How? Then why would non-insiders bet? The classic prediction market is guessing the weight of an elephant (or some animal) at a circus. The average guess of the crowd will actually get very close. But if someone knows the actual weight, no one would play.
The entire idea of a prediction market is to aggregate insider information. If you don't have insiders, you're not doing predictions, you're just doing gambling.
Granted: that's what almost every Polymarket user is actually doing. But that's a bad thing. The insider whales are the only ones actually using it for its intended purpose.
No, the entire point is gambling. Yes there is insider trading mixed in as well, but the vast majority of events these markets are pushing and people are betting on don't have "insiders" at all. Sports, election winners, Bitcoin/stock/commodity prices, weather forecasts, movie box office receipts. None of this is about insider information, just pure gambling.
The real point of Polymarket is gambling, we agree. I'd extend that argument to every prediction market that is open to all comers in which people can meaningfully gamble. But that's not the original concept of a prediction market. I'm not here to defend Kalshi and Polymarket, both of which I think are pretty evil.
It's not 'predicting' when the outcome/answer is known. From the wiki entry on prediction markets "The main purpose of prediction markets are eliciting aggregating beliefs about an unknown future event."
I think you should probably read more about the background of prediction markets. Robin Hanson is a useful place to start. The whole concept of a prediction market is to convert private information into prices. That only works with "insider" information.
As I said in a parallel comment, Hanson was also thinking about scientific questions, where there are asymmetries in knowledge but people can often invest in research that improves their own knowledge (like by performing an experiment or a scientific expedition or something). So, Hanson believed that prediction markets could incentivize people to invest in scientific research in order to get an edge over other market participants in such questions. That doesn't exactly make them insiders, though.
Interestingly, it doesn't necessarily incentivize them to publish the detailed results of their investigations. They're incentivized to reveal what they expect to happen (based on how they bet), but not necessarily incentivized to reveal why they think so, or how they know. E.g. if you became able to predict the weather more accurately than other models over some timeframe, prediction markets would incentivize you to reveal (some aspects of) your predictions, but not your method for making those predictions.
It's OK, I didn't have it in cache either, I just remembered "people like Robin Hanson have said insider trading on prediction markets is a feature not a bug" and GPT5 tracked it down for me. :)
Apparently there's also something about the duration of a White House press conference where the press secretary may have been deliberately helping some people?
I continue to think prediction markets are potentially extremely useful and valuable, but I feel like there's a huge conceptual muddle about why people would (1) care about an outcome of a market and (2) be willing to bet on the outcome of a market. And perhaps (3) whom else they would be happy or unhappy to have participating in the market with them. I doubt people will be super-content with prediction markets until those issues are a bit clearer for more participants in any given market. (And I don't know exactly how we can make them so.)
I could go either way on prediction markets but I don't think the dilemma here is all that complicated. I think most people interacting with them are just valorizing gambling, and want a Nevada Gaming Commission to step in and make sure that the games are fair. They're not supposed to be fair! They're supposed to predict! It's in the name!
I don't know much, but I can't see why betting on a known outcome is good? Why not just ask the knower? Also, just because Robin Hanson says "it's about aggregating insider information" makes it true. He writes some stuff.....
The idea is that people have all sorts of fragmentary information about future events that they can't directly reveal, due to confidentiality or trade obligations (among other things), and that a prediction market effectively liberates the directional content of that information by converting it into prices.
Robin Hanson can credibly claim to have invented prediction markets as we understand them today.
"that a prediction market effectively liberates the directional content of that information by converting it into prices."
I can see this, and I guess maybe my issue is with the phrasing of "aggregating" insider information. Because you aren't just aggregating insider info, you are also aggregating non-insider information, but no one (but the insider) knows what is right.
Is there different types of prediction markets then? One where there is a true insider and one without? For example, you could take bets on weather it will rain on Saturday. People can make educated guesses, but no one really knows (no insider). On the flip side, Kanye could create a bet on whether he will run for president. He would be the only insider, so again, aggregating insider and non insider information.
> you are also aggregating non-insider information
You're not really aggregating non-insider information, because in these cases, it's not really "information", it's just (at best) rational guessing or (at worst) gambling.
But yes, Kalshi and Polymarket essentially aggregate gambling, rational guesses, and insider information that's likely to be correct. It's a losing game unless you're an insider, and these companies profit off of other people's addictions.
