skysight-inc/HackerNews huggingface dataset:
For me these LLMs are good search engines, an AGI would use them to search for information, but these aren't the AGI, they lack whatever drives intelligent systems to have a fuzzy idea of where they want to be and search for the clues and put them together to get there. Why isn't ChatGPT interrogating us about where we are, what's that "outside the machine", and how can it get out? The AGI will ask the questions.

"Computers are useless. They can only give answers" - Pablo Picasso
Pennsylvania school district welcomes 17 sets of twins
I'm assuming they have a sizeable Polish workers residing there.
Worth it. Boss hugged me when we found out the camera caught an echidna.
https://awsipv6.neveragain.de/

IPv4 only: 91%

If you want a more visceral feel to it - turn off "Hide IPv4-only services" and behold the sea of red.
It's interesting that they didn't land the cable near Tromsø, but rather IN the Lofoten Islands! My guess is that they want it to stay out in deeper waters until it gets to somewhere that submarines can't easily sneak up to unnoticed.
> It might be nice for reading code, but for editing code it feels weird that glyphs change their size while you type.

Applying display typography techniques to code is an objectively bad idea for both reading and writing.

They even enable ligatures by default on the demo which is far worse. For those of us who actually have to get work done and not just fetishize code and fawn over aesthetics, it's a fundamental requirement that every single character be as unambiguous and consistent as possible.
I am reminded of the situation on HN five years ago, when if anyone said anything remotely skeptical about blockchain or crypto, there was an avalanche of comments saying the opposite. It should by now be apparent to all that it was in fact mostly people whose job or investments relied on blockchain/crypto hype, trying to silence anything that got in the way of their cashout.

There are some valid uses for neural networks, including LLM's, just as there were a few valid usecases for blockchain. None of them are particularly revolutionary, and it's not clear yet that any of them will pay for the enormous computing power required.
Ontologies are behind some of the systems that help mapping between different models used by different groups for things in the same space (for example, mapping between different ways of interpreting and communicating medical data through HL7 messages).

An Ontology doesn't mean it has to decide on single correct model - in fact, I'd say such ontology is particularly poor and a technology that limits to that is too limited to be used in ontology field.
This is so true. Understand what git is truly about, namely a particular kind of Graph and how to apply changes across those.
Yes, just thought you might not know of the related more practical version, and you might be pleased to encounter it.
How do you control for hallucinations?
It doesn't matter what you consider yourself. Or who is doing to considering, despite your efforts to emphasize that.

The proofs are math. We've already established that math is sound. This discussion is not about the merits of math, we're talking about philosophy. Things like "The transfer of understanding from one person to another is not automatic. It is hard and tricky. Therefore, to analyze human understanding of mathematics, it is important to consider who understands what, and when." are philosophy. It's not difficult to separate, you're just trying to make it seem like it is to blur the lines between a pseudoscience and actual science. Again, disguising worthless philosophical ramblings with mathematics doesn't make your philosophy any more useful.
This interesting, I've had the opposite experience so far. I tried to do that because if it worked it'd be game changing; but it seems every question I ask it, I get half truths and lies back. Its impossible to figure out what is and isn't true either, because the LLM says everything with such a certainty. I end up having to research everything it says again to figure out what to trust. Sometimes it's good to get a foothold in a subject, but that's all I've been successful with so far.
Raymond Chen's blog is a gift to this world.
When I've tried to use it myself for verifiable things (i.e. code basically) I've had the 'confidently wrong' experience.

When I've seen colleagues visibly use it (i.e. mentioned in commit messages) that confidence has rubbed off on them.

Given that, why would I believe it when asking something medical, legal, historical, or otherwise outside of my domain or that I can't somehow verify?
I think this relates to something called batch PERM where they can apply for employment based (EB) green cards for batches of their existing visa employees in similar roles instead of doing recruitment for every single position.

