My PhD is on auditability of AI systems, and we are exactly working on the engineering of ZKP in this context. I can confirm through our industry partner (AI Assurance and auditing), that black box auditing is indeed a huge problem for them, and snarks have shown their potential in this context. It's great to see Anthropic confirming our early results.
HTTPS makes sure you’re communicating on a secure channel with the API endpoint. ZKPs guarantees that the computation happened correctly behind the endpoint.
The work from Anthropic isn't a new idea, but they made the process more efficient, as inference verification is one of the biggest bottlenecks in the AI lifecycle. Beyond that, there's a lot more that can be proven, like showing a model was trained on a specific dataset with certain properties.
From an auditing perspective, this used to be handled either by showing the data (which is pretty rare), checking samples (which has been shown to be easily hackable), or similar workarounds. For inference, an auditor might want to interact with a model on demand and have absolute certainty that the model they're talking to is actually the one declared, and not a substitute.
I’m not sure what you mean by correctly here - do you mean with the model expected? That’s not related to correctness.
Pleased to hear somebody cares about this. What is it a bottleneck for though? Are people actually doing this right now in non-academic settings? I had the impression this is not in fact usable with larger models.
ZKPs verify "things" by encoding the verification in a computation, and verifying that the computation happened in the right way, means to assess it was computed correctly, i.e., with the right inputs, the right operations, etc.
So this is what I meant with "correctness".
The bottleneck has two sides: first, there's a technical bottleneck. ZKPs are quite expensive, and finding the right, production-ready protocol is challenging. The other side is a socio-technical bottleneck. There is always quite a large friction with adopting these kind of technologies, also due to the first bottleneck.
As you said, it is difficult to adopt because of how fast the complexity grows with big models. It's great indeed to see that Anthropic is tackling the challenge.
Well luckily not really, their work is more foundational-y, the idea is not new, but they made it more efficient and practical. On our side, we work more on the integration of these protocol in production systems, but we are pretty much on the frontier and is not very common to see such big companies experimenting with this technology. Microsoft already tried something [1] but I'm not sure if it ever went further than a research prototype.
What scares me about this new AI mode thingy is that every answer sounds like a systematic literature review, but only for the results. For example, if I look for users feedback about a specific product, it says "People think that..., but also that...; It's important to notice that some people ..." where with 'people' it means just a random comment on a random website just because it thought it was a good contribution to the results. Sounds like it's giving a ground truth from "multiple" data, when instead it's just aggregating almost random stuff. In the context of a systematic review, the feature that I would love the most is augmenting my initial query, so that I can just get more results that I could find interesting. I am 100% sure they thought about this, but ignored it for the most profitable option.
What scares me is the rampant inaccuracy. In my experience, the AI responses are wrong about 65% of the time. I just did a search today about an error talking about a disconnected link between apps, and Google AI result summary told me that the error was related to my pulling a USB drive too quickly in windows. The ONLY word similar to my query and that AI response was the word "disconnect". Everything else was clearly about the SaaS apps.
I have people coming to me, asking me questions, then telling my Google told them something else, so now I have to waste time convincing them that it's wrong. Over the past 2 years AI has done nothing for me but complicate my work life.
And of course, this could be because the model is crap, but it could be because they want me to keep refining my query over and over for more ad views. Either way, it's a terrible experience.
Yeah the Google AI results are more dangerous than ChatGPT, not only because it uses a smaller model but because Google's knowledge graph used to deliver very accurate and authoritative information but now that's been replaced by a stochastic system in the same place, so people are used to trusting it.
I think we’re getting what we deserve by snarkily telling people to Google stuff instead of answering accurately. Google results have never ever been pure accuracy
The point of LMGTFY is to land people on either the official documentation or a curated site like Stack Overflow. Google used to be able to do that reliably.
