3,189 karma · joined June 26, 2023
The bill for the API usage was _hundreds_ of times higher than the subscriptions. Companies with a few hundred engineers, but without sky-high margins, simply cannot afford not to understand their codebases.
I think most normal people have tried Doritos at some point, which are the flavour-equivalent of what you’re describing here. It’s food designed to satisfy your human needs and desires.
If you're already using Blender to make assets, there's some benefit to stuffing Blender's UI into your game editor too.
They're going through an IPO, so unless this is a one-off thing, people reading this thread will become shareholders, either directly or through their pension pots.
Do you have a source for this? Because the implication is that my Samsung TV will connect to my neighbours Samsung TV, and send my PII through their network without my consent, which would be a huge deal in Europe.
They seem to struggle to reason about the boundary between the two.
It’s not two distinct people - or at least, I’ve seen no evidence that it’s two sets. The venn diagram is a single big fat circle, and we’re all in it.
The vast majority of signal is from a plurality of opinions, and the outcome is an average of those inputs, usually tweaked a bit to make them more addictive.
Your thought that “the algorithm doesn’t serve me personally because I have exceptional taste” is not a unique observation. Everyone who consumes these systems has the same thoughts. Everyone is a rounding error, whether you’re in a knitting community, or you’re a worlds leading expert in deep learning.
Best of luck to Graham. I've got no idea how they'll make CSS-Tricks work as an indy blog in this era, but I hope they do make it work.
If this is one of Anthropic's guiding principles, then I must assume that they've only ever promised five 8s of reliability.
Google Workspace revenues are something like $25bn/q, and that's the entire workspace division, (including enterprise gmail sales, so the productivity suite is meaningfully smaller than that figure). It's taken Google a long time to carve that slice out of Microsoft's entrenched market position. It's not small money, but they'd basically be going to war with two of the largest and most profitable companies in history on their turf.
I don't think they can credibly take that business plan to the market and say it'll be worth $1.75tn. They're all-or-nothing on being in the model business, and everything else is superfluous.
There’s lots of applications where it’s not being deployed at scale. Also lots of cases where someone who knows they need something classified does not know what a classifier is.
>What's the difference between a model that predicts intelligent output, and intelligence itself?
It doesn't directly attack the question, but gives a neatly constrained thought experiment which you can see running.
I get that maintenance teams need to have conviction in this approach, but this is definitely going to cause catastrophically large data breaches downstream.
You can make one trip per day with a night-train, whereas a seated train runs 16-20hrs per day. So again, fixed cost of the train existing is shared between far fewer ticket sales.
Then you need to store the train during the day, and you need to securely park the train when it arrives at its destination at night (a normal train yard isn't secure enough if you have a hotel full of people, and a train station usually needs all of its platforms).
For example, the “social workers & care -> counsel parents with child rearing problems” category is considered “well covered” because there are 13 prompts.
It’s self-evident that the spectrum of work carried out by a social worker undertaking that work cannot be well represented by thirteen prompts.
I suspect if you really strictly defined “covered well”, to the extent that any normal person would use that phrase, the headline would be above 99.9%.
Error messages are mostly useful; compilation is quick (relative to an LLM); memory is not really optimal in most cases, but it's "good enough" and the language puts up a lot of guardrails around the programmer to keep them on the path to accidental success in this regard; the training data is full of .NET code; there's a well defined set of ways of working & best practices with .NET; types are explicit.
In short, nobody can commit to, change, or meaningfully adjust this repository while adhering to its license.
While I agree that computers cannot currently be held accountable for decisions (because I do not think they have free will), I think it’s transparently clear that computers make decisions all the time, and have done since they were first implemented.
For example, I can decide which cereal to purchase at the supermarket. But I cannot decide which cereals are offered to me at the supermarket. The supermarket has allowed/enabled to make a constrained decision.
People who can find things of value, and execute on those things, are worth investing in - even if that thing of value is replicated in short order.
Nobody was looking at decision models, but now that Jev has surfaced, everybody is.
I think people are focusing on it because if you've built your business on Deno, then the "Deno will have no support in 13 months time" is a bit of an existential risk, and will be a huge time-sink for your team. So it's far more interesting to most of the people reading this page on HN, because HN is full of people who are first-adopters.