The US has proved it can do horizontal scaling, as the majority of adults gets a college degree.
The horizontal capacity is ineffectively used by having many degree mills and low-value degrees, but if we re-tooled the knowledge sets we could raise the bar.
If you filter for younger, 25-34, and for employed, it's closer to 50%. For employed young women, it's the majority (for them, if you count associate degrees, it shoots up close to 75%).
Not OP but I've tried using all the major frontier models to find niche items in a field that I collect. The models get confused and hallucinate items that sounds complete reasonable, but don't actually exist. Usually it's an amalgamation of several real products.
My prompt is akin to "recommend <item type> with <niche criteria>". The first 3-ish results are about right, and then 7 of the next 10 are hallucinations and the LLM clearly can't throw up its hands and say "I got nothing".
I'm sure this is a hard problem because of a) how many items there are, b) how much overlap there is between product names, descriptions, manufacturers, different versions of the same product, etc, so keeping them distinct in the model's memory is probably hard, and even worse if it is dynamically fetching and summarizing content then it will be very easy to conflate different items, and c) LLMs are known for not working well on the edge cases with few examples.
I read Google's business incentive as "in it to not lose". AI is a new consumer endpoint and Google currently captures a lot of the endpoints.
> I think HN skews coding agent focused
100% this. Coding agents are an interesting test ground, but the people who care about them are a fairly small bubble.
I think they carry some of the overall AI weight because of the "what if you can vibe code your entire business" moonshot, but that's still orders of magnitude away and who knows if today's coding agents will actually be a stepping stone to that. If we ever get there it will likely be with entirely new domain languages.
I've learned this applies to a lot of life. Being a good manager, tech lead, consultant, etc, advisor, parent, friend, etc, is sometimes just half being a good therapist and helping them regulate.
I've watched a lot of "not officially financial advice" finance videos on YouTube (the solid people, not grifters), and while the financial theory side is interesting, when they talk about pragmatic investing and patterns of client behavior they have dealt with professionally, a large part of it is emotion management. Convincing clients to stick with a solid plan even when this month is abnormally bad, or avoid going all-in on the latest hotness, etc.
Go seems like a pragmatic fit, even though it was obviously not designed for LLMs and nor is it purely functional.
I review a lot of Go code, which means I review a lot of LLM Go code. Even though the human vs LLM authorship distinction has strong signals, Go's simple nature seems like a useful constraint on how LLMs can express themselves.
- Too verbose, too much noise e.g. error handling.
- Type system is too concrete, can't express sets or unions.
- Not much support for functional programming except passing closures
- Can't express immutability. Well, there's const, but it's crippled
These things mean Go falls short of what GP wants.
But Go has very distinct and (honestly kinda weird) design goals, it isn't really supposed to be "the simple applications language". It's supposed to be "C with NewSqueak", very much a systems programming language. You see that in its aggressively concrete type system
I think one of the key differences is that math is abstract whereas CS is relatively concrete.
CS examples are often easy to picture and understand the motivation for. You can use tools to visualize or play around with them and test them.
Math gets abstract so fast you have to spend a week of research to even understand the problem statement. The the motivations themselves can be completely unclear until you have a lot of context.
I majored in math (B.S.) and upper level math is completely foreign to me.
I think so is upper level CS, there are fields in CS that are foreign to me too, there is a lot of depth in CS, computing is a very deep field for instance ML research although may seem simple isn't quite so intuition based as people make it out to be. Similarly there are dozens of topics where sophisticated research happens where we don't interact with at all as regular software developers.
Every slice has so much depth to it, in Maths it all seems like all of it is required at once but in computing it feels like so little is needed to get started which I honestly feel like is failure of our modern education systems.
But yes Computers being so easily accessible and compilers, documentation and libraries have made computer science so easy to get started with.
Imagine having to implement your own network layer to communicate with someone, you would have had to understand ip, tcp, network layer to an extent like http and etc. and then you finally would have been able to communicate.
In maths that's our reality for a lot of the field, there aren't good libraries, interfaces to help skip the unnecessary details. Hopefully AI might solve it I don't know though. It's fun to hope for it.
A human reader for a popular product is generally a paid actor who reads the script nearly verbatim, not a domain expert, so it has no bearing on the technical quality of the script.
You may be correct, but from a user experience and product standpoint, you'll never get around the fact that if something doesn't feel real then it will also feel less quality and thus less valuable. Whether you like this about the human experience or not is irrelevant, since they are selling something they expect humans to use and buy, and one would hope they'd want their customers to come away from it feeling like they got their money's worth.
because if its human narrated, the likelihood of it being up-to-date drops because _updating it after changes costs actual money_, whereas the ai naratted likely only needs to have its script adjusted to kick of the generation pipeline.
it really depends on how well its actually implemented imo - and i have no idea how well this particular case is in parctice, as i've never worked with perforce.
nonetheless, charging for such a video is kinda incredible. i was just adressing the difference between human anrrated and ai narrated.
I mean, yeah, if I'm going off the cuff it seems like a scam either way. What you describe is just regular product stuff, AI doesn't change that much here. Though, what I do see that is different from the usually is companies attempting to cut corners but not reducing prices. They're keeping prices high that may have reflected a cost to production and that has suddenly taken a nose dive.
I can see them as a shared fate for a couple reasons:
* the obvious one is Elon - both valuations are largely propped up on belief in Elon. Whenever he falters, his companies that are speculation-based (all of them) will take a hit
* Elon pitched SpaceX as an AI company. Tesla needs better AI because they keep sending signals that they won't be at L5 anytime soon, and Tesla's valuation is still very speculative[0]at least in part due to the race to L5 autonomy. i.e. Tesla will need better AI , and SpaceX is that natural fit (on paper, at least, I'm not sure SpaceX has any useful AI for any use case, let alone self-driving).
[0] Tesla's PE ratio of is still 30x massively out of line with it's actual earnings and ~30x the American automotive industry.
> Tesla needs better AI because they keep sending signals that they won't be at L5 anytime soon.
China now insists that self-driving cars be "SAE level 3.5" if they let the driver take their hands off the wheel. "If the driver fails to respond within the specified timeframe or is physically unable to take control (e.g., due to unconsciousness), the system must automatically initiate a Minimal Risk Manoeuvre (MRM). This includes the ability to change lanes and park the vehicle safely in a location that does not obstruct traffic, while minimising risks to passengers and other road users."[1] That takes effect in China July 1, 2027. Mercedes Drive Pilot is close to this level. Tesla, not even close.
That's probably the right answer in the assisted self driving space.
And since I won with an age, company, and title that is literally me right now... Well, I'm not exactly sure what to do with it information but some internalization is in order.
