Drag took your advice, and it only gave drag the answer drag wanted. It wasn’t as good at figuring out which question drag was asking, but it made up for that by not wasting half the screen real estate on other answers. Ecosia beats Google at answering drag’s question today.
sorry, can’t let this post go without calling out the greenwashing ecosia that has a chatgpt integration while they only report their own energy stats with a limp “it’s too early to tell uwu” when it comes to openai as a provider
I’m super happy with Kagi search. Even the AI summary is quite good as it’s based on the search results, not just made up from training data. Of course, it’s still a stupid LLM, so double check everthing. But i find it quite useful to get a grasp on the overall content of the results.
And search itself works well, haven’t had a moment where it was worse than one of the big providers. The dedicated forum, programming and other serchas are cool and i love being able to adjust the priority of pages or even blacklist them.
love when a search engine has a random guy who knows nothing message me some bullshit he made up about a search. of course, you have to double check the bullshit. but it’s a great feature
Look, AI/LLMs are the scourge of the internet and I wish the bubble would pop already. Heck I downvote people using AI to answer questions online myself.
But there is a qualitative difference between a plain LLM being forced down your throat, hallucinating left and right based on outdated training data, and the RAG that Kagi uses.
First of all, it’s not rammed down your throat, you chose if you want it by appending a question mark to the end of your query, the default is to not show any AI content.
Second, it being RAG, the generation is is based on real documents fetched from regular search and it has actual citation links to which page the information came from (these are not hallucinated but based on the search results). If you’d bothered to read my post you’d have seen me mentioning that you still can’t trust it’s output (it is still LLM technology and makes shit up), but it does work really well as an initial filter on which of the search results might be relevant to your query, and then actually read those pages by picking the one that fits what you actually want the most based on the summary.
I don’t usually turn this on for regular searches, but for technical programming things it is helpful, especially when searching for things where there’s little information. There’s really two cases, sometimes there’s 5 different ways of solving something and it will enumerate them with a short summary, making it faster to know which stack overflow or blog post to read for a likely solution.
The other, much more useful scenario imo is for those problems where there’s little information. For instance I’m currently building a bluetooth touchpad to attack to my keyboard. For this, I need to specify USB HID usages and usages pages so the OS properly picks up the device. Bluetooth touchpads are almost non-existent, especially DIY ones, so there’s not really any information on them out there. So I’ll do a search like “bluetooth hid usage for a touchpad?” and I’m immediately faced with bland, generic LLM garbage not relevant to my problem:
This immediately tells me that my query isn’t specific enough, that none of the top results contain relevant information and that I should try again. I didn’t have to waste time wading through the results.
So I do another search, a bit more specific and get
That looks more like what I’m looking for. Notice though, that the result is wrong in this case. 0x0A is not Generic Desktop, 0x01 is. I picked this specifically because it’s one of the recent examples where the output was just wrong. But I don’t care about the AI summary itself. But what it says tells me immediately that the actual search results themselves are much more relevant to what I’m looking for, and the two links it cites are actually relevant to my query, they’re documentation of bluetooth hid profiles. In the search results themselves, these are results 4 and 5. So I read those, and it takes some more queries, to realize there just isn’t a specific code for HID touchpads, they’re just generic pointer devices.
So did the AI answer my question? No. Does is sometimes answer my question, sure, but I still need to double check. Does it allow me to iterate on my searches faster and to guess if the answer is within the top 5 results? absolutely.
It has been for a while. Never a better time to switch to something different
Drag took your advice, and it only gave drag the answer drag wanted. It wasn’t as good at figuring out which question drag was asking, but it made up for that by not wasting half the screen real estate on other answers. Ecosia beats Google at answering drag’s question today.
sorry, can’t let this post go without calling out the greenwashing ecosia that has a chatgpt integration while they only report their own energy stats with a limp “it’s too early to tell uwu” when it comes to openai as a provider
Valid. Drag refused to look at any ads anyways, because fuck ads. Do you have a more ethical suggestion?
Multiple options available depending on what you want. Duckduckgo for Bing results. Startpage for Google and Bing.
I’ve been quite happy with Startpage for a while now and there are multiple options depending on just how badly you want to avoid Google.
I’m super happy with Kagi search. Even the AI summary is quite good as it’s based on the search results, not just made up from training data. Of course, it’s still a stupid LLM, so double check everthing. But i find it quite useful to get a grasp on the overall content of the results.
And search itself works well, haven’t had a moment where it was worse than one of the big providers. The dedicated forum, programming and other serchas are cool and i love being able to adjust the priority of pages or even blacklist them.
If you liked Wordpress Matt wearing a hat, then you’ll love Kagi Guy in your replies.
love when a search engine has a random guy who knows nothing message me some bullshit he made up about a search. of course, you have to double check the bullshit. but it’s a great feature
I wonder how long until these clowns start posting kagi signups with referral links embedded
I’m always surprised that’s not a factor, given how fervent the posts are
Tired: Rust Evangelism Strike Force
Wired: Kagi Jihadi
not gonna give any money to a startup whose weird fucking founder thinks their primary product is AI rather than search, thanks
insta-block
Look, AI/LLMs are the scourge of the internet and I wish the bubble would pop already. Heck I downvote people using AI to answer questions online myself.
But there is a qualitative difference between a plain LLM being forced down your throat, hallucinating left and right based on outdated training data, and the RAG that Kagi uses.
First of all, it’s not rammed down your throat, you chose if you want it by appending a question mark to the end of your query, the default is to not show any AI content.
Second, it being RAG, the generation is is based on real documents fetched from regular search and it has actual citation links to which page the information came from (these are not hallucinated but based on the search results). If you’d bothered to read my post you’d have seen me mentioning that you still can’t trust it’s output (it is still LLM technology and makes shit up), but it does work really well as an initial filter on which of the search results might be relevant to your query, and then actually read those pages by picking the one that fits what you actually want the most based on the summary.
I don’t usually turn this on for regular searches, but for technical programming things it is helpful, especially when searching for things where there’s little information. There’s really two cases, sometimes there’s 5 different ways of solving something and it will enumerate them with a short summary, making it faster to know which stack overflow or blog post to read for a likely solution.
The other, much more useful scenario imo is for those problems where there’s little information. For instance I’m currently building a bluetooth touchpad to attack to my keyboard. For this, I need to specify USB HID usages and usages pages so the OS properly picks up the device. Bluetooth touchpads are almost non-existent, especially DIY ones, so there’s not really any information on them out there. So I’ll do a search like “bluetooth hid usage for a touchpad?” and I’m immediately faced with bland, generic LLM garbage not relevant to my problem:
This immediately tells me that my query isn’t specific enough, that none of the top results contain relevant information and that I should try again. I didn’t have to waste time wading through the results.
So I do another search, a bit more specific and get
That looks more like what I’m looking for. Notice though, that the result is wrong in this case.
0x0Ais notGeneric Desktop,0x01is. I picked this specifically because it’s one of the recent examples where the output was just wrong. But I don’t care about the AI summary itself. But what it says tells me immediately that the actual search results themselves are much more relevant to what I’m looking for, and the two links it cites are actually relevant to my query, they’re documentation of bluetooth hid profiles. In the search results themselves, these are results 4 and 5. So I read those, and it takes some more queries, to realize there just isn’t a specific code for HID touchpads, they’re just generic pointer devices.So did the AI answer my question? No. Does is sometimes answer my question, sure, but I still need to double check. Does it allow me to iterate on my searches faster and to guess if the answer is within the top 5 results? absolutely.
Oh, I will have to try them out! Thank you!