It's not that you're prompting it wrong. It's that you're judging the output against a standard (human intelligence) that just isn't relevant--no matter how much we want it to be and no matter how much the fluency of the output tricks us into thinking there's a human-like mind behind it.
Now granted, if the boat salesmen were pushing hard on the idea that the boat would fly and even put little wings on the side and I bought the boat I might get really angry when I found out that it didn't fly. And I might angrily storm into the salesroom yelling about how the design is defective. But if someone pointed out 'hey, it's a boat perhaps you should stick to sailing around in it and stop getting your undies in a bundle about it not flying' the correct response is probably to take a closer look, ignore the salesmen, and cruise around the lake. LLM's are quite handy at some things and have some weird limits. Learn the limits, enjoy your time at sea.
You have misunderstood what structural racism is. It is not about the majority of people being racist. Is about the systems being constructed in ways that lead to racist outcomes. You can have a society with zero racist individuals and if they continue to enact the racist systems (perhaps created by racist folks long dead) you'll have structural racism. I don't disagree with the idea that the mis-understanding you have is widespread though, and would certainly be a cause for folks not being comfortable with the idea (as they have mis-understood it).
It's like you're making the best you can of the current situation you find yourself in as an individual while also working toward changing the overall situation.
Or you point your Claude code at a different LLM provider. It's not complicated and there are lots of vendors (and in the open-weights space multiple vendors serving the same models competing on price). Sure DeepSeek 4 isn't quite Opus at the moment. But it's plenty good to do the work. We've got different competing front-end tools and different competing back-end providers. No one 'owns' your company. Maybe that will change as the market evolves and one of the frontier tools become so much better than one vendor will own the market. But that's not where we are now.
I just put the terribly generic query "what tools would you recommend to integrate fraud prevention or account takeover protection into my product" into both Claude (Sonnet) and Gemini (3.1 Pro) via the standard web interface and both took the first step of searching the web. That's consistent with my past experience -- the usual harnesses typically will search the web in cases where I might expect/want them to. Now whether you product has good web visibility or not in those searches and how the LLM's weigh the relative merits of open-source tools versus commercial offerings in deciding what to highlight in their responses is a different issue. As is the change in what constitutes effective SEO in an era where bots, rather then human eyes are the proximal important target. But I don't think the core issue with folks finding your products is the move away from user-driven search toward using models with out-of-date training cutoffs.
FWIW while neither model included your product in it's initial response, when I followed up with "what about open-source" both did another search and Claude's response included your tool....
In the US around 26 million people have no form of health insurance. These same people are unlikely to be able to afford a 'simple' gall bladder ablation out of pocket. Which implies an effectively infinite wait time. What's crazy is that some people think this is normal.
Strength training can be done carefully with correct motions. Team sports with unpredictable dynamic movement not so much. Not to say you shouldn't engage in these, at any age, and that they have positive health benefits. They just aren't as safe as strength training for folks at the age where this is all relevant.
Tell me more about this scheme. If there's someone who imagines they can tax EV's more than ICE vehicles what exactly is keeping them from just making the same increase (now) on ICE vehicles? If their secret goal is to raise transportation taxes how does switching their target from ICE vehicles to EV's make that any easier? And who exactly is doing the scheming? Is it construction firms who build roads (which is my neck of the woods is where most of the gax tax ends up going). Are they the ones hatching this scheme? You'd think they'd be lobbying harder for more trucks (heavily vehicles -> more wear on the roads ->profit!). But the more big trucks people seem mostly to be the opposite of the EV people. How confusing.
I think we have lots of evidence that the single binary question "is this something people like 'us' support or not" is the only deciding factor in a lot of political decisions people make. They don't consider the facts of the particular issue and how it might impact them. They abdicate that role to whomever they believe defines what 'people like us' believe.
Right. The number is the result of Claude adding up the public information about the aggregate value of all those clubs plus the association. So it would mean buying all the clubs; or at least enough to have a controlling interest in the association. Clearly there are big challenges to that (e.g. clubs not being for sale for one). But I thought it was an interesting thought experiment. Of course if you're just trying to play the money = power card then it'd probably be cheaper to purchase the influence of some government officials.
Not to be pendantic (but to be pendantic) 80psi is the correct pressure for 28mm tires ridden briskly on good roads. At least according to ye olde Silca tire pressure calculator. Back in the day when folks ran 23mm tires they would typically run above 100psi (though that may not have been optimal...).
I'm pretty sure there are folks involved in doing drug testing for many sports so saying are doing nothing seems hyperbolic. Are there specific things you think the bodies in charge of drug testing should be doing but aren't? Genuinely curious.
Not sure how this helps. Olympic events already have relative rating systems that ranks all the participant: pretty complicated and sport dependent systems that determine qualification for the games and competition amongst all the competitors at the games. The problem how to have separate competitions for different groups of participants when there isn't a universally shared agreement on who should be in which group.
Any tool that auto-updates carries the implication that behavior will change over time. And one criteria for being a skilled professional is having expert understanding of ones tools. That includes understanding the strengths and weaknesses of the tools (including variability of output) and making appropriate choices as a result. If you don't feel you can produce professional code with LLM's then certainly you shouldn't use them. That doesn't mean others can't leverage LLM's as part of their process and produce professional results. Blindly accepting LLM output and vibe coding clearly doesn't consistently product professional results. But that's different than saying professionals can't use LLM in ways that are productive.
