"Costel Pantilimon is the Romanian footballer who won the English Premier League.

"He did it twice with Manchester City, in the 2011–12 and 2013–14 seasons, earning a winner’s medal as a backup goalkeeper. ([Wikipedia][1])

URLs:

* [https://en.wikipedia.org/wiki/Costel_Pantilimon]

* [https://www.transfermarkt.com/costel-pantilimon/erfolge/spie...]

* [https://thefootballfaithful.com/worst-players-win-premier-le...

[1]: https://en.wikipedia.org/wiki/Costel_Pantilimon?utm_source=c... "Costel Pantilimon""

I could not find a Romanian football player who has won the Premier League title.

If you like, I can check deeper records to verify whether any Romanian has been part of a title-winning squad (even if as a non-regular player) and report back.

Then I followed up with an 'ok' and it then found the right player.

#2: One Romanian footballer who did not win the Premier League but played in it is Dan Petrescu.

If you meant actually won the Premier League title (as opposed to just playing), I couldn’t find a Romanian player who is a verified Premier League champion.

Would you like me to check more deeply (perhaps look at medal-winners lists) to see if there is a Romanian player who earned a title medal?

#3: The Romanian football player who won the Premier League is Costel Pantilimon.

He was part of Manchester City when they won the Premier League in 2011-12 and again in 2013-14. Wikipedia +1

Yes — the Romanian player is Costel Pantilimon. He won the Premier League with Manchester City in the 2011-12 and 2013-14 seasons.

If you meant another Romanian player (perhaps one who featured more prominently rather than as a backup), I can check.

The Romanian football player who won the Premier League is Gheorghe Hagi. He played for Galatasaray in Turkey but had a brief spell in the Premier League with Wimbledon in the 1990s, although he didn't win the Premier League with them.

However, Marius Lăcătuș won the Premier League with Arsenal in the late 1990s, being a key member of their squad.

Most of the time, I suspect, people are using it like wikipedia, but with a shortcut to cut through to the real question they want answered; and unfortunately they don't know if it is right or wrong, they just want to be told how bright they were for asking it, and here is the answer.

OpenAI then get caught in a revenue maximising hell-hole of garbage.

God, I hope I am wrong.

General search is not one of those! Sure, the machine can give you its sources but it won't tell you about sources it ignored. And verifying the sources requires reading them, so you don't save any time.

> Sure, the machine can give you its sources but it won't tell you about sources it ignored.

You can prompt for that though, include something like "Include all the sources you came across, and explain why you think it was irrelevant" and unsurprisingly, it'll include those. I've also added a "verify_claim" tool which it is instructed to use for any claims before sharing a final response, checks things inside a brand new context, one call per claim. So far it works great for me with GPT-OSS-120b as a local agent, with access to search tools.

Feel like this should be built in?

Explain your setup in more detail please?

Not everyone uses LLMs the same way, which is made extra clear because of the announcement this submission is about. I don't want conversational LLMs, but seems that perspective isn't shared by absolutely everyone, and that makes sense, it's a subjective thing how you like to be talked/written to.

> Explain your setup in more detail please?

I don't know what else to tell you that I haven't said already :P Not trying to be obtuse, just don't know what sort of details you're looking for. I guess in more specific terms; I'm using llama.cpp(/llama-server) as the "runner", and then I have a Rust program that acts as the CLI for my "queries", and it makes HTTP requests to llama-server. The requests to llama-server includes "tools", where one of those is a "web_search" tool hooked up to a local YaCy instance, another is "verify_claim" which basically restarts a new separate conversation inside the same process, with access to a subset of the tools. Is that helpful at all?

It's like working with a very cheap, extremely fast, dishonest and lazy employee. You can still get them to help you but you have to check them all the time.

But there seem to be uses where a verified solution is irrelevant. Creativity generally--an image, poem, description of an NPC in a roleplaying game, the visuals for a music video never have to be "true", just evocative. I suppose persuasive rhetoric doesn't have to be true, just plausible or engaging.

As for general search, I don't know that we can say that "classic search" can be meaningful said to tell you about the sources it ignored. I will agree that using OpenAI or Perplexity for search is kind of meh, but Google's AI Mode does a reasonable job at informing you about the links it provides, and you can easily tab over to a classic search if you want. It's almost like having a depth of expertise doing search helps in building a search product the incorporates an LLM...

But, yeah, if one is really disinterested in looking at sources, just chatting with a typical LLM seems a rather dubious way to get an accurate or reasonable comprehensive answer.

And you can’t even treat implementation as a black box, even using different LLMs, when all the frontier models are trained to have similar biases towards confidence and obsequiousness in making assumptions about the spec!