With the power of LLMs you can Google a standard library function and get an inaccurate summarisation of a Reddit discussion where neither side knows what they're talking about
Stack Overflow and Reddit for years have told people to just Google it. And then the Google result is people saying to just Google it, instead of actually being helpful.
To be fair - for all of those years Google has been serving up some atrocious results - remember when googling health symptoms got you rabies or pregnancy.
There's even the meme where people ask if the code was the result of a stack overflow question, or answer
It seems to me one needs to consider the complexity of the question they are asking before searching it.
To stick with your post, consider people asking medical or financial questions. For a wide variety of reasons, many of such questions don't have an answer. In such cases, AI is still going to take a crack at it. AI shouldn't be blamed for "bullshit answers" to such questions.
Before using AI, I think people should stop and ask themselves, "Is there really a single answer to this question? Is AI the right choice?"
The problem is Google's AI results get even simple factual questions wrong all the time.
Earlier today, I searched "pixel 10 wifi 7" because I was confused that GSMArena showed my Pixel 8 supports Wifi 7, but the Pixel 10 only Wifi 6. Gemini confidently claimed that the Pixel 10 does support Wifi 7 -- but that's not true at all. Only the Pixel 10 _Pro_ supports it, as I discovered when actually reading the non-AI search results.
I had a similar thing when I was gooling a few days ago, I can't remember exactly but it was like "why does [product] not support [feature]" and the AI summary was confidently wrong, saying "The product does support [feature]", which knew was completely incorrect, and I did find a Reddit discussion or something in the actual results with discussions that were actually about what I was looking for!
It's really depressing how bad things are getting...
It’s hilariously persistent in this, esp. for anything even slightly divergent from the beaten path. Discount everything the AI box says about emacs to zero.
Admittedly I’m unsure if it was Google or DuckDuckGo. I switch between both. I quickly asked the in search AI for a UTC time conversion like a lazy fool and it got it off by almost a day wrong.
I avoid any asking any agent a fact-based (especially math) request. It's a great compression algorithm and a great language generator, and I guess the intersection of those two things is "an answer". Calculation doesn't intersect.
My google search for 'pixel 10 wifi 7' immediately shows the right answer. (10 Pro and 10 Pro XL support it but, but base Pixel 10 only supports Wifi 6E).
Though the inconsistency of results between users is definitely another frustrating thing.
Because LLMs aren't sentient, they don't draw on facts, and they don't have nuance. The answer given is similar to answers you might expect to see for similar questions.
It's really amazing we can make machines do that, and it's really depressing that we think a stochastic bullshit machine is going to give us something we can rely on.
Or… the default LLM Google uses for search has been quantized to s**. Ask a proper Thinking model, with browsing enabled, and odds of a correct answer are much higher. There’s been substantial improvement in AI in even the last year.
Ask a human a question like this, and they also have a chance of getting it wrong, even when confident.
Why would a human know specs for a random phone off the top of their head? The human response is either "I don't know" or "let me look that up", not a hallucination.
I think that it feels a little wasteful to go to Google search to ask a question like this, only for the AI that's giving you an answer instead of page results to perform its own web search to get you the response.
Also, I asked a thinking model with browsing enabled and got this:
> The Google Pixel 10 is expected to support Wi-Fi 7 (802.11be), based on the Qualcomm Snapdragon 8 Gen 4 / Tensor G5 chipset it will likely use, which includes an integrated Wi-Fi 7 modem. Specific finalized specs aren't confirmed until Google's official announcement.
(Model GLM-5-Turbo - two months old - using Kilo Code in the "Ask" profile; in its thinking token churn it reasoned that it should keep the response brief and direct. Perhaps not the best suite of model+harness for this task, but it's what I had to hand that's not quantized to shit, is a thinking model, and has a web search tool available to it.)
> Ask a human a question like this, and they also have a chance of getting it wrong, even when confident.
We google something specifically because the humans within reach don't know. The goal of searching is, well, to search pages - we're trying to find a site when we use google search.