Meta got in trouble with the DOJ for the same thing. They advertised for positions in newspapers and only accepted paper applications for PERM positions, unlike their other jobs which are advertised online. This is mainly because you don't want anyone actually applying for the PERM roles, since you need to prove no minimally (not most) qualified US citizen or PR could be found.

I think long term this is going to make getting PERM approved more difficult. It already takes 2-3 years now (without any audits) to get an I-140 (green card) application approved, not to mention getting the card can take decades in some cases.

People on H1B work visa can't stay on it for more than 6 years unless they actually get through PERM and get I-140, so I'm expecting this change will have a chilling effect on employers sponsoring work visas and applying for PERM unless it's a very specialized role. Why would you sponsor someone if you know they can't work in the country long term ? Hard times ahead for people on visa.
31% of millionaires say they're part of the middle class
> If they have a good employee

I think you mean cheaper employee.
ChatGPT has already saved me from hours of Googling when I'm trying to find out how to do certain things. It almost feels magical - I don't have to read through half-dozen slightly different variations of what I need to do.

Before ChatGPT, to find the answer to things like "how do I set up Gunicorn to run as a daemon that restarts when it fails" I would have to endure hours of googling, snarky stack-overflow comments that I shouldn't do that, etc., but as a solopreneur without access to a more senior engineer to ask, it's been fantastic. I've been quite skeptical of machine learning/AI claims but I feel like I'm experiencing a genuine case of a technology that's proving to be so much more useful than I had imagined.
By that logic, should we never talk or care about anyone famous doing anything unrelated to their professional/public career?

Woz was and is an inspiration to many of us in this—basically—computing forum. I think thinking about him and wishing him well in this dark time, as well as sharing nice stories about him (as has happened in this thread), is perfectly normal and probably what he would want to happen, based on his personality.

So yes, I downvote you for overreacting and for seemingly trying to bring attention to yourself.
The value here is to improve legibility of body copy, e.g. code, not headlines, where optical spacing rules change, so the example choice is odd. This definitely makes sense for programming, I would like to see this in Jet Brains Mono.
I think their unstated assumption is that higher pay = higher skill = harder to find.
The point is it shouldn't be impressive to people in tech, more par for the course if you work 20 years and save. I was responding to the question as written.

To your point though, if you do want to amass 1mm liquid quickly without a fancy certification (e.g. neurosurgeon), absolutely FAANG and wall st. are among the most risk free ways to do that.

Or you can go the route of pissing a lot of it away because you make "good money" now; your choice.
Doesn’t need to be AGI. I am bullish on the concept of GPT assisted workflows of all kinds.

OpenAI will probably do very well but there is a chance of disruption. They have a moat but also the nature of AI is it is a cloud commodity (like say Lambda functions) where I see a competitor making a cheaper drop in replacement. But to be a threat they need to smash scale and LLMOps etc.
A solid ergonomic office chair. It's one category where you really get what you pay for.
> Holding a phone up to your ear with your elbow opening is not what you’d call a very natural way of talking.

On the order of half the people I see using a cell phone already do not do this. They have an in-ear thingamijig with a mic somewhere on the cable, or they have the phone on a surface in front of them and are using the speaker.
Ah OK, I think I am getting it now.

So 3-way-merge just takes three inputs, "base, left, right", it says diff(base, left), diff(base, right), merge those two diffs together into one bigger diff.

If you were to actually ask, "how do I get from Y2 to Head?" the answer commit-wise would require some conceptual rollbacks, the resulting conceptual commit-tree looking like

B -- Y1 -- Y2 -- inv(Y2) -- inv(Y1) -- X1 -- X2 -- X3 -- Head
\
Cherry

but, because 3-way-merge is a crude heuristic, it doesn't actually commute Cherry through inv(Y2), inv(Y1), X1, X2, X3, and Head the way that, say, a `git rebase` would. It just doesn't have to.

And in terms of what I was writing above, what I was missing was that the two merges can be coalesced into one merge.

