Brains are resource hungry, especially oxygen hungry. Earth's air is orders of magnitude richer in oxygen molecules than its water. This likely made it easier for intelligence to develop on land. It's worth noting that the smartest aquatic animals are air breathing mammals that spent much of their evolutionary history on land before returning to water.
Both whales and prairie dogs turn out to have rather advanced degrees of verbal language capability, more complex than any of the Great Apes bar homo sapiens. Crows somehow culturally remember the face of an antagonist multiple generations later. Almost every highly social vertebrate has degrees of intelligence that would get you burned as a witch if you'd suggested it not too long ago, in the era when "Fishes clearly don't feel pain" was just a cultural default assumption.
I've heard that the biggest limiting factor in octopus ocean domination is their short lifespans. Tool use, building structures, communication, facial recognition, multiple brains, it's all there.
Gates famously came from a rich family, but Bezos did too - he used hundreds of thousands of dollars in investments from his immediate family members to get Amazon off the ground. Maybe 1 to 2% of Americans would be able draw that much from their family members if they were to launch a startup. If we define "bootstrapped" wealth as starting from an economic background within one standard deviation of the national average, then he doesn't count.
> While fact investigation continues, BAM’s current state of its investigation has uncovered significant evidence of gross negligence in how the store was previously operated by the prior owner
Verbal diarrhea. Excessive passive tense and vagueries to dilute blame (who is the "prior" owner here - Johnson or the woman before they've been trying to scapegoat?) and to top it off multiple grammatical errors in a single sentence.
I can't believe they would let this draft hit the website in its current form this in the midst of what could be an existential crisis for there company.
If you're looking for international precedent, this is an old vs. new world issue. Birthright citizenship is rare in the old world, but it is the default for the Americas. Canada, most of Latin America, and a decent part of the Caribbean have birthright citizenship.
I thought France had it. It turns out France, Italy, Netherlands, UK, and Belgium have a conditional deferred version: you can become a citizen at 18 if you're born and have been living there (plus a few caveats).
If you want to be genuine about investigating this issue you would obviously compare countries of equal wealth and economic power, but we all know that's not your goal. Could actually be a very interesting comparison if your reasoning weren't so motivated.
GME also beat the S&P 500 over the past 10 years. Is this evidence that Ryan Cohen is a business genius?
Tesla has been a meme stock for about five years now, maybe more. Its valuation correlates with Musk's abilities as a showman and media figure, not a businessman.
> GME also beat the S&P 500 over the past 10 years. Is this evidence that Ryan Cohen is a business genius?
GME did not beat the S&P500 over the past 10 years, and it is just the evidence of you needing to verify your claims before making them.
Over the past 5 years[0]: S&P500 up by 77%, GME down by 50%.
Over the past 10 years: S&P500 up by 260%, GME up by 207%.
GME performance in the past 10 years doesn't indicate that Ryan Cohen is a business genius. It indicates that he runs a company that has been underperforming the market for at least the past decade.
GME Jan 1, 2016: $7.09, $5.49 adjusted (accounting for dividend disbursements)
GME Jan 1, 2026: $20.09
266% or 365% return depending on how you count dividends. 365% for GME vs. 306% for S&P 500 over the same period (also using adjusted for dividend numbers).
For the previous 10 years, dividends reinvested, GME returned 14.59% per year, SP500 did 15.376% per year. Considering the much, much higher risk of owning a single stock, the risk adjusted return of SP500 is much higher than GME.
I consider myself AI skeptical-ish and I detest when people defend LLMs with "it's user error, prompt better," but in this case it actually is user error.
If you want a particular implementation approach, you need to specify not only the features you want, but the implementation strategy at least at a high level. This could be as simple as adding "use pywikibit" or "use relevant packages from pypi" to the end of your prompt. Or you could seed your project with some manually writtem scaffolding, including a pyproject.toml
While LLMs do tend have NIH syndrome by default, I think this is a good default. I'd much rather have tight control over when and how to include external dependencies as opposed to letting a prompt fire for 40 minutes, and coming back to find 2 GB of newly installed node packages with a dependency tree 300 levels deep.
