The "Leadership" photos all look the same, with the AI-airbrushed style. Also amazing how they both only have three people. What a coincidence.
I can't find any of the supposed "Leadership" team on Linkedin. Some of them have unusual names, which cut through the hundreds / thousands of results.
It's a worrying how easily this sort of BS is becoming. It takes far more time to debunk it then to create it. Now you could basically script this and have 100 pages by this time tomorrow, with different styles and URLs and leadership teams (along with professional portraits)...
If they'd let the URLs age a bit, and maybe not registered them on the same day, used slightly more common names, and changed the styles, it would have been much harder to definitively debunk.
The user that posted this hasn't commented in HN since more than two years ago. Their previous submissions had nothing to do with this (and they were mostly posts from their blog).
Google putting AI search summaries is the worst of all worlds.
You need cheap, fast inference to put on a search results page and finish near the deadline (~3 seconds). Cheap, fast inference is more likely to get stuff wrong. No one is happy.
My understanding of Google politics is that the small number of search team members who implemented any given 6-month-old feature will be happy as they will have been promoted away from the team and not responsible for maintenance nor quality.
I was quite happy when I could type search keywords in, Google searched for relevant pages with those keywords, and I went and read those pages. Haven’t really been happy since they broke it quite a while ago.
I'm curious how google think about the damage this does to the perception of the quality of their AI tools. Every second google search I do reinforces the association "google AI is crap" because you can't help but see it there at the top of the page. Surely this perception hurts Gemini adoption across the board?
This has become a common pattern in my Claude & Gemini usage. Always require citations, then check those citations to validate they actually contain the information/data the LLM's output claims. Claude, in particular, seems to make massive logic jumps and trust tertiary data sources way more than it should.
There’s a fun variation in W-Europe that google needs to spend some time on:
Northern Belgium and the Netherlands have web content in the same language. But google uses the content in one lump. Problem is when you search for employment/fiscal/legal/… you constantly get content that applies to the wrong nationality.
This is true of a ton of online discourse. Worse, when the headline of a claim doesn't even match the article it is fronting. I've seen more than a few articles that basically contradict the headline, but end in a "despite all evidence, we think it is correct to say X."
Never believe the citations in Google summaries, I can count on a single hand the time I have found a correct citation.
It's not 2/3 correct, it's <1% correct, in my experience. Maybe it's because I check things that sound off more frequently, but even random checks have not panned out well.
Anybody relying on Google AI summaries is misinforming themselves.
Those summaries (and Bing is at least as culpable) are mostly only good for comedy value. Most favourite genre: returns picture of someone you know alongside biography of completely unrelated person. Least favourite genre: useful stat supposedly cited from a linked reliable source (but turns out to actually be a different stat in the reliable source)
I don’t know if Claude performs similarly from a percentage standpoint, but if you’re using it for search (online or personal docs or wikis), it often also just makes things up.
When you point it out, it’ll do the “ohh you’re absolutely right!” bs. Marketing material and management that believes the material wants to pretend that AI agents are junior employees, but forget that junior employees get fired for doing something like this.
I don't even know if I disagree with this post, but this seems really astroturfed. Why are there two anti-Perplexity articles from independent research firms with identical websites on the front-page of HN right now, submitted by the same person? Am I going crazy?
If you are building your own harness that does correct citations, is the correct thing to give AI access to some deterministic tool that allows it to actually copy paste parts of documents its reading (with links), rather than stochastic reproduction that they do by default?
I did something similar for structured text extraction. I added markers throughout each source document and then, for each piece of info I wanted, I asked the LLM to provide two separate fields:
xyz
xyz_citation
The latter was just the node number. So then my code could extract the exact snippet, instead of trusting the LLM to quote something verbatim.
That’s what I did when I built my stuff. I have deterministic content with AI commentary, where it seems most people are doing this crazy thing of sending data through the model. I can’t understand it.
I noticed this personally. Saw a citation with a preview for source A, which I knew was reliable. Checked, and it referenced a Reddit article and various other less reliable sources. Was a direct citation too that actually wasn't.
To be honest perplexity does nothing to make sure it's answer are correct let alone the citations. They just look plausible. For anything little bit serious I use nouswise or nblm that sometimes abstain instead of making things up.
This is why lawyers have been getting in trouble using AI to review case law or (worse) to generate documents.
It creates citations and references that look close enough to be plausible but are just made up of thin air. CA passed a law explicitly requiring lawyers to review AI-generated documents that is now before the governor for signing (previously, lawyers were ethically expected to review documents submitted to the court or provided to clients but that doesn't have the same level of force as an explicit requirement).
Trump Media and Technology Group announced that it partnered with Perplexity to test and integrate an AI search feature, referred to as Truth Social AI or Truth Search AI, directly into the Truth Social platform.
(I just use the free account from truth+ to waste their money)
how do you reverse a linked list in python
Answers
Sources
Use either an iterative pointer-reversal approach or a recursive approach. The standard iterative version is the most common and runs in (O(n)) time with (O(1)) extra space:
class ListNode:
def __init__(self, val=0, next=None):
self.val = val
self.next = next
What's surprising about this is that you can get the bullshit machine to produce correct externally validate citations. It's not particularly hard either—it's one of the first things you build when you give an LLM access to a body of documents/search. So for a large public service to whiff like this is certainly a stain on their credibility.
On the other hands it's a boost to their credibility that they make their mistakes easier to evaluate than their competition does. It would be worse if they had a similar error rate without openly providing references. Kudos to Perplexity for including more empirical attack surface.
By training it on a whole bunch of examples with valid external citations, to the point where it's able to hallucinate something that's valid. Of course, it won't always work, which is the point of TFA.
I don't think this is an answer. I'm basically claiming there is an uncontrollable error probability, and I think you agree with that. The person I'm replying to implies it can always be reduced (maybe even to zero).
Also posted today is this HN thread:
https://news.ycombinator.com/item?id=49536375
Containing this link:
https://trellner.com/reports/manufactured-sources-behind-ai-...
Which has the exact same layout and very similar About page as "trellner.com"
https://hausresearch.com/about/
https://trellner.com/about/
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