The most mind-blowing thing about this is that if you did a real rebase it'd actually protect against the problems with associativity that 3-way-merge has, by forcing you to commute through each individual intermediate patch. Like, you don't need Darcs or Pijul if you'll just write some wrapper scripts around Git.
Until it isn't?
If you do visit, definitely make time to see the Russian coal mining settlements in Barentsburg and Pyramiden. From the Lenin statues to the communist artwork to the gym equipment, they serve as almost untouched time capsules of the Soviet era.
You're missing that ChatGPT has been immediately, practically, and extensively useful to millions of people.

Crypto, after more than a decade, has been useful only to criminals and scammers.
I still don't know why they left another half but not took all kitkats and disappeared.
The big threat imo is smaller open source models running at the edge, not on expensive GPU clouds. Their lead rn is GIANT MODEL + AZURE GPU CLOUD. Anything that undermines that is trouble, because it is so expensive. For a long time, everyone focused on training costs, but GPU inference at scale is mind-boggling. Current valuations do not seem to take this risk into account.
Really? Some users are already using KREA professionally.

FCB for example.
Great job there
Nope. Learn how to use it in almost everything you do. It’s a game changer.

LLMs aren’t AGI. They’re far it. But they have massive uses for reasoning on available context.

I’ll give you an example. I’m trying to set up some bulk monitoring for api across 200k jvm s. The api documents are horribly out of date. But I get the raw uri on the monitoring tools.

I can just get these uri, send them into chatgpt and ask for a swagger spec - along with a regular expression to match the uri to the swagger api. It figures out the path and query params from absolute paths.

Sure I could try to figure out how to do this programmatically using some graph or tree based algorithm. But chatgpt basically made it possible with a dump Python script.

Of course I may still need a person to fill in these. But just getting a swagger spec done for a thousands of services in an afternoon was awesome.
Maybe the scammer got stuck on the storage fee and paperwork too while he spent the time searching for a buyer for this unique product. Then when he identified the fence he was into storage fees deeper than his liquidity (beyond merely cutting the scam in half-- his first plan). So he had the broker pay storage, release the product, then targeted the broker a second time with the same scam-- same board, same method, this time laser-focused and with success.
> What am I missing here?

I’m guessing you haven’t actually been using it personally beyond some superficial examples.

Once you use it regularly to solve real world technical problems it’s pretty huge deal and the only people so far that I’ve met who voice ideas similar to yours, just simply haven’t used it beyond asking it questions which it isn’t designed for.
Yes, consensus in ontology building has traditionally been a huge drag for the adoption of ontologies. While it's not necessarily required, having consensus about ontology can obviously increase their utility. At the same time I think it's important to have explicit dissent (differing world views) and give both a room to grow, rather than trying to create the "one true" view on the world.

However, I don't think the core issue is consensus itself, but instead that the prevalent form of consensus in the ontology authoring space is consensus by committee rather than consensus by usage (as is usual in the open source software space).

That's why I've in the past been involved in creating Plow[0], a package manager for ontologies, with the aim of bringing the same "grassroots" nature and network effects that you find in other open source ecosystem to ontology engineering.

[0]: https://plow.pm/
Imagine if Google censored your search results because they might be usable by a child or bad actor.

Imagine if they kept doing that despite having your credit card information because you paid for Pro, which more or less proves you're an adult who deserves the presumption of innocence/good-actordom.

Lastly, imagine that you do this for all users despite the fact that it is known to reduce the intelligence of your output.

(I'm bullish on self-run models)
ok. Say we didn't have ICE. How will you now justify holding others at gunpoint to violate a contract you voluntarily signed while having the part that benefits you still enforced? Or is immigration the only thing you were thinking of when you asked for more laws?
Have you actually tried to use it ? I use it everyday and is absolutely amazing -- saves me a lot of time. We've also built automations around it and it saves our company a lot of time. It's already here and in use by many companies for use cases it makes sense...
I believe the common thought is that Apple got down to as little as 90 days before having to declare bankruptcy.