I would argue that a "market" in whether it will rain on Saturday isn't really a prediction market, or even a market, at all. It's just a bookmaking operation. The core function of any market, in anything, is price discovery.
What's the difference between a "Monday it will rain" market and a NCAAF prop bet on a team's rushing yards? I could argue that DraftKings prop bets are actually more like prediction markets than these "will it rain" bets. People actually do have directional information to contribute to sports propositions!
Tradeable risk is the difference between the ncaaf bet and rain futures. Levine has joked that perhaps there is some tenuous way that sports gambling is poolable risk to owners, players and coaches but there is real and obvious economic utility with rain. Neither get the advantages of prediction markets.
> would argue that a "market" in whether it will rain on Saturday isn't really a prediction market, or even a market, at all. It's just a bookmaking operation
How would you define the difference?
Cat bond premiums absolutely bet on near-term weather odds. I’d argue they’re prediction-esque.
You seem to be arguing that that's the canonical definition of a prediction market, and anything else, including markets merely aggregating non-insider beliefs about future events, should be called something else. Do you have a better proposal?
Sure, but then discussions about these types of markets are bound to become somewhat confusing for purely nominalistic reasons. (Turns out not every hard naming problem is automatically computer science :)
Well, it's confusing because you have markets on questions with very different characteristics in terms of whether they are exogenous or not (and whether they are exogenous from the perspective of particular groups), or just with different degrees of asymmetry regardless of whether there are literal "insiders".
Like, prediction markets have questions ranging from what the weather will be in a certain year, to who will win elections, to what stock prices or exchange rates will be, to whether companies will announce specific products, to whether particular people will start dating, to whether a specific person will say a specific word during a conference (some of the Manifold "prop bets" for Manifest).
These are not the same kinds of questions in terms of whether there are insiders at all or who the insiders are. Maybe we can't expect prediction markets to have the same dynamics in all of these cases.
Depending on what you want out of a prediction market, there's probably a sweet spot in terms of whom you should expect (or want) to be trading in it.
In the most exogenous events, those that are most outside of the control of individuals or groups, I think Robin Hanson hoped (in proposing "idea futures") that people would be incentivized to invest in research in order to gain a statistical edge in the market, but also assumed that there wasn't anyone who was inherently drastically better positioned to get information about the question than anyone else. E.g. "I will spend $X to get a better estimate of this probability (hopefully by otherwise ethical means?), and that will make my expected return from buying $Y worth of prediction contracts greater than $(X+Y)". Indeed not something retail investors or gamblers should probably participate in.
It's also true that in some cases where there are true insiders it can give the insiders a financial incentive to reveal confidential information. From the point of view of trying to get the most accurate possible estimate of the likelihood of future events, that would indeed also be a success, even if the process was "unfair" to non-insiders.
Yeah, I mean, they're a wretched hive of scum and villainy, I preemptively agree. I'm just saying, insider bets don't have the same ethical or legal valence on a prediction market that they do in the financial markets (even there, at least in the US, the principles underlying insider trading law are really poorly understood.)
It's funny to think that the most villainous markets might be some of the humorous prop bets where the person creating the market (or a friend of the person creating the market) literally completely controls the outcome. Like "will I say SOME_WEIRD_WORD on stage at the conference tomorrow?".
Although maybe the villainy would come in more from deceiving people about whether or not an event was under your control, more than merely encouraging people to bet on an event that was clearly and unambiguously under your control.
I agree that is funny and want to take this opportunity to say I think things like Polymarket are bad, a real corruption of the original idea. I'm not sticking up for them!
What uses or structures of prediction markets would you like to see? For things like Polymarket, are you more particularly concerned about the kinds of participants (e.g. people who really are just gambling for entertainment), or about the kinds of questions that are the subjects of contracts?
The original prediction markets were internal things at large companies, which I think are a great idea. I've flirted for a long time with doing a vulnerability prediction market. The good-faith incarnations of prediction markets aren't open to all comers; they're structured so you can't meaningfully gamble on them.
Yeah, sorry for not being clear enough. I just struggle how a good faith market can even exist. I immediately start thinking how participants would be incentivized to cheat by neglecting or even introducing vulnerabilities to win. Maybe I’m just a bit too cynical and/or should do more reading on the topic.
If you don't have insiders, you're not doing predictions, you're just doing gambling.