The goal when using an LLM is generally different; we want an answer, not a site.
LLMs can not point you to sites, only in a general direction. That is because complete URLs do not exist as single tokens in any of the large models. It can synthesize a plausible-looking url, and if you're lucky that URL might even exist. But that doesn't mean that there is any relation between between the text surrounding a hyperlink in LLM output and the text on the linked page.
AI agents can verify and summarize URLs, but a plain LLM can not.
its bad in dev as well... i've seen llm code review bots tell me things that are flat-out not true; this like "this wont compile because windows 11 doesn't exist" like wtf am i paying for this again?
They are this wrong about everything, but you don't usually notice it when using it to look for things you aren't an expert in. The default stance really does need to be "do not trust, verify" at all times.
They can still be useful, e.g. they're significantly better at finding "I want a thing that does x but not y and it must be blue, or maybe two things that can be glued together to do that" than classic search. But they'll routinely miss extremely obvious answers because the related search it ran didn't find it, or completely screw up what something can actually do. Checking more pages of results by hand or asking humans who know even a little about those fields is still wildly more useful... but they're absolutely slaughtering the sites where people do that, by stealing all the real traffic and sending DDoS-level automated requests.
How can you say they are wrong about "everything"?
I built a retro game clone once and I used that project as a way to try out AI. While it wasn't perfect, it definitely wasn't wrong about everything. I'd go so far as to say it was probably correct (or damn close) 75% of the time.
I see people on HN all the time saying AI is terrible, but that just isn't the experience I'm having. I'm willing to admit it may have something to do with me not being able to recognize I'm being fed bullshit. Or, I may be asking really simple questions. Who knows? But AI seems like a pretty useful tool for average people.
Your profile says you're a guitarist. Take the model and talk to it about guitars. Not like "what's a good Stratocaster clone", talk to it about materials, physics and playing techniques and see if it feels like a reliable source of information (or even a solid thinking buddy), or someone who read a lot about guitars but has actually never played one.
I know bits from non-IT fields like RC planes and quads, electric motors, aerodynamics, mountain biking, cars. I often use Claude (Opus 4.7 on Max sub atm) to brainstorm new ideas or refine my understanding of some phenomena, and almost without fail, I can get it to claim something ridiculously stupid or contradictory 5-10 messages in. I can usually catch it, because I don't venture far from what I'm already familiar with, and I also need explanations to be thorough and things to make total sense to me before I accept them, but not everyone is that pedantic.
I’d make assumptions about how the cheapest and fastest possible flash model optimized for being extra cheap and extra fast would get something wrong based on its limited context (which can be very incomplete summaries of search results)
I often have the expensive models give relatively simple inaccurate answers, even when they cite sources that directly contradict them. The error rate is lower, but you can’t have confidence with llm answers.
It somehow seems to interpret whatever sources it's grepping as the exact opposite of what those sources say fairly often. I've lost track of how many times I've clicked on the sources it cites, and every single one is in agreement, but the AI claims the opposite.
In watching the demo, I didn't come away with the impression that they were removing search results. Yes, they are pushing AI hard, but users can still opt to use Google in the more traditional way. Unless I misunderstood the demo, it's definitely possible to choose.
An interesting aspect of this is the decrease in quality feedback on th organic links. If most people never get down to the actual links there is very little to tell which ones were good or if they had any relevance.
There is also that less incentive to properly maintain the search algorithms to fight SEO and spam.
For all intents and purpose, organic search results have been given a death sentence and are just waiting for the last moment.
> one needs to consider the complexity of the question they are asking before searching...consider people asking medical or financial questions...many of such questions don't have an answer. In such cases, AI is still going to take a crack at it. AI shouldn't be blamed for "bullshit answers"...people should stop and ask themselves, "Is there really a single answer to this question?
