Food groups seem like such a strange way to quantify this, especially given that production of several of the food groups is a net macronutrient destroyer.
The macronutrient story is far more telling. For instance my math says (based on 17B bushel annual production) that the US produces 11,400 calories and 250g protein per person, per day, just in corn. The vast majority of this is used for animal feed and ethanol.
Whether resorting to eating just corn and multivitamins is a good life could be debated, but it's silly to suggest (as the paper figures do) that the US has a food security issue.
Couldn't some of the use cases presented for this be accomplished with ZSETs? I get the performance angle, but it seems that this could have been accomplished without the new API surface by selectively optimizing ZSET storage for dense values (in the same way that Arrays selectively use sparse representations).
The RE component is interesting, but as commentary here has noted it seems orthogonal to the array data structure (i.e., usable on others as well). Does this not make more sense to accomplish with Lua scripting? Or if performance of Lua is an issue perhaps abstracting OP to be composable on top of any command that returns a range of values.
I say this with reverence for Antirez as the expert in this space, but some of this new feature set feels like the sort of solution that I tend to see arise from LLM-driven development; namely creation of new functionality instead of enhancement of existing, plus overcomplicating features when composition with others might be more effective.
Unfortunately not, sorted sets are actually a bit in the other side of the spectrum: they are semantically sound, but absolutely wasteful because of the combined skiplist + array. Also, if the underlying representation is not an array, range queries and ring buffers will never be as efficient and compact as they should. In theory you can do everything with everything, but segmenting what each API can do allows you to exploit the use cases to provide the best underlying implementation.
Oh boy, you are far from what it requires, we are probably talking 3B+, but note that this is just codex, obviously codex is also doing automatic adversarial with the regular zoo (gemini-3.1-pro-preview, opus-4.6/4.7, gpt-5.3-codex, minimax-2.7, glm-5.1, mimo-2 (now 2.5) and so-on, you get the gist) :)
I don't know their margin so I can't really say, but do we have 8 OpenAI accounts, I doubt they are making that much with us seeing that there isn't a single hour where we don't saturate the accounts.
Indeed; sorry for leaving that out, it was a judgement call triggered by HN limits. However, whilst very relevant, it doesn't in fact change the point that much.
Off by an order of magnitude. Average TBO (which airplane engines routinely exceed if they don’t rust out) is 2,000 hours assuming piston, or about 300,000 miles for a Piper Arrow at cruise speed.
Thanks for clarifying, I thought that sounded wrong - otherwise aeroplane engines would have to be "rebuilt", each and every time, after more than half of all international flights in and out of Australia (5000 miles, aka 8000km, is just down the road to grab a sausage roll for us!).
For comparison, latest commercial turbofans approach 6000h (they don't have a strict TBO limit AFAIU, overhauls are decided based on various inspections and measurements). At a typical airliner speed that's something like 3,000,000 miles.
Going to have to disagree on the backup test. Opus flamingo is actually on the pedals and seat with functional spokes and beak. In terms of adherence to physical reality Qwen is completely off. To me it's a little puzzling that someone would prefer the Qwen output.
I'd say the example actually does (vaguely) suggest that Qwen might be overfitting to the Pelican.
Qwen's flamingo is artistically far more interesting. It's a one-eyed flamingo with sunglasses and a bow tie who smokes pot. Meanwhile Opus just made a boring, somewhat dorky flamingo. Even the ground and sky are more interesting in Qwen's version
But in terms of making something physically plausible, Opus certainly got a lot closer
The fundamental challenge of AI is preventing unprompted creativity. I can spin up a random initialization and call all of it's output avante garde if we want to get creative.
I recently fell down the rabbithole of AI-generated videos, and realised that many of the "flaws" that make them distinctive, such as objects morphing and doing unusual things, would've been nearly impossible or require very advanced CGI to create.
"artistically interesting" is IMHO both a subjective and 'solved' problem. These models are trained with an "artistically interesting" reward model that tries to guide the model towards higher quality photos.
I think getting the models to generate realistic and proportional objects is a much harder and important challenge (remember when the models would generate 6 fingers?).
Even the first one - Qwen added extra details in the background sure. But he Pelican itself is a stork with a bent beak and it's feet is cut off it's legs. While impressive for a local model, I don't think it's a winner.
I've been using the same Patagonia Black Hole backpack for nearly a decade, every day include bike commutes (and crashes). The longevity is frustrating at this point because I'd really like a larger one but can't bring myself to switch until this one fails (which may be never).
I don't see how it could be a surprise to anyone paying attention that a JanSport backpack doesn't deliver on quality. Perhaps there's more to the story but I got to the second AI slop one-liner and gave up.
Strong disagree on Pinnacles being underwhelming. The California condors acord woodpeckers and alone are worth the visit. The caves are also very cool if you go when they're open.
Well, after changing the ssh port to something really big, OOM and heavy CPU usage stopped, as I was still using that public IP, so concluded it was not an inside job .
There were like thousands of requests in an hour, and that went on continuously, before I changed the port.
The macronutrient story is far more telling. For instance my math says (based on 17B bushel annual production) that the US produces 11,400 calories and 250g protein per person, per day, just in corn. The vast majority of this is used for animal feed and ethanol.
Whether resorting to eating just corn and multivitamins is a good life could be debated, but it's silly to suggest (as the paper figures do) that the US has a food security issue.
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