Kagi News

I do live these days with the understanding that pretty much all of my personal info is out there one way or another, a social security number is about as private as a phone number these days.

But instead, Kagi "helpfully" regurgitates the whole story, visits the article once, delivers it to presumably thousands, and it can't even be bothered to display all of the sources it regurgitates unless you click to expand the dropdown. And even then the headline itself is one additional click away, and they straight up don't even display the name of the journalist in the pop-up, just the headline.

Incredibly shitty behaviour from them. And then they have the balls to start their about page with this:

> Why Kagi News? Because news is broken.

This is exactly how I want my news to be. Nothing worse than a headline about a new vaccine breakthrough, followed by a first paragraph that starts with "it was a cold November morning as I arrived in..."

I guess it's a matter of taste, but I prefer it short and to the point

Hmmm. Here I will quote some representative sections from the announcement [1]:

>> News is broken. We all know it, but we’ve somehow accepted it as inevitable. The endless notifications. The clickbait headlines designed to trigger rather than inform, driven by relentless ad monetization. The exhausting cycle of checking multiple apps throughout the day, only to feel more anxious and less informed than when we started. This isn’t what news was supposed to be. We can do better, and create what news should have been all along: pure, essential information that respects your intelligence and time.

>> .. Kagi News operates on a simple principle: understanding the world requires hearing from the world. Every day, our system reads thousands of community curated RSS feeds from publications across different viewpoints and perspectives. We then distill this massive information into one comprehensive daily briefing, while clearly citing sources.

>> .. We strive for diversity and transparency of resources and welcome your contributions to widen perspectives. This multi-source approach helps reveal the full picture beyond any single viewpoint.

>> .. If you’re tired of news that makes you feel worse about the world while teaching you less about it, we invite you to try a different approach with Kagi News, so download it today ...

I don't see any evidence from these selections (nor the announcement as a whole) that their approach states, assumes, or requires a value/fact dichotomy. Additionally, I read various example articles to look for evidence that their information architecture group information along such a dichotomy.

Lastly, to be transparent, I'll state a claim that I find to be true: for many/most statements, it isn't that difficult nor contentious to separate out factual claims from value claims. We don't need to debate the exact percentages or get into the weeds on this unless you think it will be useful.

I will grant this -- which is a different point that what the commenter above made -- when reading various articles from a particular source, it can take effort and analysis to suss out the source's level of intellectual honesty, ulterior motives, and other questions I mention in my sibling comment.

[1]: https://blog.kagi.com/kagi-news

Unfortunately, the above is nearly a cliché at this point. The phrase "value judgment" is insufficient because it occludes some important differences. To name just two that matter; there is a key difference between (1) a moral value judgment; (2) selection & summarization (often intended to improve information density for the intended audience).

For instance, imagine two non-partisan medical newsletters. Even if they have the same moral values (e.g. rooted in the Hippocratic Oath), they might have different assessments of what is more relevant for their audience. One could say both are "biased", but does doing so impart any functional information? I would rather say something like "Newsletter A is compromised of Editorial Board X with such-and-such a track record and is known for careful, long-form articles" or "Newsletter B is a one-person operation known for a prolific stream of hourly coverage." In this example, saying the newsletters differ in framing and intended audience is useful, but calling each "biased in different ways" is a throwaway comment (having low informational content in the Shannonian sense).

Personally, instead of saying "biased" I tend to ask questions like: (a) Who is their intended audience; (b) What attributes and qualities consistently shine through?; (c) How do they make money? (d) Is the publication/source transparent about their approach? (e) What is their track record about accuracy, separating commentary from factual claims, professional integrity, disclosure of conflicts of interest, level of intellectual honesty, epistemic standards, and corrections?

I think Google hates the loss of no/few ads or lame suggestions.

...yes? If I go to a website called "_ News" (present company included), I expect to see either news stories aggregated by humans or news stories written and fact checked by humans. That's why newspapers have fact checking departments, but they're being replaced by something with almost none of the utility and its proponents are framing the benefits of the old system as impossible or impractical.

Either you mean every time you read something interesting (“huh”) you should check it. But in that case, why bother with reading the AI summary in the first place…

Or you mean that any time you read something that sounds wrong, you should check it. But in that case, everything false in the summaries that happens to sound true to you will be confirmed in your mind without you ever checking it.

The main point of my original comment was that I wanted to understand what this is, how it works and whether I can trust the information on there, because it wasn't completely clear to me.

I'm not super up to date with AI stuff, but my working knowledge is that I should never trust the output of an LLM and always verify it myself, so therefore I was wondering if this is just LLM output or if there is some human review process, or a mechanism related to the citation functions that makes it output of a different, more trusted category.

I did catch the message on the loading screen as well now, I do still think it could be a little more clear on the individual articles about it being LLM generated text, apart from that I think I understand somewhat better what it is now.

I continue to subscribe to Reuters because of the quality of journalism and reporting. I have also started using Kagi News. They are not incompatible.