-information spill” that contaminates search results over time.
  • Several advocate for legal or normative requirements that AI-generated images include identifiable metadata or watermarks, arguing it would greatly help filtering with limited downsides, though metadata can be stripped.
  • Others question how long AI vs non-AI will remain reliably detectable at all, given model progress and content remixing.

Kagi workflows and perceived value-add

  • Heavy searchers say Kagi’s clean, ad-free result pages, domain blocking, and “fail to find” behavior significantly reduce time spent searching.
  • Liked extras include “summarize this page,” “Small Web” emphasis, and the new AI image filter as part of a broader effort to downrank low-quality content.
  • Some users, despite appreciating the philosophy, still feel no dramatic productivity improvement and remain unconvinced by the paid model.