Another to check out is Solvespace. It's a much smaller simpler CAD modeling program than FreeCAD; doesn't do nearly as much, but I find it much easier to use, more intuitive, and more stable than FreeCAD.
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There are still some we pull from Docker Hub, especially in the build process of our own images.
To work around that, on AWS, you can prefix the image with public.ecr.aws/docker/library/ for example public.ecr.aws/docker/library/python:3.12 and it will pull from AWS's mirror of Docker Hub.
It took them quite a long time to do so.
* Bouncy people mover. Some Google searching turns up the SFO article that you liked. Trying to pin down the exact dates is harder. ChatGPT maybe did narrow down the time frame quicker than I could through a series of Google searches,
* The picture of the building. Go to Google lens, paste in the image, less than a second later I get results. Of course, the exact picture in this article comes up on top, but among the other results I get a mix of two different buildings, one of which is identified as the Blade, one Independence Temple. So a few seconds here between searching and doing my own quick visual scan of the results.
* Starbucks UK Cake Pops: This one is harder to find the full details with a quick Google search. I am able to find that the were fairly recently introduced in the UK after my second search. It looks like ChatGPT gave you a bunch of extra response, some of which you didn't like, because you then spent a while trying to reverse engineer its system prompt rather than any actual follow up on the question itself.
* Official name of the University of Cambrdige: search gave me Wikipedia, Wikipedia contains the official name and a link to a reference on the University's page. Pretty quick to solve with Google Search/Wikipedia.
* Exeter quay. I searched for "waterfront exeter cliff building" and found this result towards the top of the results: https://www.exeterquay.org/milestones/ which explains "Warehouses were added in 1834 [Cornish's] and 1835 [Hooper's], with provision for storing tobacco and wine and cellars for cider and silk were cut into the cliffs downstream." You seemed to be a lot more entertained by ChatGPT's persistence in finding more info, but for satisfying curiosity about the basic question, I got an answer pretty quickly via Google.
* Aldi vs Lidl: this is a much more subjective question, so whether the results you get via a quick Google search meet your needs, vs. whether the summary of subjective results you get via ChatGPT, is more of a question you can answer. I do find some Reddit threads and similar with a quick Google search.
* Book scanning. You asked specifically about destructive book scanning. You can do a quick search of each of the labs and "book scanning" and find the same lack of results that ChatGPT gives you. Maybe takes a similar amount of time to how long it spent thinking. You pretty much only find references to Anthropic doing destructive book scanning, and Google doing mostly non-destructive scanning
Anyhow, the results are mixed. For a bunch of these, I found an answer quicker via a Google search (or Google Lens search), and doing some quick scanning/filtering myself. A few of them, I feel like it was a wash. A couple of them actually do take more iteration/research, the bouncy travelator being the most extreme example, I think; narrowing down the timeline on my own would take a lot of detailed looking through sources.
I'll take one of your examples: Britannica to seed Wikipedia. I searched for "wikipedia encyclopedia brtannica". In less than 1 second, I got search results back.
I spend maybe 30 seconds scanning the page; past the Wikipedia article on Encyclopedia Britannica, past the Encyclopedia article about Wikipedia, past a Reddit thread comparing them, past the Simple English Wikipedia article on Britannica, and past the Britannica article on Wiki. OK, there it is, the link to "Wikipedia:WikiProject Encyclopaedia Britannica", that answers your question.
Then to answer your follow up, I spend a couple more seconds to search Wikipedia for Wikipedia, and find in the first paragraph that it was founded in 2001.
So, let's say a grand total of 60 seconds of me searching, skimming, and reading the results. The actual searching was maybe 2 or 3 seconds of time total, once on Google, and once on Wikipedia.
Compared to nearly 3 minutes for ChatGPT to grind through all of that, plus the time for you to read it, and hopefully verify by checking its references because it can still hallucinate.
And what did you pay for the privilege of doing that? How much extra energy did you burn for this less efficient response? I wish that when linking to chat transcripts like you do, ChatGPT would show you the token cost of that particular chat
So yeah, it's possible to do search with ChatGPT. But it seems like it's slower and less efficient than searching and skimming yourself, at least for this query.
That's generally been my impression of LLMs; it's impressive that they can do X. But when you add up all the overhead of asking them to do X, having them reason about it, checking their results, following up, and dealing with the consequences of any mistakes, the alternative of just relying on plain old search and your own skimming seems much more efficient.
On Linux, I use Compose-hyphen-hyphen-hyphen.
I don't use it as often as I used to; but when I was younger, I was enough of a nerd to use it in my writing all the time. And yes, always careful to use it correctly, and not confuse it with an en-dash. Also used to write out proper balanced curly quotes on macOS, before it was done automatically in many places.
Obviously, this is quite corrupt; that person is supposed to be paid by Walmart to best represent their interests, not select the vendor based on who will best financially compensate them personally.
The Sopranos part is a reference to a TV show about the Mafia, in which (presumably, I haven't seen it), bribery schemes like this are common.
First of all, keep in mind that research has shown that people generally overestimate the productivity gains of LLM coding assistance. Even when using a coding assistant makes them less productive, they feel like they are more productive.
Second, yeah, experience matters, both with programming and LLM coding assistants. The better you are, the less helpful the coding assistant will be, it can take less work to just write what you want than convince an LLM to do it.
Third, some people are more sensitive to the kind of errors or style that LLMs tend to use. I frequently can't stand the output of LLMs, even if it technically works; it doesn't live to to my personal standards.
