skysight-inc/HackerNews huggingface dataset:
I'm a little surprised the article doesn't mention using a key file in conjunction with a password as an added security measure.
Yeah, totally, it really just gives a flavour of how the structure of price levels and price/time priority, tick sizes, etc. can create exploitable probabilistic advantages, and I think many folk are not aware of even this.

The real environment is absolutely messier and more complex than described by this article. Still, it's a layer deeper than "speed is good", to paraphrase Gordon Gekko.
I don’t think it would be about ethically problematic for Knuth it would be more about he would like to know precisely the details

at his age he has to be super conservative about what he spends his time
Good point. I would even add humans brain have to process a lot of sensory input: video, audio, touch, smell, taste. And communication channel for human is very inefficient - you have to translate your thoughs to language then to audio then someone alse doing the same in reverse. If you texting you have to slowly type something then someone else need to do text recognition in their brain.

LLM are like handicapped humans that are visual, hearing impaired, with long covid no taste or smell and can focus only thinking and very efficient communication channel as text/tokens sent via wire on the internet
I have very quickly picked up the habit of pasting snippets of my code into GPT-4 and simply asking "Why is this not working?" Almost every time, it succinctly explains the apparent purpose of the code, and how it is subtly wrong.

It's so good that I often do this preemptively to avoid a compile/deploy/test cycle.
You know, the capacity of though/intelligence/understanding the world?

As in "homo sapiens" (same latin root)
COVID sort of turned that around a bit: it became more important to go abroad and not get stuck getting locked down in Shanghai or one of China’s top cities. We have to see where things land now that china has abandoned zero COVID.
Brooks has done something I appreciate a lot: he turned a phrase.

> stop confusing performance with competence

You can safely skip the rest of the article. That sentence gives you all you need, because you are competent.

If you want a little more meat:

> The example I used at the time was, I think it was a Google program labeling an image of people playing Frisbee in the park. And if a person says, “Oh, that’s a person playing Frisbee in the park,” you would assume you could ask him a question, like, “Can you eat a Frisbee?” And they would know, of course not; it’s made of plastic. You’d just expect they’d have that competence. That they would know the answer to the question, “Can you play Frisbee in a snowstorm? Or, how far can a person throw a Frisbee? Can they throw it 10 miles? Can they only throw it 10 centimeters?” You’d expect all that competence from that one piece of performance: a person saying, “That’s a picture of people playing Frisbee in the park.”

---

So I've calmed down. Now what? The problem isn't only that this train is flying off on a tangent: it's that it's off the rails. What rails should it be on?

The problem, as I see it, is narrative. As soon as we called it "AI", that wrote the Genesis of the Scripture of the cult. In this new religious movement, God is spelled L-L-M. Back here in reality, LLM isn't a God; or even a person at all.

That's the mistake: personification. A person can perform, but a performance can't person.

---

Narrative is a powerful tool. It's why we're so excited about Natural Language Processing in the first place. Ever since the very origins of software, the power of narrative has been so close, but always still just out of grasp. Do we even know what we are reaching for in the first place?

In a sense, we have a part of it: explicit definition. What Chomsky categorized "Context-Free Grammar", we have made into programming languages. What they are missing is implicit inference: context.

That's what LLMs do. They use inference to model the patterns that exist in written text. With that model, they can hallucinate more text that follows the same patterns: they can perform natural language.

So that's it, right? Problem solved! What's missing? explicit definition. We traded one problem for another. No one (so far) has figured out how to solve both in the same program. You can have definition, or, you can have inference. You can't have both.

This doesn't make any sense to us humans. We don't have any trouble at all doing both at the same time. We do it all the time! Do we actually do anything else? Unfortunately, LLMs are not humans.

---

The two approaches to language are diametrically opposed, but they work with the same domain. Approaching from either end of the spectrum, definition and inference explore together the wild universe that is story. That's the missing piece: once we figure out what story is made of, we should be able to put all three pieces together.
The title is misleading. That website has existed for a long, long time. I remember reading lectures there at least five years ago.