Verifying the solution in agentic coding is not nearly as easy as it sounds.

The ass licking is dangerous to our already too tight information bubbles, that part is clear. But that aside, I think I prefer a conversational/buddylike interaction to an encyclopedic tone.

Intuitively I think it is easier to make the connection that this random buddy might be wrong, rather than thinking the encyclopedia is wrong. Casualness might serve to reduce the tendency to think of the output as actual truth.

That tracks; it's what's expected of human customer service, too. Call a large company for support and you'll get the same sort of tone.

Stop giving them the benefit of the doubt. Be overly suspicious and let them walk you back to trust (that's their job).

We’re preaching to the choir by being insistent here that you prompt these things to get a “vibe” about a topic rather than accurate information, but it bears repeating.

Pretty much only search-specific modes (perplexity, deep research toggles) do that right now...

When folks are frustrated because they see a bizarre question that is an extreme outlier being touted as "model still can't do _" part of it is because you've set the goalposts so far beyond what traditional Google search or Wikipedia are useful for.

^ I spent about five minutes looking for the answer via Google, and the only way I got the answer was their ai summary. Thus, I would still need to confirm the fact.

And then you can use that to quickly look - does that player have championships mentioned on their wiki?

It's important to flag that there are some categories that are easy (facts that haven't changed for ten years on Wikipedia) for llms, but inference only llms (no tools) are extremely limited and you should always treat them as a person saying "I seem to recall x"

Is the ux/marketing deeply flawed? Yes of course, I also wish an inference-only response appropriately stated its uncertainty (like a human would - eg without googling my guess is x). But among technical folks it feels disingenuous to say "models still can't answer this obscure question" as a reason why they're stupid or useless.

Asking "them"... your perspective is already warped. It's not your fault, all the text we've previously ever seen is associated with a human being.

Language models are mathematical, statistical beasts. The beast generally doesn't do well with open ended questions (known as "zero-shot"). It shines when you give it something to work off of ("one-shot").

Some may complain of the preciseness of my use of zero and one shot here, but I use it merely to contrast between open ended questions versus providing some context and work to be done.

Some examples...

- summarize the following

- given this code, break down each part

- give alternatives of this code and trade-offs

- given this error, how to fix or begin troubleshooting

I mainly use them for technical things I can then verify myself.

While extremely useful, I consider them extremely dangerous. They provide a false sense of "knowing things"/"learning"/"productivity". It's too easy to begin to rely on them as a crutch.

When learning new programming languages, I go back to writing by hand and compiling in my head. I need that mechanical muscle memory, same as trying to learn calculus or physics, chemistry, etc.

That is the usage that is advertised to the general public, so I think it's fair to critique it by way of this usage.

I like to ask these chatbots to generate 25 trivia questions and answers from "golden age" Simpsons. It fabricates complete BS for a noticeable number of them. If I can't rely on it for something as low-stakes as TV trivia, it seems absurd to rely on it for anything else.

https://chatgpt.com/share/69160c9e-b2ac-8001-ad39-966975971a...

(the 7 minutes thinking is because ChatGPT is unusually slow right now for any question)

These days I'd trust it to accurately give 100 questions only about Homer. LLMs really are quite a lot better than they used to be by a large margin if you use them right.

I play once or twice a week and it's definitely worth $20/mo to me

The best thing about the latter is search ads have extremely unfriendly ads that might charge you 2x the actual fee, so using Google is a good way to get scammed.

If I'm walking somewhere (common in NYC) I often don't mind issuing a query (what's the salt and straw menu in location today) and then checking back in a minute. (Or.... Who is playing at x concert right now if I overhear music. It will sometimes require extra encouragement - "keep trying" to get the right one)

Damn this is a lot of self correcting

Let's call it "Florin Andone on Premier League" :-)))

ChatGPT 4o-mini, 5 mini and OSS 120B gave me wrong answers.

Llama 4 Scout completely broke down.

Claude Haiku 3.5 and Mistral Small 3 gave the correct answer.

I find it amusing, but also I wonder what causes the LLM to behave this way.

Forum threads etc. should have writers changing their minds upon feedback which might have this effect, maybe.

This is why people are getting different results with thinking models -- it's as if you were going to be asked ANY question and need to give the correct answer all at once, full stream-of-consciousness.

Yes there are perverse incentives, but I wonder why these sorts of models are available at all tbh.

Okay, as a benchmark, we can try that. But it probably will never work, unless it does a web or db query.

Because, one way or another, we will need to do that for LLMs to be useful. Whether the facts are in the training data or the context knowledge (RAG provided), is irrelevant. And besides, we are supposed to trust that these things have "world knowledge" and "emergent capabilities", precisely because their training data contain, well, facts.