“State’s rights” the conservative small government supporters shouted for.
States attempt to hold a company accountable for giving its citizens cancer.
Not like that the conservative Supreme Court says.
Either the conservative movement lacks internally consistency, or it is quite consistent but around one central idea, corporations (that are willing to bend the knee to the government, see anthropic for a cautionary counter example) should be unrestrained from seeking profit.
Even the original states rights arguments around slavery were about how private enterprise shouldn’t be restrained from utilizing chattel slavery to extract profits. Seen through that lens the ruling makes perfect sense, if deferring to a big federal rule is best to protect profits, then big government. If deferring to states causes a race to the bottom ensuring profits, then states rights it is.
I hear they have real loose permitting regulations on Mars, and I think Elon was supposed to have a colony up and running about a decade ago, so that should all work out for him.
Yea, but if I can get a ChatGPT-like experience from Siri AI for free, why would I pay OpenAI.
Now it remains to be seen if Siri AI will deliver anything close to a ChatGPT-like experience. But if they did, for the consumer segment that isn't using LLMs for agentic work and just ask it questions from time to time, I can't imagine one textarea has engendered some huge amount of brand loyalty over another.
There will be a generation of children who will grow up and look back at their childhood photos and wonder if they ever really happened.
I also laughed out loud when they are showing the "cleanup" tool and they guy is talking about removing "distractions" and then removes 2 of the 3 girls juggling and having fun.
Ah yes, those friends you were forming core memories with, or as our tech overlords call them, distractions.
I like OCaml, I spend most of my day to day though in Elixir and I think one of the things that's so nice about elixir is mix.
There's a handful of mix commands you learn when you get started and it's such a great experience. You can crack open erlang application structure and learn more if you want, but if you just want to `mix compile` `mix deps.get` `mix test` that's also fine.
When I first learned ocaml I watched this really wonderful series https://www.youtube.com/watch?v=MUcka_SvhLw&list=PLre5AT9JnK... (highly recommend if you are at all interested) and it's great for learning the language and tooling but it's all opam up until the end when some of it switches to dune.
I think wanting to provide more details about what's going on is nice too, but I think there's a place for "here's the commands you will actually need in your day to day"
Yea, all the new advice around using dependency cooldowns only works if _someone_ is installing these things before you and finding the vulnerabilities.
It seems like the advice right now is to become a freerider while there are still people installing closer to release that will do free work for you finding out there's something nasty in the release.
Once everyone is waiting 2 weeks to install an update, then the value of everyone waiting goes down dramatically.
I've spent the last decade in erlang / elixir / OTP. I think a lot of the naming comes from the early use of erlang as effectively an "OS" for telecom switches.
I always joke that BEAM wants to be the operating system.
For the last 15 years you could take a picture of a lightbulb and pop it in google search and it would tell you what kind it was.
I know because I bought a house in 2013 where the builder delighted in using a dozen weird fixtures and the cheapest bulbs they could find and I spent a lot of 2015 doing just that.
There are lots of things that LLMs are genuinely good at, searching by image isn't something we need LLMs for. I asked Google's LLM when google image search launched and it reported
> Google officially launched its "Search by Image" feature—allowing users to upload a picture or image URL to find related content—in 2011
I don't use much AI, but we have an AI agent that reviews all of our PRs at work.
It's pretty good, I actually like it because it's very thorough and catches real things.
On a recent PR though it very confidently pointed out an "error" and suggested a "fix." Now I authored this code by hand and the "error" was it going "this one doesn't look like all the other ones" and I'm a relatively consistent person so it's not the kind of mistake I would make, which means I probably put thought in and the difference was intentional.
I looked at it's suggestion carefully and my original code was correct, the "fix" would have broken the logic. Not a huge deal all things said.
But I'm looking back at it's original report, it's comprehensive, confident, and ends with "Reply 'fix this for me' and I will fix it" and it made me think about more junior engineers.
I double checked it because I had written the code by hand, I understood the context, I also have enough experience to know that if I wrote this one function differently there was probably a good reason. But if I were earlier in my career, with less experience, would I have just clicked the easy button?
Probably. Especially if everyday I'm clicking the easy button.
Highway engineers used to think it was a good idea to make highways as straight as possible, people going in a straight line is easiest right? They realized that if you didn't put some curves in the road that people would just disengage and a perfect straight "easy" highway was much more dangerous than one with curves. I feel like AI is an "easy" highway
Magical editing of pictures is so wild to me. Most photos people take have a longterm have an audience of basically 1. That's why there's jokes about how awful it is when a coworker corners you to show you their vacation photos, the vast majority of photos taken by the average person are only ever going to be viewed again by the person taking it (and maybe their immediate family).
Memory is already this squishy thing and then when you go back to reminisce about {meaningful event} and go pull up your edited photos, what are you even looking at? Google used to play these ads constantly of editing stuff out, changing how the sky looks, etc.
It's present day you effectively gaslighting future you. What's the point of memorializing something and then immediately destroying the truth of what happened with a bunch of edits.
CGP Grey has an older video now about what happened to horses over time.
For a while every economic advance seemed to mean more and better jobs for horses. But then the automobile comes along and there's no more need for horses and we can see what happens to an animal that has no economic reason to exist.
We still have a much smaller number of horses for the few economically viable roles a horse can fill and as toys for the wealthy.
The question is if labor will follow the same path.