Unfortunately, I cannot prove it, because the domain has been excluded from the Internet Archive for some reason. But I can tell you with complete confidence that the lectures have not just "now" gone online.
I bought 3 Samsung EVO 256G cards last year for a dash cam, all of them failed around 36 days - no return but just warranty with Samsung. Samsung’s customer service for storage products is pretty bad that there is not even a website to submit a warranty claim.

Switched to Sandisk then it has not failed after a year, I probably will never buy Samsung cards.
> Feynman Lectures on Physics now online

It's been online since 2013. [1][2]

[1]: https://www.openculture.com/2013/09/the-famous-feynman-lectu...

[2]: https://en.wikipedia.org/w/index.php?title=The_Feynman_Lectu...
Not really the thrust of the article, but that '50s picture of the family playing scrabble in a self-driving car, surrounded by text about trains... really makes me think that if I were that family, I'd still prefer to be playing scrabble on a train, rather than in a cramped self-driving car on a highway.
Biden Seeks to Call McCarthy After Debt-Limit Talks Hit Impasse
The Steinsgate VN starts off very slow and stays very slow for quite some time. I also found it tedious. Fortunately there are hundreds (thousands) of other VNs to choose from.
“US officials are now suggesting that NATO should not be fearful of such [nuclear war] scenarios.

In March, three US officials with intelligence on Russian warheads claimed that around 60 percent of Moscow's missiles do not work.”

https://www.express.co.uk/news/world/1615231/putin-news-russ...

US government officials and armchair generals on social media are not exactly the same thing.
National Treasure wins the Preakness in a Triple Crown clouded by horse deaths
I had no idea. I take it to mean Neeva was on the rocks since last year:

https://www.linkedin.com/posts/sridhar-ramaswamy_web3-crypto...
Gpt is a great tool, it won’t be able to do complex tasks because it really isn’t that smart. If you tell it to do something relatively complex end to end, it will fail unless a plugin specifically supports it.
UX Journal – Catalogue of Impossible Objects
Relational dbs are mostly keyval stores inside. An index is a keyval store projection of another keyval store (but say, keying it for another subset of the value). B-tree indices and hashmaps are ways to represent keyval stores for quick look-up (b-trees are convenient as they allow range look-ups, & are automatically sorted, while hashmaps aren't, but have lower overhead for lookup and storage).

In essence everything is keyval. A sparse array is an ordered keyval store with integer keys (also technically everything is ordered, too, but some orders are stable, and useful, while others aren't). A dense array is an adjacency-optimized version of a sparse array where the key is implicit based on computable offset within a larger dense array, your address space. RAM address space is also variations on that theme. Raw disk storage. And file systems. Everything is. Maybe I spend too much time messing with storage, but I can't see it any other way at this point.
I checked the first answer with a Google search. The first answer was right.
Do steamers make games popular, or do steamers go for popular games? Yes. Both.

And the sponsored time bought from variety steamers pretty much proves the effectiveness. It wouldn't continue happening if it wasn't worth it for the distributors. Since the links published during the stream are attributable to the source, they get a good signal that it pays off.

(+ Personal anecdote: I bought some games just because I saw them on Twitch)
Yes , while not close to anything Copilot is capable of , most importantly Intellisense is precise.

Programmers should value precision over productivity of generating code for something that has compile and run.
They said they would offer refunds, if that is what you mean:

> If you have a paid subscription to Neeva Premium, you will receive a refund for the unused portion of your subscription.
When Skynet takes over this is how the robots will amuse themselves.
UPI is just a simpler IMPS(which used mobile number) with email address like handle which again is either mobile@bank or email/name@bank just like Venmo. Nothing revolutionary at all. There is no customer care that you can call & have your problems addressed, you still need to go to your bank for any issues or refunds coz for some reason Indians are not exactly entrusted with being able to raise chargebacks
Show HN: Summary GPT – host a bot to summary Text, URLs, PDFs and videos for you
An AI-powered text summarization Telegram bot that generates concise summaries of text, URLs, PDFs and YouTube videos.
Yeah a lot of times is right, unless they are really complex subjects then maybe even less then 50
They did not, though; they offered a paid product, which is different, not better.
Deleting 80% of the marketing footprint of IBM.com
Only if you have the skill and ability to verify the correctness , a lot of developers who use it do not.