Even if one-shot LLM performance has plateaued (which I'm not convinced this data shows given omission of recent models that are widely claimed to be better) that missing the point that I see in my own work. The improved tooling and agent-based approaches that I'm using now make the LLM one-shot performance only a small part of the puzzle in terms of how AI tools have accelerated the time from idea to decent code. For instance the planning dialogs I now have with Claude are an important part of what's speeding things up for me. Also, the iterative use of AI to identify, track, and take care of small coding tasks (none of which are particularly challenging in terms of benchmarks) is simply more effective. Could this all have been done with the LLM engines of late 2024. Perhaps, but I think the fine-tuning (and conceivably the system prompts) that make the current LLM's more effective at agent-centered workflows (including tool-use) are a big part of it. One-shot task performance at challenging tasks is an interesting, certainly foundational, metric. But I don't think it captures the important advances I see in how LLM's have gotten better over the last year in ways that actually matter to me. I rarely have a well-defined programming challenge and the obligation to solve it in a single-shot.
Seems like the ability to distinguish LLM versus 'good human' writing depends on the size of the writing sample you have to look at (assuming you think it can be done). And that HN-scale posts are unlikely to be a long enough for useful discernment.
Please expand more on the idea that LLM's are not trained on English to begin with. Not sure what you mean by this as clearly many LLM's are trained on data that contains a lot of English. For instance GPT-1 seems to have been trained on a purely English corpus.
And there's 'nothing wrong' with just writing code with variables named 'a1, a2, a3'. But when some poor sod has to dig through your mess to figure out what you had in mind it turns out that having an easier to discern logical structure to your code (or html) makes it better. I've dug through a lot of html. And there's a ton of ugly code smell out there. Layers and layers of "I don't really know what I'm doing but I guess it looks okay and I'll make it make sense later". I'm sure it pays the bills for someone. But it makes me sad.
Seems like if evolution managed to create intelligence from slime I wouldn't bet on there being some fundamental limit that prevents us from making something smarter than us.
I'm pretty sure the Luddites judged the threat the machines posed to their livelihood to be a greater damage than their employer's loss of their machines. So for them, it was an easy justification. The idea that dollar value encapsulates the only correct way to value things in the world is a pretty scary viewpoint (as your reference to the value of saving a life illustrates).
I will find this often-repeated argument compelling only when someone can prove to me that the human mind works in a way that isn't 'combining stuff it learned in the past'.
5 years ago a typical argument against AGI was that computers would never be able to think because "real thinking" involved mastery of language which was something clearly beyond what computers would ever be able to do. The implication was that there was some magic sauce that human brains had that couldn't be replicated in silicon (by us). That 'facility with language' argument has clearly fallen apart over the last 3 years and been replaced with what appears to be a different magic sauce comprised of the phrases 'not really thinking' and the whole 'just repeating what it's heard/parrot' argument.
I don't think LLM's think or will reach AGI through scaling and I'm skeptical we're particularly close to AGI in any form. But I feel like it's a matter of incremental steps. There isn't some magic chasm that needs to be crossed. When we get there I think we will look back and see that 'legitimately thinking' wasn't anything magic. We'll look at AGI and instead of saying "isn't it amazing computers can do this" we'll say "wow, was that all there is to thinking like a human".
Science is distributed. Lots of researchers at lots of different institutions research overlapping topics. That's part of its strength. In the U.S. most basic research is funded by federal grants. And as a results you'll find that research in pretty much any science area you can imagine is funded by federal grants going to multiple different institutions. In this case you're confusing things by bringing in NOAA which is a government agency (part of the Dept of Commerce). NCAR is a non-profit organization and competes for federal grant dollars with researchers at many other institutions (mostly universities). So in that sense there is a strong parallel here to Trump wanting to shut down Harvard (another non-profit organizations at which many different researchers work) and someone saying "doesn't Stanford do research on similar topics?" Yes, there is some conceptual overlap, but in detail there is not. The bigger difference is that Harvard has a big endowment and so can survive (at some level) if the federal grants it has been getting stop flowing. NCAR can't. Also, NCAR happens to have the experts and equipment (supercomputers) to do research that few other organizations can (none really in the U.S.). Harvard probably can't lay claim to that except in very narrow niches....
For perspective the annual budget for NCAR is about half the amount being spend on the new White House ballroom.
The multiple meanings of many of the words in this sentence make it really poor at communicating what the site is about. "Endeavour" (with a capital 'E') is a proper name I associate with a space shuttle, and 'stellar' can mean 'having to do with stars'. So a first read for me leads to the conclusion that this site has something to do with space flight. And 'system' could mean almost anything. Maybe this site will let me personalize my own star system? All I can take away is that I'm not sure what this is, but clearly I'm not the target audience. Which I'm fine with.....
Or more likely the 'whole' accesses the stable bit through some interface. The stable bit can happily keep doing it's job via the interface and the whole can change however it likes knowing that for that particular tasks (which hasn't changed) it can just call the interface.