Consistently impressed with the performance of Grok, given they started so much later than everyone else and, in some ways, are less well funded compared to OpenAI and Anthropic. I wonder how much of this is just luck, name recognition, or management style. Marc Andreesen likes to talk about how Elon companies have a unique engineering-heavy management structure, which contrasts the research heavy cultures of OpenAI and other labs, and it makes me wonder if that sort of thing could be behind their relative success.
> US prosecutors charge Atlanta man after GrapheneOS phone wipes itself during airport search
I really don't like this title. Officers asked him to open the phone, which he pretended to do, but instead wiped the device
> During the questioning, agents repeatedly asked Tunick to unlock his phone and warned they would seize it if he refused. When he finally provided a passcode, the phone appeared to restart. The defense motion states that "the screen went blank, flashed several times, and the phone appeared to restart," resulting in the loss of data.
The title implies the agents maybe entered too many pins by mistake and the device auto-wiped, or that it reset itself with no human intervention, which isn't what happened. This is more like shredding paper when the FBI arrives at your office, which most people would attribute to destroying evidence. I hope he wins the case in principle (I think there's a risk of a slippery slope here) but it wouldn't be a moral tragedy if he lost.
> it isn't the landlord's business what a tenant is doing inside, working or sitting around watching TV
Unfortunately the government is extremely interested in what you do in your property, and this makes it the landlord's business. That said in this case, it seems more like homeowners are renting out a small unit behind their home, and want to disincentivize people who will be there more. Personally if you can work from home I'm not sure why you'd live in a bay area suburb, especially not walnut creek, but to each their own I guess.
I'm not sure they always are. Certainly employers/managers are pro-US tech workers because it gives them access to lower-cost talent, and many H1B's/immigrants are supportive of it because they want more people like them. This is a common complaint of Indian H1B's and something I've experienced myself, where, especially before the H1B fee, they would only seriously consider other Indians/people of their nationality.
If you go into the midwest though there's more animosity about being replaced by lower-wage foreign talent. In my experience when US-born engineers support immigration strongly it seems to be a holdout from their college years, and not something they picked up since starting their career.
You say this as though every company doesn't take on debt, and all debt isn't a risk. I'm sure you have some much riskier debt than ChatGPT already in your portfolio, and interest rates are adjusted by relative as judged by the market. I'm sure some debt will fail, but certainly all of it won't, and while anything could cause a market crash saying "when these fail" holds a lot of incorrect assumptions.
I wonder about this. One would think new programmers, like old, will learn through experience to care most about the things that matter, and ignore things that don’t. In 10 years my pre-AI experience may condition me to waste time thinking about struct packing, while younger developers simply won’t know or care
I think the problem will be that there was only so much we could do wrong while we were learning 15+ years ago and our mentors had a reasonable scope to watch around us as we learned. People learning now can get a coding agent to build the entire system for them and it is much harder for the mentor to review that work.
My concern is how much damage could be done by someone learning 15+ years ago vs someone learning now.
> thinking about struct packing, while younger developers simply won’t know or care
That particular ship sailed more than 15 years ago. Back then if you asked people trained on high level GC-based languages about low level details or memory allocation, they wouldn't know.
I do a lot of embedded C++ work and byte stream work where packing matters, but I agree with you. For most development it’s irrelevant, and in the future I expect it to be even less relevant.
But I feel like over the last ~10 years there's been a trend back towards learning more about them (e.g., with Rust and new C# features, or with C++ Win32 programming becoming a cool hipster thing somehow)? Or maybe that's just my own personal bubble.
UBI's have been tried repeatedly and have never worked. They have a bad incentive structure and are simply unaffordable. Unfortunately if you want money you will have to get a job
Nobody can agree how big UBI should be, and people talk past each other. Small UBI, like poverty level, could replace welfare but wouldn't be enough to survive on without doing something about housing and healthcare. Big UBI would be enough for good life, like median income, but not affordable with current economy and create inflation.
There is something in between that would be enough to survive, with small UBI, universal healthcare, and guaranteed housing.
You've got to start somewhere. Even a small UBI, like the one Alaska has, is better than nothing. Replacing welfare with a UBI at the same level would already be a massive improvement because you'd get rid of the poverty trap. From there you can grow it as much as the economy demands.
I just did some brief research on this using Perplexity. I’m failing to see how partial pilots of basic income “didn’t work”, what exactly do you mean by that?
Yeah but that's an issue with the researcher putting out a bad paper, and it suggests you'll have to reject more papers. We wouldn't ban email because many of the emails are spam, it just means we need new tools to filter out junk. AI will allow researchers to be more productive all together and take less time to publish a paper, which is good.
The problem with spam is that it's not a technology like AI is. So I suggest taking cars instead.
Cars have plenty of advantages, and yet no one would say "the number of pedestrians killed by cars is rising, but that's an issue with the drivers". In fact, the opposite is true: from fines and school zones to speed bumps and bollards, we have accepted that cars bring structural problems with them that cannot be solved at the driver level alone.
> we need new tools to filter out junk
Agreed, but if my office suddenly was flooded with garbage my first thought wouldn't be "I need more, bigger trash cans" but rather "who brought all this junk here and why?". To simply assume that the garbage is a sudden natural phenomena that I have to live with seems, at the very least, unfair.
> Yeah but that's an issue with the researcher putting out a bad paper, and it suggests you'll have to reject more papers. We wouldn't ban email because many of the emails are spam, it just means we need new tools to filter out junk. AI will allow researchers to be more productive all together and take less time to publish a paper, which is good.
It's a signal:noise ratio thing. If 1 out of every 1000 AI-written papers are bad, it makes sense to put in a filter that auto-rejects any paper that has AI tells.
After all, if that 1 researcher was any good, he wouldn't have used AI to write the thing in the first place.
Publishing was always about getting past the filters. There's one more filter - "AI-generated content" - so do what you have to to get past it. IOW, write your own paper.
People are saying this is a bad thing but is it really a problem? The compelling aspect of research is the data and/or description of work, not the writing. Papers probably should be written by AI so that they're clear and well presented, while the researchers should focus on generating good data. If there is no data or work behind the paper, we should question whether the research group needs funding.
but the assumption you are making is that the underlying ideas are compelling. In practice, people often decide to publish incremental and/or mediocre work for various reasons, and then dress these ideas up to seem as compelling as possible to get past peer review or make a press release.
at the least, this is problematic for peer-review because the absolute number of submissions outpaces the time availability of a finite number of expert reviewers. We cannot quickly generate expert human reviewers, and so the community might converge towards half-baked solutions (AI-generated reviews or rejection systems, vastly expanded referee pools, etc.) that tend to erode trust and and make scientific communities more adversarial.
I've sort of thought this too. Rust adoption feels a lot like Haskell to me in that it's centered around ideological things that don't really improve the final product and arguably slow development. If your goal is to write a bunch of LLM code there are better languages than rust, and rust development is often regarded as too annoy for anyone not enthusiastic about the ideological improvements.
There are cool ideas in the language but I don't see it enduring over time.
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