It is much worse than copying Stackoverflow answers as they at least for the most part have crowdsourced validation
This right here. I actually am strongly starting to believe that this is indeed what's going on.

I read the book Kingdom of Speech a few years ago and that also left me with the perspective that perhaps language has a lot more to do with how we think and perceive the world than most people like to admit. The book has been heavily criticized but I believe it made an interesting point about language.
thread: https://threadreaderapp.com/thread/1659941975519375360.html
Good question, and well stated.

I am the first to admit being a terminology geek.

I say "pharma" of any kind today implies overwhelming benefit compared to risk in making a disorder measurably better, and I expect doctors and pharmacists to know this better than me.

When it comes to solvents though, and materials much more toxic than appear in the pharmacopeia, I do have an unfair advantage in direct experience and decades of intense study for the most part.

Substance abuse or addiction is possible whether or not the substance is really a drug or even very toxic or responsible for physical dependence.

I guess my point is that people can use things, and develop a dependence of many different kinds, like they were a drug even if they are something as simple as a common solvent.

Availability may mean more than "drug seeking behavior" a lot of the time.

Sometimes it's straightforward toxicity-seeking.

Chemically, solvents are usually very simple industrial chemicals compared to the vast majority of true drugs, especially the drugs in the PDR which are mostly carefully crafted for particular patients, rather than general consuption (like beer or liquor) or non-consumption which is how I scientifically categorize the solvents overall.

When I see people treat drugs no differently than alcohol, or alcohol no differently than drugs, I just don't think that is the most realistic treatment.

Once again, I'm not the expert on metabolism but I do think the metabolic effects are far too dissimilar also.

Disclaimer: my first job was with a drug manufacturer, I got there after paying my way at the university giving chemistry lessons to pre-med students, but that's as far as I went in that field. Solvents OTOH, I've been handling and directly observing their effects for decades since.
>ithout even chugging along as a legacy systems provider (think OS/2).

Haven't the same happened to SGI, DEC(VAX), and OS/2 itself? All much bigger successes at their time than Symbolics has ever been.
They didn't break any law. They failed to practice common sense.

"In the 2015 model year, immobilizers were standard on 96% of other manufacturers' models, the institute said. But they were standard on only 26% of Hyundai and Kia models."

That's a serious lapse in judgement. Theft rate Hyundai and Kia w/o the device was 80-90% higher than the rest. They would've known that, yet they failed to act.
The lefty Europeans who want to deliberately shrink the economy
https://searx.space/#

https://wikipedia.org/wiki/Searx

Blocks tracking + search aggregation + self hostable
How does a nonprofit save anyone on tax?
So that was the answer to the halting problem.
The grandparent is referring to the fact the ARMv6-M and ARMv7-M architectures are Thumb only (and are different subsets of Thumb 1 and Thumb 2, to keep it spicy); so a Cortex M has zero instruction set overlap with a pre-Thumb ARM core, even though both are "32 bit ARM."
The buttons

"Get started - It's free"

and

"Pricing"

are right next to each other. I get it, but it feels a bit disingenuous.
The bitter lesson, that data beats understanding, reminds me of the more-energy beats efficiency in "Review: Energy and Civilization" (https://news.ycombinator.com/item?id=36013309) which notes that the pursuit of efficiency suffers from diminishing returns, whereas abundent energy does not.

In a similar way, parsimonious concepts and knowledge, while personally satisfying to researchers, are inherently limited; whereas massive data (including self-generated data, as for alpha-zero) are not.