It's co-opting the term to call it open source.
This has been debated and settled. What people mean by open source or free software has a well agreed upon definition, and this isn't it.
New housing is simply more expensive; so it's marketed as "luxury", and it's sold at a premium to the higher end of the housing market. This reduces demand for the older, more affordable existing housing stock, and with depreciation and wear and tear, the new housing will become more affordable as time goes on.
If you're in a market with a shortage of housing, those with more money will simply outbid those with less, even for older, less desirable housing. I've seen it, where when I moved out of my last apartment before I bought a house, my landlady raised rent considerably when looking for a new tenant, and even then she got a tenant who wanted to pay her over the rate that she was asking for to ensure that they were able to get the apartment over all of the other applicants. Wealthy empty-nesters who were downsizing, and willing to pay a premium for an older apartment in a desirable neighborhood, forcing out anyone who might have otherwise been able to afford it.
So yes, while it does help for there to be some push to build more affordable housing, if taken to an extreme building only luxury housing will leave an unbalanced market, in a lot of cases building luxury housing is exactly what you want to do to reduce the competition for the existing, more affordable housing stock.
So you have to be willing to take those risks, and want to be handling those mundane day to day details.
I can't necessarily block them because I might need them for a password reset or a future transaction. I spent a while trying to set up filters that work, but eventually got tired of trying to fight it.
Now I mostly just avoid email for anything other than these things, and have just given up on it otherwise.
Spam won. Email lost.
And social media services are losing in similar ways. Rather than showing me things from people I subscribe to, they're pushing all kinds of automated "content for you" that I don't want and can't opt out of. So I'm pulling away from them as well.
The internet is filling with slop and distraction, both AI generated and not, and we haven't "solved" it, it's constantly getting worse.
It isn't hard to imagine that a chatbot that seems to know a lot, and which can easily be convinced to produce text about any arbitrary subject with no grounding in reality, and which is prone to just making up plausible sounding text which is written in an authoritative tone, could be something that could easily trigger such psychotic episodes.
And it doesn't seem improbable that the interactivity of it, the fact that it responds to what is going on in someone's mind, could make it even more prone to triggering certain types of psychosis more easily than traditional unidirectional media like writing, TV, or radio.
Now, that's all supposition. For now, we just have a few anecdotes, not a rigorous study. But I definitely think it is worth looking into whether chatbots are more likely to trigger psychotic episodes, and if there are any safety measures that could be put in place to avoid that.
No, not in the archived version: https://web.archive.org/web/20250612153046/https://www.smith...
Oh, was this mentioned in the original HN title? Has that been edited?
Yes, it was. https://web.archive.org/web/20250615154352/https://news.ycom...
Original HN title was "1k year old 3 sisters crop farm found in Northern Michigan"
I really wish that when titles were edited there was some history, it would make it much easier to understand these little discussions based on the original title.
Yeah, people tout RAG and fine tuning, but lots of people just use the base chat model, if it doesn't keep up to date on new data, it falls behind. How much are these companies spending just keeping up with the Joneses?
The thing is, with enough magical thinking, of course they could do anything. So that let's unscrupulous salesmen sell you something that is not actually possible. They let you do the extrapolation, or they do it for you, promising something that doesn't exist, and may never exist.
How many years has Musk been promising "full self driving", and how many times recently have we seen his cars driving off the road and crashing into a tree because it saw a shadow, or driving into a Wile E Coyote style fake painted tunnel?
While there is some value in evaluating what might come in the future when evaluating, for example, whether to invest in an AI company, you need to temper a lot of the hype around AI by doing most of your evaluation based on what the tools are currently capable of, not some hypothetical future that is quite far from where they are.
One of the things that's tricky is that we have had a significant increase in the capability of these tools in the past few years; modern LLMs are capable of something far better than two or three years ago. It's easy to think "well, what if that exponential curve continues? Anything could be possible."
But in most real life systems, you don't have an unlimited exponential growth, you have something closer to a logistic curve. Exponential at first, but it eventually slows down and approaches a maximum asymptotically.
Exactly where we are on that logistic curve is hard to say. If we still have several more years of exponential growth in capability, then sure, maybe anything is possible. But more likely, we've already hit that inflection point, and continued growth will go slower and slower as we approach the limits of this LLM based approach to AI.
The paper itself is behind a paywall so I can't see it, but it looks from the references like they provided proper attribution.
It's unfortunate that some of the articles around it don't mention that, but it seems like the main point of this is discussing the process for building the transistors, and then showing that can be used to build a complete CPU, not the CPU design itself which they just used an off-the-shelf open source one, which is designed to use a very small number of gates.
"Musk did not ask investors for approval but told them that the two companies had been collaborating closely and the deal would drive deeper integration with Grok, the investor said."
Now, he may have only been talking about xAI investors here, but it seems pretty clear from his actions that Musk pretty much demands full control of his companies, and if he does involve the other investor's they're likely rubber-stamping without much opportunity to push back.
Musk's privately held companies, and to a large degree Tesla as well, are all things he treats as effectively one big company that he controls; he'll take employees from one to another at will, he sells them to each other or spins them out at will in all stock transactions, etc.
The EU regulations allow seeing through such sham company boundaries that are all controlled by a single entity, and treating them as a single company.
It's hilarious that you think that advertising is actually necessary for economic prosperity. If anything, it's probably a net drain on it.