It's a bold position to say that it's the users fault for being lied to by Google. There isn't a "single answer" to most questions. It's still Google's job to provide answers that are accurate and reflect the best information available on complicated topics. That's what they're trying to sell us anyway. When google's AI can't live up to the hype "You shouldn't be asking AI such difficult questions" is not a great response, especially when people are just trying to get web search results and AI is suddenly interrupting with an opinion nobody asked for.
It's nice that Google's AI summary always lists its sources. It's less nice that those sources more often than not do not corroborate the summary. It often seems to be a few random links thrown in there for good measure.
I have no idea why this is, but it is impossible that these links are primary sources of the data, if such things even exists at all. In which case, why list them?
It is certainly seems possible that the actual sources of the data is the output of some other LLM.
> It is certainly seems possible that the actual sources of the data is the output of some other LLM.
My guess is you can see this happening with the bots on Reddit where they are refining the answers to one certain thing, often getting two or three the same responses in a row from different users because they have been enforcing themselves by digesting the output of other bots. Waiting to see when they cut down the sentences and start talking garbage.
What scares me is the massive incentivization to manipulate the results.
With AI ads you get all the power from big data aggregation, the trust/framing of an authoritative voice, and cheap personalization that specifically optimizes for what convinces you. It's too powerful. Even if it only works a small percentage of the time we're interacting with these things so frequently that a small percent is a large number. They're already feeding user profiles into these machines and there's explicit talk about having the LLMs optimize ad campaigns. It's already dystopian if it's ads to get you to spend your money, but people seem to dismiss that. Do we not care that this is also being used in the same way to convince you to believe certain things? To join certain political organizations?
Yeah, these things help me write more lines of code faster (if we include all the lines from our design docs) but I don't like the idea of pointing a supercomputer at my brain and someone else using it to try to manipulate me. That's not a game I'll win. It's not a game you'll win either.
The built-in Search AI is fucking braindead and people constantly come up to me "Google said xyz" and I just have to turn around and say "I do not care what the Google Search AI said".
Whatever it says is a waste of time 99% of the time. Although people believe it, or consider it worthwhile majority of the time because its so simple to use. It's always there, always instant and appears at the very top.
I would much rather people shove a question into a locally running Qwen model and tell me what it said rather than use the nonsense search model. I hate it.
Depends on what you ask. It's pretty easy to get wrong information.
e.g. search for "how do you make money with options"
Google's AI says
"When you buy a Call, you are betting the stock price will go up. When you buy a Put, you are betting it will go down."
Wrong right off the bat, because it ingested a whole bunch of get-rich-quick bull on the internet. The correct version is that if you buy a call you are betting the stock price will go up more than the market expects it to.
I tried this search. It gave a write up about buying and selling options, noting that the price of the stock had to move significantly, not just go up or down. It also talked about vertical spreads and iron condors. It touches on delta, theta, and volatility and their impacts, as well as leverage risk and potential uncapped risk.
While I agree that AI gets things wrong a lot, and someone should read significantly more before getting into actually trading options, this does give a decent overview to give a layperson an idea of what they are, and some key terms on what to look for if they want to dive deeper. That said, with this info alone, there are some sharp edges that would leave the person open to unnecessary risk if they went on this information alone.
They probably update these answers offline. I tried "how do you profit from options" and got:
> Call Options: You buy these when you believe a stock's price will go up. If the stock rises past your strike price, the option's value increases, allowing you to sell it for a profit or exercise it to buy the stock at a discount.
> Put Options: You buy these when you believe a stock's price will go down. If the stock falls below your strike price, you profit.
Which leaves me wondering if changing the search textually busts some cache that they update using a slower/smarter model.
And this is yet another problem, it's stochastic. And often it's self-contradicting even within the same response. What else do you expect from a language model which essentially predicts tokens.
It's wild to me that someone looking for advice on how to do any kind of stock trading would be looking for a once sentence answer.
I hope it at least has real citations to actual websites like, I dunno, fidelity or some other reasonably competent authority that can explain all the details?