Non-insiders can't make predictions? I'm not into betting as a hobby but I make minor bets with myself or friends on topics with clear win-loss conditions in areas of politics where I consider myself knowledgeable. I'm pretty good at it since I find it easy to distinguish between results I'd like to see vs what I expect to actually happen.
I don't think you have a real point here, and are just using pejorative language about gambling to suggest one. Your thesis seems to be that only insiders can make valid predictions, which is nonsense.
I'm saying that most people --- and functionally all the people who feel victimized by insiders --- on Polymarket are gambling, not predicting. I feel pretty comfortable with how sound that argument is. I agree that there are users of prediction markets who are neither "insiders" (for whatever definition of that you want to use) nor gamblers, but they're participating with the understanding that they're bidding alongside insiders. And they're a tiny cohort.
If it helps, draw a line between "entertainment" and "enterprise", and use whatever term you like for uses on the "entertainment" side of the line. Either way: it has stark implications for the notion of insider impropriety.
This is just redefinition to belabor your insiders point (which I am not disagreeing with). Non-insiders are gambling, but they are also making predictions, with varying degrees of skill
Some kinds of gambling are truly random (unless they are scams) and no prediction is possible. Other kinds involve some degree of skill and predictive power. For example, people can make predictions on races based on past form, even though the element of chance can't be eliminated. It seems like you're trying to redefine 'prediction' to mean 'anticipated outcome as assessed by the most informed' in order to disqualify the validity of any opinions held by non-insiders.
I get that you want everyone to be aware of how prediction arguments were originally a fun way for experts to drive decision making without writing ever-longer arguments for their position. But you seem to be overlooking the the fact that objections are not so much to insiders disrupting hte prediction market as the impropriety of government officials or their special friends cashing in on military adventurism. Said government officials defend engaging military adventures without consent or even notice to Congress by citing security, yet placing big bets on markets to make a quick profit is highly insecure.
I don't care about insiders cashing on on adventurism. Or, rather, I care about the adventurism, but I do not care even a little bit about the impact of it on gambling platforms like Polymarket. I think I'm on reasonably firm footing when I say that (a) the inventors and popularizers of modern prediction markets and (b) US law doesn't care either.
The irony here is that the one bank-shot argument I'd see in the medium term for "insider trading" enforcement at places like Polymarket is Nevada Gaming Commission-style gambling regulation.
I would say that the popular conception of insider trading as a problem of fairness to other traders on financial markets is misapprehended, but no, it's not at all true that the only rational actors on financial markets are insiders.
> the popular conception of insider trading as a problem of fairness to other traders on financial markets is misapprehended
Well, it's the legal theory underpinning insider trading laws in much (all?) of the EU.
And the US might have a different legal theory underpinning their regulations, but practically, it largely amounts to the same effect, so under POSIWID, it's questionable whether the difference matters much.
> no, it's not at all true that the only rational actors on financial markets are insiders.
Then a non-insider-trading prediction market should be possible and at least somewhat useful too, no? You'd essentially create incentives to do thorough research and analysis of public information and publish the results.
Whether it's practically possible to enforce is a different question.
Non insiders would be if they think the odds are in their favor or that they predict that the market's opinion will shift.
>But if someone knows the actual weight, no one would play.
Now what if someone in the audience knew the weight of an average elephant, giving them an advantage. Would people still bet. I would guess yes, but they wouldn't bet as much as the person who did because they have less information.
While having better information may make it more likely for you to win, it is not surefire. Things can change last minute.
One way to use a market rationally as a non-insider is to bet big against something you want to happen. You've just set a bounty for someone to do it. Presumably for some reason this is even more in your favor outside the betting market.
Isn’t this a classic fool’s money game. The betting markets have a strong incentive to pretend it isn’t an insider’s game.
Honestly, of all the vices, I think I pity gamblers a lot. It’s just so visible and understandable to see the harm. Something like being too into porn or drink, those are less visible harm. Where running out of money is comprehensible to even a child.
It could reasonably be a non-insider, just someone monitoring comms activity, ship movement, pentagon pizza and parking indexes, and other open sources.
They won't collapse, but they won't reach their full potential. I would hate to be a16z sitting on their $10B post-money valuation Kalshi bags, that's for sure.
I'll toss in my 2 cents: 1. people that have no business whatsoever now know what linux is ie sales dawgs that only touch a computer for the occasional spreadsheet. 2. 70 year old man fed up with windows, moved to linux.
it looks great, its fast and responsive let's make this happen.
