Kagi employee here. The search business is sustainable, we don't need a push in AI things to make it work. We're looking at these features/ideas because we think they complement search well, not because we need them from a cashflow perspective :)
Heya, I work at Kagi, and I did the math for this. Our unit economics are sound, and we don't plan on subsiding usage with ties-attached money. We have abuse prevention mechanisms, and we regularly review heavy use use-cases so we can either optimize for them or offer alternative workflows.
Heya, I work at Kagi. This sounds like a great idea, thanks for the input! We'll put it on the requested features list (which our users contribute to).
If you think we should word the privacy policy differently, please do submit some feedback on kagifeedback.org with the specifics - we have changed it in the past through exactly this process. It has been written by engineers, mostly for engineers at the beginning. I'm sure we can improve the wording to make it more binding, we're not trying to squirrel away from it. If you have enough legal knowledge to harden it, we'd welcome the contribution :)
I work at Kagi. We don't have KPIs that track search quality in that way (we don't have any frontend tracking at all, so we can't know if you click any of the results), and we haven't touched much the sources of data we're using over the last few months. We've also had conflicting reports about this problem, so I'm wondering if it's not variable quality over time and place of our upstreams that's a problem.
That said, we're aware that it's currently something we have to improve, "!g best cafe" is not where we want to be. We're working on it, but if you have specific examples/suggestions, please do submit them to kagifeedback.org so we can track them.
I work at Kagi. I think this is partly a function of who our early user base was, and the tons of feedback they gave which helped improve our search. Because we don't keep the data, we can't do a lot of the ML-powered search improvements that eg. Google could over all search domains.
The good news is as the community expand, so will the feedback, and we'll be able to improve overall. The even better (and quite surprising) news is that we could be better than eg. Google on any topic at all, which tells me that the ML magic is not actually magic, and we will be able to outperform them in other domains as the community grows, the feedback increases, and our code becomes better.
I work at Kagi. I'm as blown away by the recent coverage as you are. We're a 15ish people company, which is staying away from VC money and only raised a relatively small amount of money directly from our users. We don't have the time nor the money to spend on astroturfing. We do have an incredibly supportive community, and I'm sure they help in spreading the word. I think it also help that we have a good product :)
In fact, we even axed our referral bonus program a couple months back to ensure that no third party had anything to gain in promoting Kagi. Recently, that included saying "no" to an independent journalist who explicitly asked for referral bonus for their readers. All you see is organic.
I work at Kagi. This is a very reasonable fear to have. We do actually not store the data, but of course you'd need to take me at my word for this.
That said, if we did lie or change that, we'd be in immediate breach of our privacy policy (https://kagi.com/privacy), and as a result be a very easy target for a lawsuit. Given that we're intentionally not VC backed, between the horrible press this would be and the actual costs of fighting such a lawsuit, I expect not much would be left of Kagi afterwards. We are liable to users, in a pretty existential way.
Heya, I work at Kagi. This is correct, we do not personalize searches other than by respecting the user's customizations (eg. domain preferences, lenses, etc...) which are all entirely user-controlled.
Hey daveoc64. I work at Kagi. Really cool stuff is coming for Ultimate, and being a early Ultimate user you'll get it before the newer users. If that doesn't work for you (fair enough) and you switch to pro, we'll prorate all your credits. If that also doesn't work, contact [email protected] and we'll help.
Possibly yes - I think that's my point with predicting peak oil wrong for 50 years. Still, right now it seems every time OpenAI/someone else adds a new content filter, someone figures out a prompt escape that works.
I'm not too worried about GPTs trained on GPTs, maybe that's an LLM analogy to AlphaGo playing itself a lot to learn how to play go. I'm more worried about people specifically trying to get into the training corpus with biased/wrong/misleading/security-risk content.
(author here) How do you know what's a prompt injection vs actual content? If you train another LLM to tell you what's a prompt injection, how do you know it has 100% coverage of all possible injections? OpenAI has been battling people trying to bypass their prompt re-write filter, and as far as I can see, not really winning, just constantly adding stuff to their blocklist until the next thing gets discovered.
Agreed, and I mentioned that solution in the article, but I'm not so convinced this is true. It reads a bit like the "if you're a great programmer, the lack of memory safety of C isn't a problem!" argument. In theory sure, but in practice it seems CVEs keep on popping up.
(Author here) that's what I thought originally, but then it means that LLMs never get to learn from new content - current ones stop in 2021, they don't know that Russia invades Ukraine, or that Arc is a cool browser or the API of any libraries released after their end date (which has been an issue for me for code generation using fast moving libraries). I don't think it's good enough to stop acquiring new content.