I know that articles like this, and the broader dialog, doesn't go too in-depth on how these things work. The phrase, "one agent understand marketing econometrics" from the article makes me wonder what exactly they mean.
This could be anything from "we put a prompt in front of chat-gpt that says 'you are an expert in marketing econometrics'" to "we built a model trained exclusively on marketing econometrics material"
No matter what they actually did, the agent (assuming its an LLM) doesn't understand marketing econometrics, instead it's tuned to produce output tokens that I suppose make more sense when the topic is marketing econometrics.
I'm not an LLM detractor, but I find the kind of thinking that's become prevalent to be so squishy. Humans are great anthropomorphizers and it seems today that no one is attempting to hit the brakes on that instinct. The models don't understand anything, in the way that we commonly use the term understand.
It seems we've confused ourselves because the box that doesn't understand marketing econometrics can produce marketing econometrics analysis and when we ask it why it came to such and such conclusion it can produce convincing explanations.
As an aside, I also feel like I've heard this for 30 years about marketing. Marketing is everywhere, the surveillance economy tracks our every move in more and more invasive ways every day, and still companies go "Aw shucks, we just can't make sense of this data." It reminds me of a time when I was working for Abercrombie and Fitch and there was this massive report our team was partially responsible for generating. 500+ pages, generated everyday, sent to a high speed printer from a COBOL job every morning at 5am so that 10-15 copies could be made for the executives. It had *all the data* and each executive had their own little ritual around which bits they thought were important.
Throughout my career as an engineer I've been asked to get more data, more data, more data. Process the data, analyze the data, create some graphs and tables and help people understand the data.
One thing I've realized is that the people demanding all this data, all this insight, all this analysis, are unlikely to actually need it or use it when available. They are tasked with making decisions, and decisions are scary because you can make the wrong one. They would love to not make the decision and maybe you can find enough data that the choices get cut down to just one. Then if it ends up wrong they aren't to blame, the data made them make that decision.
So all of this surveillance, all of this analysis, all of this data is likely just to make some person feel a bit more comfortable about making a decision.
Obviously it's partial (or else there would be a billion lines) but it gives a good broad view of what things have gotten more or less expensive.
- TVs, toys, software, and cellphone services are cheaper.
- Clothing, funishings, and cars roughly flat.
- Healthcare, education, childcare, food, and housing are all more expensive by more than 50%.
So this is the moment we are in, we can certainly find things that were cheaper but your average consumer buys a TV once every few years, they buy food and pay for housing every day.
I don't think people are ignorant of the upsides of this deal, they are just capable of also recognizing the downsides.
> Several large tuna species and sharks, known as “mesothermic” species for the way their bodies run hot, require more fuel to maintain their temperature and are thus confronting a “double jeopardy” of warming oceans and declining food, mainly from overfishing. As water temperatures climb, these species will be forced to relocate to cooler waters.
They are moving to cooler waters but the cooler waters won't have the food supplies they need. So it's either stay where the food is and overheat or go to cooler water and starve.
The graph shows both public and private expenditure. If you only consider the public per-capita expenditure it's more than every other nation on the graphs public + private per-capita spending.
The terminology is used to talk about languages that have async and sync functions where you declare (or color) the function as either async or sync.
In these languages it's pretty common for the language to enforce a constraint that async functions can only call other async functions. Javascript / Typescript, Python are popular examples of languages with colored functions.
In the author's own analogy of blacksmithing and metallurgy, I see an interesting parallel.
Humans worked metal for a long time and you can make better and better forges without knowing the metallurgy of why the result is better. If I make the fire hotter the metal comes out better, and I can get to work making forges that produce hotter and hotter fire.
LLMs could in this analogy be the forge. We can make them bigger and bigger and get better and better answers out, in the same way a pre-metallurgy human could make their forges hotter and hotter and get better and better metal out.
But the hottest forge doesn't mean you get metallurgy.
Just to emphasize this as someone that's worked in Elixir professionally for a decade now.
It really is that easy. The interoperability between Erlang and Elixir is fantastic and the communities get along well. There has been a long time push from many of the thought leaders that BEAM (the VM that Erlang and Elixir run on) should be a community regardless of language. That way we can share resources.
When I first learned Elixir I spent all my time in Elixir. Erlang has a lot of nice libraries though, so it wasn't uncommon back when I started to reach for one.
It was a pretty gentle learning curve, you can write Elixir with no knowledge of Erlang at all. You can consume Erlang libraries from Elixir with no knowledge of Erlang at all. Then if you are like me, you are curious about how something works and you go read some library code and it's a bit odd but you can mostly get the gist of it. Then over time reading Erlang is easy enough, the prolog inspired syntax is the hardest hurdle to get over, but then you realize how much Erlang and Elixir have in common.
States attempt to hold a company accountable for giving its citizens cancer.
Not like that the conservative Supreme Court says.
Either the conservative movement lacks internally consistency, or it is quite consistent but around one central idea, corporations (that are willing to bend the knee to the government, see anthropic for a cautionary counter example) should be unrestrained from seeking profit.
Even the original states rights arguments around slavery were about how private enterprise shouldn’t be restrained from utilizing chattel slavery to extract profits. Seen through that lens the ruling makes perfect sense, if deferring to a big federal rule is best to protect profits, then big government. If deferring to states causes a race to the bottom ensuring profits, then states rights it is.