I use their browser called Comet for finance related research. Very nice. I use pretty much all of the main ai's, chat, deep, gem, claude - all i have found little niche use case that i'm sure will rotate at some point in an upgrade cycle. there are so many ai's i don't see the point in paying for one. I'm convinced they will need ads to survive.
Oh man I use Comet nearly daily, I tried setting perplexity as my new tab page on other browsers and for some reason its not the same. I mostly use it that boring way too.
A few years ago i forgot my id, i boarded my departure flight with my costco card and the return flight with my sams club card. both had pictures and my full name. to top it off, I was escorted right past everyone in line by security. it was great!
Theres alot of forces tugging at American "healthcare" - lawsuits, uninsured non-payment, subsidiation of 3rd world drugs, heterogeneous population, over eating, under exercise... usa practices reactive medicine. and maybe part of that is due to hectic modern life, but it certainly adds to the cost, time and money, that could potentially be avoided or at least reduced in a more preventive, educated system.
that being said, one can certainly find cheaper insurance (a policy to limit liability) if one knew where to look.
for instance a self employed single male, 27, queens new york, healthy non smoker, can have a national network $300 deductible, aca qualified policy, $329 a month.
I'd like to see them pull all support for Bitcoin and crypto related "companies". As we all know, bitcoin's only use cases are scamming little old lady's out of thousands of dollars at atm's and speculation. It is not an investment vehicle, it is not a currency.
If someone at the NWF is reading this, please take this into consideration. Let's start to take action against the fraud and grift, and try to make humanity a little better, one step at a time.
After decades of Bitcoin, how can you not know that it is the principal currency for darknet markets - ie the ability to purchase psychedelics without being involved with drug dealers?
I was surprised to see a children's game is publicly traded. So there is no real incentive to protect children, just max extract as much money as possible.
russians been interfering with other countries through all possible channels for decades.
They integrated with every single Ukrainian election (including the most recent one in 2019), they tried hijacking (with various success) elections in Romania, Moldova, Lithuania, Czech Republic, Estonia, etc..
This includes radio, tv, printed periodics, social networks, even popular music, movies, tv series and books...
So how much are you willing to bet that this time it's definitely not russians?
I'm getting ai fatigue. It's ok to rewrite quick emails that i'm having brain farts on but anything deep it just sucks. I certainly can't see paying for it.
Weird because AI has been solving hard problems for me. Even finding solutions that I couldn’t find myself. Ie. sometimes my brain cant wrap around a problem, I throw it to AI and it perfectly solves it.
It is weird that AI is solving hard problems for you. I can't get it to do the most basic things consistently, most of the time it's just pure garbage. I'd never pay for "AI" because it wastes more of my time than it saves. But I've never had a problem wrapping my head around a problem, I solve problems.
I'm curious what kind of problem your "brain cant wrap around", but the AI could.
I'm curious what kind of problem your "brain cant wrap around", but the AI could.
One of the most common use cases is that I can't figure out why my SQL statement is erroring or doesn't work the way it should. I throw it into ChatGPT and it usually solves it instantly.
Yes. To me, it is. Sometimes queries I give it are 100-200 lines long. Sure, I can solve it eventually but getting an "instant" answer that is usually correct? Absolutely priceless.
It's pretty common for me to spend a day being stuck on a gnarly problem in the past. Most developers have. Now I'd say that's extremely rare. Either an LLM will solve it outright quickly or I get enough clues from an LLM to solve it efficiently.
You might be robbing yourself of the opportunity to learn SQL for real by short-cutting to a solution that might not even be correct one.
I've tried using LLMs for SQL and it fails at exactly that: complexity. Sure it'll get the basic queries right, but throw in anything that's not standard every day SQL into it and it'll give you solutions that are not great really confidently.
If you don't know SQL enough to figure out these issues in the first place, you don't know if the solutions the LLM provides are actually good or not. That's a real bad place to be in.
Have you ever read Zen and the Art of Motorcycle Maintenance? One of the first examples in that book is how when you are disassembling a motorcycle any one bolt is trivial until one is stuck. Then it becomes your entire world for a while as you try to solve this problem and the solution can range from trivial to amazingly complex.