Knuth sides with actual understanding:

> [I shall continue] to devote my time to developing concepts that are authentic and trustworthy (https://news.ycombinator.com/item?id=36012360)
These debates about how well GPT can think seem merely philosophical.

This is a perfect case of perfection being the enemy of the good.

Useful AI is here. Hard stop.

The impacts will be huge and unpredictable.

Billions will be made.

The world will change.

Humans will continue making rapid progress, via merging various AI methods and new breakthroughs.

Nevertheless, I enjoy reading the debate.

But anyone wringing their hands over how much is GPT thinking is missing the point.

These are companies making products.

This is not academic research.

It's just another tool in a long list of tools made by humans.

And it's already a productive tool.

This reminds me of the skepticism surrounding electric cars while Tesla was already growing by leaps and bounds.

The ship has sailed. The revolution has started. Progress will undoubtedly be rapid and continual.
In case you are not aware, UPI is basically a rebranded IMPS with Venmo style of address. Until UPI actually works internationally it is still a domestic payment system.
We are trying to find a way to return to $10/mo unlimited searches. It is not for the lack of trying that we are not there already and we are trying to ensure Kagi's long term sustainability. Very grateful to all our customers supporting us in this period.
I'm not sure if I understand what you're trying to convey with the following: "X Lectures on Chemistry" or "Y Lectures on Biology"? Chemistry and Biology are higher level abstractions of physics, Sure you can communicate the physical properties that are happening, but the abstractions we create help us to communicate the relevant physical properties we are trying to understand/ manipulate.

Also, I love Feynman's work, It's what made me see the beauty in physics, but to say that it covers every base is completely wrong and an injustice to his findings and the broad nature of the field.. Are you trying to say that he was great at communicating abstract concepts?
Most people who are annoyed by ads just use an ad blocker. I know there are people with ethical qualms about it, but "hates ads but has ethical aversion to blocking them" isn't a large enough market to support a VC-funded startup.
Given that these are ~60 years old by now, can someone that knows both the fields they cover and has gone through them explain what, if anything, they are missing ?
Have you checked out Jackett?
I think past hackernews comments and posts have proven that this is at least partially wrong. The drag is real. How many posts are there about duckduckgo -- in its golden days; that it was better and should be the default.

How much people preaching time and marketing has been spend on that? And yet the average user, even technical users does not care.

It has to be a vastly different experience, e.g. ChatGPT enabled search -- like Bing now say -- and even then, the monopoly moats placed by Google and the decades of inaction by DoJ have created what we have now.

Chrome should have never been allowed to be under Google or Alphabet. Paying to set a search engine to this scale should not have been allowed as a practice.

[Edit:] P.S. And to answer all the thousands of comments by users here of why Google has gotten worse: because it can; it can get worse while maximizing its ad profit; DoJ allowed it.
Make up a plausible conversation of what Donald Knuth says to Stephen Wolfram about chatGPT.

Donald Knuth: Stephen, I've been studying the OpenAI's GPT model, in this case, ChatGPT. I'm impressed by how much it has improved over the years and the capabilities it has now.

Stephen Wolfram: Yes, I've been observing the progress of GPT too, Donald. It's an intriguing representation of where we've arrived in computational linguistics and AI. Yet, I find it fascinating how much of its learning depends on the breadth and depth of training data.

Donald Knuth: Indeed, Stephen. It’s remarkable to see that it can produce plausible-sounding text across a wide range of topics. However, like any tool, it still has its limitations, particularly when it comes to producing entirely reliable or context-specific information.

Stephen Wolfram: Right, the lack of a true understanding of the content it's generating is its main limitation. For instance, it can’t make real-world interventions or learn from specific interactions beyond its training cut-off. But the way it has been designed to simulate conversation is quite fascinating, even though it's fundamentally a pattern matching mechanism, rather than a thinking entity.

Donald Knuth: I share your perspective, Stephen. It's impressive to see how far machine learning models like ChatGPT have come, but there's a lot of ground to cover when it comes to the essence of comprehension and knowledge application. In terms of mathematical representation and handling, it leaves a lot to be desired.