It's an answer that's too short for an expert to find useful, and useless to a layperson unless all they want to do is reply to a post on twitter.
I've never searched for a financial question where I did not want to know all the weird details because why would I search for it unless I was considering doing it? Seems like someone who doesn't care about the answer is going to be more an edge case than I am.
Those looking for a one sentence answer will be the quickest to invest. When people talk about the harms of AI, this is the kind of thing that comes to mind first for me.
It is, in fact, categorically wrong, and misleads beginners to make bad decisions. Robinhood is notably bad for promoting this kind of gambling behavior on its platforms; they also state the same misinformation (that you buy a call if you think something is going to go up, when it is in fact a bet that it is going to go up more than a certain amount in a certain period of time).
People shoot themselves in the foot because they think NVDA is going to go up after earnings, buy call options, and then even though the stock goes up they lose money because they did not understand IV crush.
People looking for one-sentence explanations should really not be playing with options. In finance you should understand what you're buying thoroughly. If you just want to bet that "NVDA goes up", you should just buy NVDA stock; that is the trade that accurately captures that bet.
This is the problem with teaching and learning. Everything is wrong to some extent. I used to be this way but I don't have a better approach.
Newtonian physics is actually wrong, the founding of any country will be wrong, biology is wrong, nutrition is wrong… what can we even teach? what should we teach in this lens? serious question.
The serious answer is in the non-AI-summarized world, you can choose whose information to read and trust.
If you want to learn about finance, you can learn about it from people who actually know what they're talking about. You can choose to listen to Jim Simons or Warren Buffet or whoever actually knows a thing or two instead of the rando dude you met at the bar. The AI summaries, on the other hand, ingested a lot of internet garbage.
I picked finance as an example because anecdotally, most of the information on the internet by pure token volume is wrong. The Youtubers drawing lines on charts want your attention because they make money from page views; the financial advisors want your annual fees; the brokerages want you to gamble and get your commisions or PFOF (in the case of zero-commision brokers); the market makers and HFTs want your spreads; Reddit users want to show off their lucky, statistically insignificant profit charts for karma points. None of the above have an intention to give you good information.
Honestly Google's AI answer is about as right if not more right then your answer.
You can easily make money buying a call without the stock price moving a single cent (IV increases). Funny enough the stock can even go down and with a large enough IV increase you still make money.
I used this exact search in Google's AI, plus 3 other LLM models, and not a single one simplified options down to anything like the one sentence example you provided. In fact, they all provided multiple paragraphs of information on the basics on option buying and selling, and provided links for learning more.
Funny enough, the LLM's prove to be more trustworthy than the the posters here on HN like you who are full of shit.
It hallucinates greatly about many things when I ask about C++ things. Things that you can easily find the right answer in cppreference or by just inspecting headers in your own IDE.
Yup, I was looking up a pair of IEMS vs another pair of IEMs. It said option A is overall better, when really it was just reciting a single person's opinion. I've been aware it will summarize only a single source and present it as an aggregation of many opinions, but it stood out to me how matter-of-fact it was that the one was definitely better than the other. I simply wanted to find forum discussions on people's thought and wasn't influenced by this AI blurb, but I think seeing an answer at the very top state so matter-of-factly that one is definitely better and present it as though everyone thinks that will definitely influence a lot of people. It makes me wonder how "gameable" this will become...
> It makes me wonder how "gameable" this will become...
You better make sure your ad spend is high enough that your product's matter-of-fact result will be positive. That's a nice product you have there. It'd be a real shame if nobody knew about it.
The iem sub has a post with some recommendations at various price points. I’d probably start there, not sure your budget and I don’t know have the most experience with the super cheap ones: https://reddit.com/r/iems/comments/1la65kr/top_5_iems_in_eve...
I also encourage finding the right tips. Tips are cheap and finding proper fitting ones is important.