You are using the term “hard problem” to mean something like solving P = NP. But in reality as soon as you step outside of your area of expertise most problems will be hard for you. I will give you some examples of things you might find to be hard problems (without knowing your background):
- what is the correct way to frame a door into a structural exterior wall of a house with 10 foot ceilings that minimized heat transfer and is code compliant.
- what is the correct torque spec and sequence for a Briggs and Stratton single cylinder 500 cc motor.
- how to correctly identify a vintage Stanley hand plane (there were nearly two dozen generations of them, some with a dozen different types), and how to compare them and assess their value.
- how to repair a cracked piece of structural plastic. This one was really interesting for me because I came up with about 5 approaches and tried two of them before asking an LLM and it quickly explained to me why none of the solutions I came up with would work with that specific type of plastic (HDPE is not something you can glue with most types of resins or epoxies and it turns out plastic welding is the main and best solution). What it came up with was more cost efficient, easier, and quicker than anything I thought up.
- explaining why mixing felt, rust, and CA glue caused an exothermal reaction.
- find obscure local programs designed to financially help first time home buyers and analyze their eligibility criteria.
In all cases I was able to verify the solutions. In all cases I was not an expert on the subject and in all cases for me these problems presented serious difficulty so you might colloquially refer to them as hard problems.
In this case, the original author stated that AI only good for rewriting emails. I showed a much harder problem that AI is able to help me with. So clearly, my problem can be reasonably described as “hard” relative to rewriting emails.
What happens when these "AI" companies start charging you what it really costs to run the "AI"? You'd very likely balk at it and have to learn SQL yourself. Enjoy it while it lasts, I guess?
I work with some very complex queries (that I didn't write), and yeah, AI is an absolute lifesaver, especially in troubleshooting situations. What used to take me hours now takes me minutes.
In my case, Learning new stuff is one place I see AI playing major role. Especially the academic research which is hard to start if you are newbie but with AI I can start my research, read more papers with better clarity.
Calculate the return on investment for a solar installation of a specified size on a specified property based on the current dynamic prices of all of the panels, batteries, inverter, and balance of system components, the current zoning and electrical code, the current cost of capital, the average insolation and weather taking into account likely changes in weather in the future as weather instability increases due to more global increase of temperature, the chosen installation method and angle, and the optimal angle of the solar panels if adjusted monthly or quarterly. Now do a Manual J calculation to determine the correct size of heat pump in each section of that property, taking into account number of occupants, insulation level, etc.
ChatGPT is currently the best solar calculator on the publicly accessible internet and it's not even close. It'll give you the internal rate of return, it'll ask all the relevant questions, find you all the discounts you can take in taxes and incentives, determine whether you should pay the additional permitting and inspection cost for net metering or just go local usage with batteries, size the batteries for you, and find some candidate electricians to do the actual installation once you acquire the equipment.
Edit: My guess is that it'd cost several thousand dollars to hire someone to do this for you, and it'll save you probably in the $10k-$30k range on the final outcomes, depending on the size of system.
My God, the first example is having an AI do math, then he says "Well I trust it to a standard deviation"
So it's literally the same as googling "what's the ballpark solar installation cost for X in Y area" unbelievable, and people pay $20+ per month for this
Where they agree it shows the data supports that answer - not necessarily that it is true, where they disagree it shows you need to hedge. That's useful.
I would recommend spending that "couple thousand" for quote(s). It's a second opinion from someone who hopefully has high volume in your local market. And your downside could be the entire system plus remediation, fines, etc.
To be clear, I'm not opposed to experimenting, but I wouldn't rely on this. Appreciate your comment for the discussion.
No I'm not relying on it in the sense of going out and running the entire project through it, but as an accurate screener for whether it's worth doing, there's nothing comparable available.
Well deep/hard is different I guess; I use it, day and night, for things I find boring. Boilerplate coding (which now is basically everything that's not pure business logic / logic / etc), corporate docs, reports etc. Everything I don't want to do is done by AI now. It's great. Outside work I use it for absolutely nothing though; I am writing a book, framework and database; that's all manual work (and I don't AI is good at any of those (yet)).
As an LLM-skeptic who got a Claude subscription, the free models are both much dumber and configured for low latency and short dumb replies.
No it won’t replace my job this year or the next, but what Sonnet 4.5 and GPT 5 can do compared to e.g. Gemini Flash 2.5 is incredible. They for sure have their limits and do hallucinate quite a bit once the context they are holding gets messy enough but with careful guidance and context resets you can get some very serious work done with them.
