Kagi Search, human connection, and why LLMs are not a good search replacement (www.alexselimov.com)

Kagi Search, a privacy-focused alternative to traditional search engines, has gained attention for enhancing user experience by minimizing irrelevant AI-generated content, or "AI slop." Users report a significant improvement in locating relevant information, especially in niche technical areas, as Kagi promotes access to blog-based solutions. This shift has not only provided better quality search results but has also fostered a more humanistic approach to learning and problem-solving through personal blogs where developers share their insights and experiences. The discussion brings to light the limitations of large language models (LLMs) as search replacements. The author argues that while LLMs can boost efficiency, they often limit users' exposure to broader solutions, potentially leading to a narrow understanding of problems. By relying too heavily on these models, users may sacrifice long-term growth and critical thinking in favor of immediate answers. The article advocates for traditional search methods, emphasizing the value of exploring diverse sources to discover innovative solutions and gain a comprehensive understanding of technical challenges, ultimately suggesting that tools like Kagi can better support meaningful learning and human connection in the digital space.