Stephen Wolfram: Absolutely, Donald. Mathematical models can offer so much more than pattern recognition. If a model like ChatGPT could incorporate more of the principles of computational thinking, it might generate responses that are not just statistically likely, but also meaningful in a deeper sense.

Donald Knuth: I agree. It seems like the next frontier for models like ChatGPT could be the integration of symbolic computation or at least some form of knowledge representation that mimics understanding. That’s where your work on Mathematica and Wolfram Language can be really enlightening, Stephen.

Stephen Wolfram: That's a compelling thought, Donald. Taking advantage of the power of symbolic computation, we could push the boundaries of what AI systems like ChatGPT can do. I suppose this is one of the thrilling parts of living in this era – the possibilities for what we can build next seem endless.

Donald Knuth: Indeed, Stephen. It's an exciting time for both computer science and AI. We're in a unique position to shape the future of these technologies and their impact on society.
> Neeva failed because they didn’t understand distribution

No, the team and its VCs fully understood distribution.

Neeva never got to the point of having a compelling enough product where cost of user aq was below LTV for a large enough market.
So the families who are coming up through South America. Who don’t have money. They are the black money folks? The families buying cheap condos in Thailand for 300k to send their parents to live because of freedoms and better quality of life? How’s 300k black money? This is ignoring the high unemployment rate at the moment. I guess with so many people losing their jobs that means there’s lots of opportunities. The foreclosures on homes is an indication there are more jobs right?
Missing from this the article: outcrossed, inbred, and hybrid vegetables. Inbred are the only seeds from your produce worth saving.
So if we gathered all books, writings, games, etc on ever published on chess could we develop a grandmaster player without having to build a deep blue type system?
fwiw the prompt wasn't that good.

Here's my prompt: Make up a plausible conversation of what Donald Knuth says to Stephen Wolfram about chatGPT.

Output:
Donald Knuth: Stephen, I've been studying the OpenAI's GPT model, in this case, ChatGPT. I'm impressed by how much it has improved over the years and the capabilities it has now.

Stephen Wolfram: Yes, I've been observing the progress of GPT too, Donald. It's an intriguing representation of where we've arrived in computational linguistics and AI. Yet, I find it fascinating how much of its learning depends on the breadth and depth of training data.

Donald Knuth: Indeed, Stephen. It’s remarkable to see that it can produce plausible-sounding text across a wide range of topics. However, like any tool, it still has its limitations, particularly when it comes to producing entirely reliable or context-specific information.

Stephen Wolfram: Right, the lack of a true understanding of the content it's generating is its main limitation. For instance, it can’t make real-world interventions or learn from specific interactions beyond its training cut-off. But the way it has been designed to simulate conversation is quite fascinating, even though it's fundamentally a pattern matching mechanism, rather than a thinking entity.

Donald Knuth: I share your perspective, Stephen. It's impressive to see how far machine learning models like ChatGPT have come, but there's a lot of ground to cover when it comes to the essence of comprehension and knowledge application. In terms of mathematical representation and handling, it leaves a lot to be desired.

Stephen Wolfram: Absolutely, Donald. Mathematical models can offer so much more than pattern recognition. If a model like ChatGPT could incorporate more of the principles of computational thinking, it might generate responses that are not just statistically likely, but also meaningful in a deeper sense.

Donald Knuth: I agree. It seems like the next frontier for models like ChatGPT could be the integration of symbolic computation or at least some form of knowledge representation that mimics understanding. That’s where your work on Mathematica and Wolfram Language can be really enlightening, Stephen.

Stephen Wolfram: That's a compelling thought, Donald. Taking advantage of the power of symbolic computation, we could push the boundaries of what AI systems like ChatGPT can do. I suppose this is one of the thrilling parts of living in this era – the possibilities for what we can build next seem endless.

Donald Knuth: Indeed, Stephen. It's an exciting time for both computer science and AI. We're in a unique position to shape the future of these technologies and their impact on society.