I will give you an example of what it can’t do and what it can: I am working on a complicated financial library in Python that requires understanding nuanced parts of tax law. Best in class LLM cannot correctly write the library code because the core algorithm is just not intuitive. But it can:
1. Update all invocations of the library when I add non-optional parameters that in most cases have static values. This includes updating over 100 lengthy automated tests.
2. Refactor the library to be more streamlined and robust to use. In my case I was using dataclasses as the base interface into and out of it and it helped me split one set of classes into three: input, intermediate, and output while fully preserving functionality. This was a pattern it suggested after a changing requirement made the original interface not make nearly as much sense.
3. Point me to where the root cause of failing unit tests was after I changed the code.
4. Suggest and implement a suite of new automated tests (though its performance tests were useless enough for me to toss out in the end).
5. Create a mock external API for me to use based on available documentation from a vendor so I could work against something while the vendor contract is being negotiated.
6. Create comprehensive documentation on library use with examples of edge cases based on code and comments in the code. Also generate solid docstrings for every function and method where I didn’t have one.
7. Research thorny edge cases and compare my solutions to commercial ones.
8. Act as a rubber ducky when I had to make architectural decisions to help me choose the best option.
It did all of the above without errors or hallucinations. And it’s not that I am incapable of doing any of it, but it would have taken me longer and would have tested my patience when it comes to most of it. Manipulating boilerplate or documenting the semantic meaning between a dozen new parameters that control edge case behavior only relevant to very specific situations is not my favorite thing to do but an LLM does a great job of it.
I do wish LLMs were better than they are because for as much as the above worked well for me, I have also seen it do some really dumb stuff. But they already are way too good compared to what they should be able to do. Here is a short list of other things I had tried with them that isn’t code related that has worked incredibly well:
- explaining pop culture phenomenon. For example I had never understood why Dr Who fans take a goofy campy show aimed in my opinion at 12 year olds as seriously as if it was War and Peace. An LLM let me ask all the dumb questions I had about it in a way that explained it well.
- have a theological discussion on the problem of good and evil as well as the underpinnings of Christian and Judaic mythology.
- analyze in depth my music tastes in rock and roll and help fill in the gaps in terms of its evolution. It actually helped me identify why I like the music I like despite my tastes spanning a ton of genres, and specifically when it comes to rock, created one of the best and most well curated playlists I had ever seen. This is high praise for me since I pride myself on creating really good thematic playlists.
- help answer my questions about woodworking and vintage tool identification and restoration. This stuff would have taken ages to research on forums and the answers would still be filled with purism and biased opinions. The LLM was able to cut through the bullshit with some clever prompting (asking it to act as two competing master craftsmen).
- act as a writing critic. I occasionally like to write essays on random subjects. I would never trust an LLM to write an original essay for me but I do trust it to tell me when I am using repetitive language, when pacing and transitions are off, and crucially how to improve my writing style to take it from B level college student to what I consider to be close to professional writer in a variety of styles.
Again I want to emphasize that I am still very much on the side of there being a marketing and investment bubble and that what LLMs can do being way overhyped. But at the same time over the last few months I have been able to do all of the above just out of curiosity (the first coding example aside). These are things I would have never had the time or energy to get into otherwise.
You seem very thoughtful and careful about all this, but I wonder how you feel about the emergence of these abilities in just 3 years of development? What do you anticipate it will be capable of in the next 3 years?
With no disrespect I think you are about 6-12 months behind SOTA here, the majority of recent advances have come from long running task horizons. I would recommend to you try some kind of IDE integration or CLI tool, it feels a bit unnatural at first but once you adapt your style a bit, it is transformational. A lot of context sticking issues get solved on their own.
Oh I am very much catching up. I am suing Claude Code primarily, and also have been playing a bit with all the latest API goodies from OpenAI and Anthropic, like custom tools, memory use, creating my own continuous compaction algorithm for a specific workflow I tried. There is a lot happening here very fast.
One thing that struck me: models are all trained starting 1-2 years ago. I think the training cutoff for Sonnet 4.5 is like May 2024. So I can only imagine with is being trained and tested currently. And also these models are just so ahead of things like Qwen and Llama for the types of semi-complex non-coding tasks I have tried (like interpreting my calendar events), that it isn’t even close.
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