Most of the ones that are over 50 can last a lot of time and have good enough sounds. But the most important stuff are the tips and the cable. Make sure the former fits (just buy a pack) and the latter is thick and braided. Some cheapo ones send every rubbing amplified to your ears.
Not gp but I really like the sound of my GK Kuntens and 7Hz Zero2s. Both have a rather V-shaped sound signature, some like it and some don't. Though unfortunately the Zero2s feel a bit uncomfortable in my ears when using them for longer
I was only intending to use the traditional search engine, the AI was incidental. If I want an AI answer, I will go to it separately. This is better in my mind as optimal queries for each are different.
The issue is, Google has mixed the two in a way that promotes the AI response as primary. This has resulted in dubious answers being presented as “official” summaries (to the lay person).
At the very least, one would expect it to be a little smarter—perhaps by automatically doing things like you suggested—instead of basing things off a single source, as it seems to enjoy doing.
Indeed - just earlier this week I read Google AI summarize a query about testosterone, citing 3 sources. The first citation was a link to a NIH study (or of similar repute). Ok great. The second? Two spam (and explicit) websites existing solely to sell penis enlargement pills.
What was worrying is only some of the claims were supported by the linked study, and most of the response content was drawn from the spam sites.
This problem is not limited to Google, it's the core value of mass-marketed LLMs, or isn't it?
Without "random comments", Google wouldn't have anything to say about "does an air purifier help my asthma, if yes: which one?" or "find the problem with this Hibernate annotation".
They also don't make much effort to exclude sloppy sites, to the contrary, they made way more efforts against SEO spam in the time when Google was a search engine, not trying to be an AI "oracle".
I think their end game is that the only metrics relevant for ranking sources are:
- agreeability (works well as a proxy for correctness with many questions!)
- originality, but not in a scientific sense, just to prevent model collapse
- legal factors such as preventing false health claims or similar things, as long as there is legislation against this kind of thing
What scares me are the basic usability fails it still has. Search for a few foreign language words and it will come back with paragraphs upon paragraphs of AI output in that foreign language despite me telling Google in 15 different ways that I don't speak it, nor anything else on the Google page being in that language. How are all their products always made by and for the most narrow minded people on this planet.
Funnily enough, I have the exact opposite problem, where Google likes to give me results in the configured main language even when I do queries in another and actually want results in the other language.
I’ve found it quite unsettling to be served foreign language videos on YouTube automatically dubbed over by Google into English. Just mixed in with the search results.
It's quite unsettling to be served English AI-dubbed videos randomly on Youtube, where the original is another language you normally default to (Japanese) and it's happy to use in most cases. And then you go to the settings for the video to go back to the original and it tells you it is... Chinese (and no, it's really Japanese).
> "People think that..., but also that...; It's important to notice that some people ..." where with 'people' it means just a random comment on a random website
I’ve noticed this too. A single result can determine the answer it gives. And removing the content from its context makes it harder to assess. Suddenly it’s “Gemini said …” rather than “some guy in the YouTube comments said”.
The scary bit is the use of the term AI. The "I" implies critical thinking.
For models trained on a corpus of groomed data, the "critical thinking" bit is baked into the work of grooming the data and how it is trained. And someone is thinking critically about both so as to make a good model.
Now, every damn thing is called AI no matter where it is getting results from.
Are modern models super handy? Absolutely.
But calling it AI implies a lot more critical thought than is actually happening!
> where with 'people' it means just a random comment on a random website
My experience is that Google's AI summaries tend to be be very heavily reliant on YouTube videos. If there is a YouTube video on the topic, you can bet that's what the source will be, at least for the topics I have searched lately.
And this is the best the results will ever be! There's so many sites now that are themselves just the unreliable output of AI that it will soon be summarising the summary of a summary of an hallucination and presenting it just as confidently as ever.
I love asking AI about blatantly wrong opinions but by people it thinks must be an authority.
