I used the Kagi free trial when I was doing Advent of Code in a somewhat unfamiliar language (Swift) last year, as well as ChatGPT occasionally.
The LLM was obviously much faster and the information was much higher density, but it had quite literally about a 20% rate of just making up APIs from my limited experiment. But I was very impressed with Kagi’s results and ended up signing up, now using it as my primary search engine.
It is really a double edged sword. Some APIs I would not have found myself. In some way an AI works like my mind fuzzy associating memory fragements: There should be an option for this command to do X because similar commands have this option and it would be possible to provide this option. But in reality the library is less than perfectly engineered and the option is not there. The AI also guesses the option is there. But I do not need a guess when I ask the AI - I need reliable facts. If the cost of an error is not high I still ask the AI and if it fails it is back to RTFM but if the cost of failure is high then everything that comes out of a LLM needs checking.
The LLM was obviously much faster and the information was much higher density, but it had quite literally about a 20% rate of just making up APIs from my limited experiment. But I was very impressed with Kagi’s results and ended up signing up, now using it as my primary search engine.