I have been using it for about a year. There is zero advertisement anywhere. No sponsored posts. Just pure search. And they give you quite some good tools to derank or delist websites for you to define your own quality of search results, it's very effective. They also go the extra mile of de-ranking websites with many trackers as that apparently correlates with SEO which normally doesn't track with result quality. It's a very refreshing take on the web. I love it :)

Edit: they also have an extension which allows you to search while being logged out using tokens which improves privacy, in case you want to go the extra mile.

I think so! I'm pretty sure they still give you 100 free searches to try it out, might be worth test driving before you pay for it. From what I understand it's mostly Google on the back end with Kagi's own algorithm. My favorite part about it is being able to rank how the results sort themselves based on sites you want to see more or less of, and some other pretty powerful filters. I definitely think it's worth a shot if you're curious.

Vanilla guess: WP = Winnie the Pooh.

WP interpreted as "Winnie the Pooh" instead of "Wikipedia"?

Though, a big catch is that whatever is generated needs to be verified. The most recent story I've seen was the AI proposing the hypothesis of a particular drug increasing antigen presentation, which could turn cold tumors (those the immune system does not attack) into hot tumors (those the immune system does attack). The key news here is that this hypothesis was found to be correct, as an experiment has shown that said drug does have this effect. (link to Google's press release)

The catch here is that I have not seen any info on how many hypotheses were generated to find this correct hypothesis. It doesn't have to be perfect: research often causes a hypothesis to be rejected, even if proposed by a person rather than AI. However signal-to-noise is still important for how game changing it will be. Like in this blogpost it can fail to identify a solution at all, or even return incorrect hypotheses. You can't simply use this data for further training the LLM, as it would only degrade the performance.

There needs to be a verification and filtering first. Wikipedia has played such a role for a very long time, where editors reference sources, and verify the trustworthiness of these sources. If Wikipedia goes under because of this, either due to a lack of funding or due to a lack of editors, a very important source will be lost.