Aside: Does anyone actually use summarization features? I've never once been tempted to "summarize" because when I read something I either want to read the entire thing, or look for something specific. Things I want summarized, like academic papers, already have an abstract or a synopsis.
In-browser ones? No. With external LLMS? Often. It depends on the purpose of the text.
If the purpose is to read someone's _writing_, then I'm going to read it, for the sheer joy of consuming the language. Nothing will take that from me.
If the purpose is to get some critical piece of information I need quickly, then no, I'd rather ask an AI questions about a long document than read the entire thing. Documentation, long email threads, etc. all lend themselves nicely to the size of a context window.
> If the purpose is to get some critical piece of information I need quickly, then no, I'd rather ask an AI questions about a long document than read the entire thing. Documentation, long email threads, etc. all lend themselves nicely to the size of a context window.
And what do you do if the LLM hallucinates? For me, skim-reading still comes out on top because my own mistakes are my own.
Yeah, basically every 15 minute YouTube video, because the amount of actual content I care about is usually 1-2 sentences, and usually ends up being the first sentence of an LLM summary of the transcript.
If something has actual substance I'll watch the whole thing, but that's maybe 10% of videos I find in experience.
I'd wager there's 95% of the benefit for 0.1% of the CPU cycles just by having a "search transcript for term" feature, since in most of those cases I've already got a clear agenda for what kind of information I'm seeking.
Many years ago I make a little proof-of-concept for displaying the transcript (closed captions) of a YouTube video as text, and highlighting a word would navigate to that timestamp and vice-versa. Such a thing might be valuable as a browser extension, now that I think of it.
YouTube already supports that natively these days, although it's kind of hidden (and knowing Google, it might very well randomly disappear one day). Open the description of the video, scroll down and click "show transcript".
Searching the transcript has the problem of missing synonyms. This can be solved by the one undeniably useful type of AI: embedding vector search. Embeddings for each line of the transcript can be calculated in advance and compared with the embeddings of the user's search. These models need only a few hundred million parameters for good results.
One of the best features of SponsorBlock is crowd sourced timestamps for the meat of the video. Skip right over 20 minutes of rambling to see the cool thing in the thumbnail.
> AI False Information Rate Nearly Doubles in One Year
> NewsGuard’s audit of the 10 leading generative AI tools and their propensity to repeat false claims on topics in the news reveals the rate of publishing false information nearly doubled — now providing false claims to news prompts more than one third of the time.
Wonderful article showing the uselessness of this technology, IMO.
> I just realised the situation is even worse. If I have 35 sentences of circumstance leading up to a single sentence of conclusion, the LLM mechanism will — simply because of how the attention mechanism works with the volume of those 35 — find the ’35’ less relevant sentences more important than the single key one. So, in a case like that it will actively suppress the key sentence.
> I first tried to let ChatGPT one of my key posts (the one about the role convictions play in humans with an addendum about human ‘wetware’). ChatGPT made a total mess of it. What it said had little to do with the original post, and where it did, it said the opposite of what the post said.
> For fun, I asked Gemini as well. Gemini didn’t make a mistake and actually produced something that is a very short summary of the post, but it is extremely short so it leaves most out. So, I asked Gemini to expand a little, but as soon as I did that, it fabricated something that is not in the original article (quite the opposite), i.e.: “It discusses the importance of advisors having strong convictions and being able to communicate them clearly.” Nope. Not there.
Why, after reading something like this, should I think of this technology as useful for this task? It seems like the exact opposite. And this is what I see with most LLM reviews. The author will mention spending hours trying to get the LLM to do a thing, or "it made xyz, but it was so buggy that I found it difficult to edit it after, and contained lots of redundant parts", or "it incorrectly did xyz". And every time I read stuff like that I think — wow, if a junior dev did that the number of times the AI did, they'd be fired on the spot.
See also, something like https://boston.conman.org/2025/12/02.1 where (IIRC) the author comes away with a semi-positive conclusion, but if you look at the list near the end, most of these things are something that any person would get fired for, and are things that are not positive for industrial software engineering and design. LLMs appear to do a "lot", but still confabulates and repeats itself incessantly, making it worthless to depend on for practical purposes unless you want to spend hours chasing your own tail over something it hallucinated. I don't see why this isn't the case. I thought we were trying to reduce the error rate in professional software development, not increase it.
You mean you don't summarize those terrible articles you happen to come across and you're a little intrigued, hoping that there's some substance, and then you read, and it just repeats the same thing over and over again with different wording? Anyway, I sometimes still give them the benefit of the doubt, and end up doing a summary. Often they get summarized into 1 or 2 sentences.
Not him, but no. I read a ton already. Using LLMs to summarize a document is a good way to find out if I should bother reading it myself, or if I should read something else.
I occasionally use the "summarize" button on the iPhone Mobile Safari reader view if I land on a blog entry and it's quite long and I want to get a quick idea of if it's worth reading the whole thing or not.
Yes. I use it sometimes in Firefox with my local LLM server. Sometimes i come across an article I'm curious about but don't have the time or energy to read. Then I get a TL;DR from it. I know it's not perfect but the alternative is not reading it at all.
If it does interest me then I can explore it. I guess I do this once a week or so, not a lot.
I just use it for personal information, I'm not involved in any wars :) I don't base any decisions on it, for example if I buy something I don't go by just AI stuff to make a decision. I use the AI to screen reviews, things like that (generally I prefer really deep review and not glossy consumer-focused ones). Then I read the reviews that are suitable to me.
And even reading an article about those myself doesn't make me insusceptible to misinformation of course. Most of the misinformation about these wars is spread on purpose by the parties involved themselves. AI hallucination doesn't really cause that, it might exacerbate it a little bit. Information warfare is a huge thing and it has been before AI came on the scene.
Ok, as a more specific example, recently I was thinking of buying the new Xreal Air 2. I have the older one but I have 3 specific issues with it. I used AI to find references about these issues being solved. This was the case and AI confirmed that directly with references, but in further digging myself I did find that there was also a new issue introduced with that model involving blurry edges. So in the end I decided not to buy the thing. The AI didn't identify that issue (though to be fair I didn't ask it to look for any).
So yeah it's not an allknowing oracle and it makes mistakes, but it can help me shave some time off such investigations. Especially now that search engines like google are so full of clickbait crap and sifting through that shit is tedious.
In that case I used OpenWebUI with a local LLM model that speaks to my SearXNG server which in turn uses different search engines as a backend. It tends to work pretty well I have to say, though perplexity does it a little better. But I prefer self-hosting as much as I can (of course the search engine part is out of scope there).
Even if you know about and act against mis- and disinformation, it affects you, and you voluntarily increase your exposure to it. And the situation is already terrible.
I gave the example of wars, because it’s obvious, even for you, and you won’t relativize away the same way how you just did with AI misinformation, which affects you the exact same way.
Most recently, a new ISP contract: because it's both low stakes enough where I don't care much about inaccuracies (it's a bog standard contract from a run of the mill ISP), there's basically no information in there that the cloud vendor doesn't already have (if they have my billing details) but also where I'm curious about whether anything might jump out, all while not really wanting to read the 5 pages of the thing.
Just went back to that, it got both all of the main items (pricing, contract terms, my details) correctly, but also the annoying fine print (that I referenced, just in case). Also works pretty well across languages, though that depends on the model in question a bunch.
I feel like if browsers or whatever get the UX of this down, people will upload all sorts of data into those vendors that they normally shouldn't. I also think that with nuanced enough data, we'll eventually have the LLM equivalent of Excel messing up data due to some formatting BS.