Thanks! I will set a more detailed Claude.md next time. And an additional thing that kind of frustrates me is that even when I explicitly tell it to migrate to existing packages, Claude still stubbornly sticks to its custom rule set rather than replacing it with existing SOTA.
> But it’s getting harder and harder to define a task that humans beat LLMs on. On pretty much any easily quantifiable test of knowledge or reasoning, the machines win.
Quite to the contrary, I think it's extremely trivial to find a task where humans beat LLMs.
For all the money that's been thrown at agentic coding, LLMs still produce substantially worse code than a senior dev. See my own prior comments on this for a concrete example [1].
These trivial failure cases show that there are dimensions to task proficiency - significant ones - that benchmarks fail to capture.
> Is medical diagnosis one of these high judgement tasks?
Situational. I would break diagnosis into three types:
1. The diagnosis comes from objective criteria - laboratory values, vital signs, visual findings, family history. I think LLMs are likely already superior to humans in this case.
2. The diagnosis comes from "chart lore" - reading notes from prior physicians and realizing that there is new context now points to a different diagnosis. (That new context can be the benefit of hindsight into what they already tried and failed and/or new objective data). LLMs do pretty good at this when you point them at datasets where all the prior notes were written by humans, which means that those humans did a nontrivial part of the diagnostic work. What if the prior notes were written by LLMs as well? Will they propagate their own mistakes forward? Yet to be studied in depth.
3. The diagnosis comes from human interaction - knowing the difference between a patient who's high as a bat on crack and one who's delirious from infection; noticing that a patient hesitates slightly before they assure you that they've been taking all their meds as prescribed; etc. I doubt that LLMs will ever beat humans at this, but if LLMs can be proven to be good at point 2, then point 3 alone will not save human physicians.
> I doubt that LLMs will ever beat humans at this, but if LLMs can be proven to be good at point 2, then point 3 alone will not save human physicians.
Agree with your division but I'm baffled by this argument. If humans are better than machines at point 3 and can also use a machine to do point 2, then unless they have particularly terrible biases against taking point 2 data into account they're going to be strictly better than machines alone. Doctors have costs, but they're costs people/society are generally willing to underwrite, and misdiagnosis also has costs...
I disagree, I use metaprogramming in application code quite regularly, although I tend to limit myself to a single construct (instance_eval) because I find that makes things more manageable.
In my opinion the main draw of Ruby is that it's kind of Lisp-y in the way you can quickly build a metalanguage tailored to your specific problem domain. For problems where I don't need metaprogramming, I'd rather use a language that is statically typed.
The two are not mutually exclusive. On many occasions I've used C# to define domain-specific environments in which snippets of code, typically expressions, are compiled and evaluated at runtime, "extending the language" by evaluating expressions in the scope of domain-specific objects and/or defining extension methods on simple types (e.g., defining "Cabinet" and "Title" properties on the object and a "Matches" extension method on System.String so I can write 'Cabinet.EndsWith("_P") || Title.Matches("pay(roll|check)", IgnoreCase)').
I don't think instance_eval is too nasty. The toughest "good" codebase I've worked in was difficult because it used method_missing magic everywhere, which built tons of methods whose existence you had to just infer, based on configuration stored in a database. So most method calls could not be "command clicked" or whatever to jump to their definition, because none were ever defined.
I know this is tangential to your overall point, but did really they murder everyone in the room? I was under the impression that a few Venezuelan generals kidnapped Maduro themselves, left him at a predetermined point for US forces to pick up, and had their soldiers fire some small arms into the air to make a token show of resistance. There's no way the US would have flown a slow-moving convoy of helicopters into a hostile city unless they knew a priori that Venezuelan air defense missile batteries would be ordered to stand down.
I agree there was almost certainly some collaboration with some factions in Maduro's military standing down for the mission to go so smoothly, but its pretty well-established that a number of soldiers were killed, with some US soldiers coming back with the wounds to show for it. The entire bodyguard being killed is something the US and Cuba actually agree on!
Who knows what's true, but the official US narrative is that they entered his bunker, slaughtered the (mostly Cuban) security guards, and stopped Maduro just before he could hide behind a reinforced door. So the official narrative is indeed that US forces slaughtered a bunch of people and took Maduro.
Whether there was also cooperation from the Venezuelan military, failure to shoot down helicopters, etc, is a different matter.
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