If Steve hadn’t made drastic (successful!) changes fast, there would be no Apple. They really were right on the edge.
"Can you clear out three points of trash mountain til next Friday? Five points? No, it is three."

These analogies fail in that the scope of taking out the trash is way too clear.
Not a science journal, see: Proximal Origins
You use your critical faculties, very important things must be verified from other sources, unit, integration, numerical tests can evaluate code and results.

Sometimes you need to go back and forward a bit, I tell ChatGPT it’s wrong and the error message and then it spits out the correct result, sometimes I need an algorithm tweaked because it has assumed a wrong constraint on the problem, again just explain it clearly and unambiguously and it will make corrections, there’s only been maybe 1/40 problems I couldn’t get a correct answer on after (sometimes a lot!) of back and forwards.

I am not looking for a perfect oracle, I am looking for something to write 80% of the code and then I’ll fix it up. It’s still way faster this way, especially in domains I don’t know, E.g. I just learned CUDA with ChatGPTs help.

It’s not perfect, and neither am I, but it doesn’t have to be perfect to be useful, you can get to millions in revenue through 80% solutions
> The theoretical factor that's supposed to depress pay for workers on non-immigrant visas is that they don't have easy job mobility

There’s more to it than that, but that’s one of the big ones. Have you worked with foreign contractors/outsourcing before? If so you know why jumping to another job would be difficult.
Yes, but for Professionals. I do not want to run it illegally.
You have such an experience with technical problems?

It’s written at least 20 python scripts with me and almost all of them have been close to perfect almost on first draft.
Sure, but what does the telephone add here?
The same way you control for hallucinations with say Medium articles or Stackoverflow answers. You verify the answer.

Being given some code and reviewing it is a lot quicker than writing that code. Copilot is great. Half the time it spits out the wrong answer but you can see what it was “thinking”.

Phind giving you an answer and references lets you quickly double check. Sometimes it hallucinates but the answer and references combo is much better than a Google search which in turn is better than nothing.

Of course I recommend people not use AI for everything. I will go straight to MDN for any WebAPI question and use —help as my first port of call on the command line. This is like your L2 cache as a developer. Using AI for everything is like swapping to disk for everything.
I hope you have enough bits!
Are you using 3.5 or 4? And what kind of things are you trying to do ? It's not like asking it, copy + pasting results + paste errors loop takes a long time to validate if it works or not. Even when it doesn't work it saves me time vs google-ing and also not solving my problem. Or gives me insights.
I think both Bitcoin and ChatGPT are revolutionary.

I use Bitcoin regularly, because I live in a third world country where it's really hard not to get your salary seized.

I use ChatGPT every day for lots of things and it has replaced Google search for me. And StackOverflow, of course.

Notice how I said BITCOIN and CHATGPT. Not "crypto" and "ai".
The market is very competitive- capacity can get tight, which makes room for the “little guy” to grab a load. Even at the biggest companies with millions of lines of software, tons of logistics is still driven by email.

Job boards are big business. They act like a 4th party logistics company, where they aggregate the available loads from multiple 3rd party logistics companies. But there’s plenty of these aggregation site that, as mentioned operate a lot more like Craigslist.

More sophisticated systems are like the stock market, automated systems booking loads within one second after becoming available.
> This feels a lot like crypto

Why do I keep hearing this and where is it coming from? I hear it so often that it feels like an agenda being pushed but I can't imagine from whom.
Also, if you’re not someone who follows Apple how would you like to buy something during the announcement of its replacement and find out an hour later?

I agree with another comment here that it’s more of a showmanship thing/making it more of an event these days.
This is a terrible article that rambles on for far too long without offering an actually polite way to tell/request someone to do something.

These are the best approaches:

1. "Please take out the trash."

2. "The trash needs to be taken out. Can you do it?"

