Pantone is not a color space. They are a supplier of physical color samples. Most pigments can be represented in a color space, but that representation differs based on illumination so it not unique. Some Pantone pigments, fluorescent colors in particular, cannot be represented by a color space at all!
Yup. This is a rabbit hole like time. Learn a little bit about it, know there's a staggering amount more and then any time you actually need it, check that you've agreed on as much detail as you needed for the task and not less.
The person inviting you to a picnic "next week" might need to know about Time Zones if they live some distance away. The requirement to update the CSS on the web site to match a new company logo might need to know about Colour Spaces. But that picnic invite doesn't care about Calendar systems or Leap Seconds, and the logo mandate doesn't care about Sub-surface scattering or a Quantum theory of light.
I think you'd want to remove e.g. knowledge of harry potter universe and ancient egypt. Training on a bunch of high quality java code bases is still likely to improve your python model.
Actually, it's shown that even general knowledge helps coding models because their input is natural language itself so they need to understand it well enough to even turn into code.
To me it seems like a first-year physics scaling laws problem. To get linear improvements in capability, you appear to need need exponential (or at least superlinear) increases in model size. We have no technical nor business solution for that kind of scaling, so the long-term outcome is obvious.
> Imagine if you were given a layer of abstraction that randomly flipped bits from time to time... That's not an abstraction, it's random programming.
Computing has never really been deterministic all the way down. Storage gets corrupted, RAM has soft errors, networks drop/reorder packets, schedulers race, caches go stale, distributed systems partition, query planners change plans, ...
Obviously those become tolerable because we've developed layers of contracts and understanding the bounds around them. Error correcting codes, checksums, retries, consensus, idempotency, false-positive rates, SLAs, etc.
If the abstraction is “delegate this task to a junior engineer, analyst, lawyer, designer, or support rep,” you dont expect deterministic behavior. There's review, constraints, escalation, checklists, tests, and accountability.
> defence - why would anyone buy US weapons after Greenland and Canada
Huh? US foreign military sales are up at all time highs
"Total exports by the United States, the world’s largest supplier of arms, increased by 27 per cent. This included a 217 per cent increase in US arms exports to Europe, according to new data published today by the Stockholm International Peace Research Institute (SIPRI)"
Aren't a lot of these purchases (a) locked in from contracts made pre-admin; and (b) otherwise largely related to anxiety over Russia? There are too many confounding variables here.
The admin has also said that many of those arm shipments may be delayed by years because the US first needs to restock after blowing their inventory in Iran. So, sign a deal for US weapons which may not be delivered as agreed or may be denied the latest required software patches. I expect many countries to look elsewhere for future armaments.
Exactly, but Hacker News is upvoting this because it wants the US to be seen as the loser of this conflict.
Both sides in a conflict (or any negotiation) make demands that they know the other will not accept. You can't just take someone's list like that and assume that'll be the exact outcome.
As does this one. The 10 points aren't the agreed-upon terms, though. The agreed-upon terms are: stop bombing for two weeks, and open the straits for two weeks.
hackers are often leftwing sweaty tryhards, obviously not all of them ;) but whatever, let them suck on those circumsized penis while the local paki rape gangs rule the streets of europe.
It's been like this since GPT 3.5. This is not a limitation and is generally considered a natural outcome of the process.
So there's no major update in the sense that you might be thinking. Most of the time there's not even an announcement when/if training cut offs are updated. It's just another byline.
A 6 month lag seems to be the standard across the frontier models.
I've actually started worrying that the amount of false data produced with LLMs on the public internet might provoke a situation where the knowledge cutoff becomes permanently (and silently) frozen. Like we can't trust data after 2025 because it will poison training data at scale, and models will only cover major events without capturing the finer details.
I agree. That's why you should write as much as you can now, if you want to get it into the LLMs (https://gwern.net/blog/2024/writing-online). You never know when the window will slam shut and LLM training goes 'hermetic' as they focus on 'civilization in a datacenter' where only extremely vetted whitelisted data gets included in the 'seed' and everything is reconstructed from scratch for the training value & safety.
Apple sold something like 60M ipads in 2025. Perhaps you are not the target audience
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