To not make this political, let me give you a game example. Right now the dota 2 fandom wiki is abandoned, and it has been vandalized with covert shitposts. One of them was the addition of a 4th attribute called Charisma, which is completely fake. If you ask AI's "What are the main attributes in dota, according to the official wiki", the dumber AI will fall for it, but the smarter AI will know it's wrong, but try hard to hallucinate some sort of valid explanation like claim charisma is from a custom game or a fan suggestion or writing exercise.
Because you said the word >>OFFICIAL<<, they can NEVER straight up just say "The wiki is wrong". They presume authority from a bunch of shitposts.
I ran into one that kept referencing "people", but then I found that it was a single Reddit thread from a couple of years ago about a relatively small and obscure foreign city with 2 replies.
Well, you'll be happy to know that most of American media is exactly the same way: 2 people on twitter will generate a "Americans find Widget X is bad"
The problem of AI eating and regurgitating its own slop is only going to get worse with time. The best datasets are behind us. Future models are going to have to depend on a lot of human intervention.
> when instead it's just aggregating almost random stuff
How do you know that?
Scraping websites is literally what Google does best, stringing together information in the pattern of "some people x, other people y" requires 0 AI and could have been done since forever. I find it implausible that otherwise obviously capable models would be reduced to do something akin to just that.
Oh who cares. We are barely scratching the surface of AI. You all make it sound like it’s been around for 30 years and it sucks. It will only get better. Got to stop throwing up imaginary walls like nothing will improve.
As a counterexample, I've been seeing more "safety rejections" from Claude. Unlike search, being unable to ask _anything_ about botulinum, or details about the recent Copy Fail vulnerability (without giving my fingerprints to Anthropic to become a "verified security researcher") we're only just beginning to see the ways LLM can be used to distort information and its availability.
So you started with ‘highly doubtful’ as a comment, got given lots of examples and instead of assimilating that info you closed your eyes put your fingers in yours ears and said “oh who cares?’ - you’re on team AI regardless eh? That’s fucking weird mate.
Na. Wasn’t given any good examples. People just whining about the same stuff because “oh no I got information that’s former in the same structure that I can tell it’s AI and it makes me feel bad”
LLMs do write code better than many engineers. It’s just the the normal distribution and the way LLMs are built. Just because it doesn’t write better code than above average engineers doesn’t make it useless though.
People that say AI writes better code than them is such a tell but not the one they might think it is.
Whether or not it is worth the cost or second order effects on society is a different discussion.
Well, Italian *nonnas rarely used more than one pan for cooking, and it was very common to just put pasta without the sauce in the plate, and a generous spoonful of sauce on top. This is what you used to find in restaurants, too.
The cooking water in the large pan is a rather new thing.
Or maybe it's something just from my region :)
Can't speak for all italian regions as there are many differences; but this "spooning some sauce over naked pasta" right on the plate always struck me as positively un-italian. I would always expect the pasta and sauce to be mixed in the kitchen, and the pasta to be completely coated in sauce when the plate reached my table. Maybe it's something italian-american ?
It might be a potato culture thing as it was historically common when pasta showed up on shelves in Sweden that the older generation just went with the instructions separately, cooking pre-peeled potatoes loses so much of it's flavor so if boiled, it's done so separately, getting a new ingredient you probably do it as you've always done.
New Gemini models are quite good, Gemini 2.5 Pro is 1st in the user-benchmarks [1]. They also have Gemma, very good model that can run locally [2]. Benchmarks are not oracles of truth, but I feel like Google is not a kid who arrived late at the party anymore.
Considering that several key breakthroughs happened at Google that made GPT-style LLMs possible (e.g. Attention is all you need paper), it's more like they took a long smoke break than showed up late.
Yeah, with Gemini 2.5 Google stepped up to the grown up AI table and added on top. I still have a soft spot for Claude for general purpose chats, but have fully switched to Gemini for dev.
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