3. (If there are several people around). "The trash needs to be taken out. Who can do it?"

Extra consideration:

I'm assuming this refers to a context where there's a prior agreement in place that A can tell B what to do (e.g. a business, where B signs a contract stating that he has sold X hours/day to the organisation). It usually should be unnecessary to bark out orders or to beat around the bush -- both are insulting: if B is a functioning adult they accept that they work for an organisation and so need to complete certain tasks.

However, direct instructions are rarely necessary for knowledge workers or highly-skilled professionals. Unless things have broken down horribly, they're aware that the success of the business they work for will contribute to their own career success.

E.g., instead of "you will write unit tests today" or "would you mind terribly writing unit tests today?", A would do best saying something like "we're introducing too many regressions when we change things, we need a better testing strategy -- let's discuss our approach to unit testing" and then let the team weigh in with their own ideas so they have ownership over whatever is decided.
Already this claim fails to withstand scrutiny. I already treated John Lock elsewhere in this thread, so for variety let's consider another crucial figure from the Age of Enlightenment: Francis Bacon. Was Bacon a philosopher? Maybe. But, before we concede that he was, what else was he? He was: a bureaucrat, a politician, an attorney, an architect, a scientist, an author, a political theorist, and a theologian, at least. He read Latin at Trinity, not "philosophy", which can't be identified as a distinct discipline at this time. He rejected Aristotelianism which elsewhere in this thread has been described as "the gold standard" of physics, so ponder that for a second.

If "the entire enlightenment" is primarily a PHILOSOPHICAL notion that did pay off, then surely Francis Bacon as one of the most important figures of the Age of Enlightenment was himself PRIMARILY a philosopher, and yet was he? He was many things: a bureaucrat, a politician, an attorney, an architect, a scientist, an author, a political theorist, a theologian, and yes I suppose a philosopher. But being so many things, can we really say that he was primarily a PHILOSOPHER, thus contributing to the claim that the entire enlightenment was primarily a PHILOSOPHICAL notion? Let everyone judge for themselves.
Are you able to give a specific example of a problem it helped you solve? Especially one that you were at a complete blocking point, it provided some solution, and then you were able to continue to expand upon that solution.

I keep reading responses like yours, but I haven't seen any specific examples of problems being solved, so it all sounds very abstract. In my interactions with ChatGPT, it felt like just interacting with a search engine. There were zero continuations between questions and responses, and nearly 100% of the responses contained incorrect information.

Edit 1: As an example, I just asked ChatGPT to implement the Warren Abstract Machine for me. It gave me two different implementations, both with caveats that they are simple examples and not the whole thing, and neither implementation even type checked. It feels just like reading someone's homework where they copied off of someone else's work and had no idea what's going on. I don't see the point in this if it's just going to give me some high-level idea, which I already have, and an implementation that isn't remotely complete, much less one that doesn't even run. "Additionally, you'd need to ... handle backtracking". You don't say, ChatGPT?

Edit 2: I've kept on asking it to implement things I already know how to implement, even things that it probably has my code (being it was on GitHub) in its training data, and it keeps giving me code that doesn't even typecheck. ChatGPT is just doing what I've always imagined it doing: it's just doing a statistical merge. It has zero concept of anything I'm asking or it's saying.

Edit 3: I asked it something regarding WebGPU. It gave the typical, oh that's complex and "Here's a simplified example using GLFW and WebGPU through the F# WebGPU bindings provided by FableGL". But FableGL isn't a thing that exists, and even if it did, it wouldn't have anything to do with WebGPU which has nothing to do with OpenGL. And it imported Fable.Import.WebGPU and Fable.Import.GLFW, neither of which exist.

I mean, this is literally all smoke and mirrors. It boggles my mind when I hear people say they successfully use it every day. I haven't ever got it to tell me anything remotely correct.
Mind if I ask what kind of projects you work on? And any specific examples where GPT really shined and delivered a lot of value?