skysight-inc/HackerNews huggingface dataset:
Why not? There's certainly a group of consumers who will pay more for higher quality. I'm one of them. 2-3x current SSD costs for a better quality SSD would be completely fine with me. You're not paying more than you have to for the same product, you're purchasing a different product.
Imagine if we had to write `整数 i = 0`.
As mentioned, much of our discussion is rote parroting. I can usually go into any hackernews thread and roughly know what the top discussions are going to be. It's not surprising that an AI trained on a large portion of the internet would thus look human like.

If you really poke at GPT, you begin to realize it's fairly shallow. Human intelligence is like a deep well or pond, where as GPT is a vast but shallow ocean.

Making that ocean deeper is not a trivial problem that we can just throw more compute or data at. We've pretty much tapped out that depth with GPT4 and are going to need better designs.

This could only take half a decade or it could be half a century. Plenty of enterprises stagnate for decades.
Dumb is the last adjective I would use to describe Knuth, even if you believe that becoming old makes you dumb, like you clearly do.

My advice to you is to never dismiss anyone's opinion just for being old. And I hope you lose your ingrained ageism before you become old yourself, otherwise you'll find old age intolerable.
realistic perspective: it's not a single problem.

Oil producers and cartels raise prices because... money's being printed. Because Biden shuts down new pipelines, slows oil leases, is hostile to energy production. Demand craters because of lockdowns that don't work... then ramps up because the world tries to return to normal after a "pandemic".

Almost like an increasing demand with a dipshit anti-energy President is going to have disastrously bad results.

Seriously though... the problem isn't "evil companies" doing shit like making money (how dare they!). The problem is multifaceted.

Did you have an issue when companies closed at record levels because of bad decisions that caused the slowdowns? Or were you cheering because those companies got what was coming? do you think that the surviving companies don't deserve profits after years of losses?
>The most useful things I know of built in a Lisp are Crash Bandicoot games [...] But both of those left out the garbage collector, which is one of the main things Lisp programmers would always act endlessly superior about.

GOAL was a target DSL in Common Lisp, so garbage collection was still aiding the development.
Eh, if my memory serve me correctly among us only got popular after some streamers started playing it, same applies to a lot of smaller games. vampire survivor too, I think ?
I also do about 50/year. It's entirely for recreation. A lot of the books are non-fiction and educational but I'm not doing work. I'm enjoying myself and there's no outcome I'm hoping for besides that enjoyment in the moment.
Whose leaving? Folks with black money trying to park abroad because they know CCP can crack down vs safety of parking wealth in western housing where dysfunctional politics and entrenched interests won't go after real estate. Meanwhile actual top tier talent being retained and even returning because more opportunities in PRC than ever. They're the ones pushing PRC up these indexes. Even PRC students who go abroad aren't cream of crop anymore, many just have wealthy enough parents who know their kids won't make it in PRC, aka the bench team. Who still do good enough work to fill up space in US / western institutions.
The difference is that GPT-4 is already useful even in its current imperfect form. It’s not a 90% thing that needs to get to 100% for us to use it.
I hear you, but it still seems redundant to me. Take the next use of the phrase:

> The developer's team, role and responsibilities, and seniority all impact their lived experience.

Why not just "all impact their experience"?
> It gives an answer with complete confidence, and I sort of believe it. And half the time, it’s completely wrong.

That's bullshit, unless you are asking questions specifically designed to make GPT-4 hallucinate. For most real-world, everyday topics, the accuracy is close to 100%. GPT-4 would be utterly useless otherwise.
It means people get better prices than they otherwise would.

US markets are pretty bad along many axes (e.g. brokers have an obligation to route flow to wholesalers in an effort to maximize price improvement, but this does not necessarily result in the best price on a per-order basis the way that a limit order book populated by wholesalers and other retail orders would. startup costs to get "fair access" to institutional flow and 0 captive retail flow are multiple millions of dollars because of exchange revenue models. retail maker orders will literally never interact with retail taker orders. etc.), but it's difficult to argue that retail would pay less in fees and spreads if they had to always interact with quotes manually created by humans.
Reminds me of the Luke in the mandalorian. The cgi luke looked like a teenager, but Luke wasn't a Jedi until he was older and the actor had scarring from being in a car crash before The Return of the Jedi.
Firstly, if humans created sapience, we created it with all the interacting elements that evolution used plus all the things that we used.

Secondly, maybe sapience isn't as big of a deal as we thought compared with all the other things that evolution did. Remember that biological entities have to figure out survival, reproduction etc. Sapience emerges as a byproduct but the selective pressure is towards those things so sapience is only selected for to the extent that it also moves forward those other goals.

By contrast, LLM training is just focussed on the task of making the model better. The model doesn't have to figure out how to feed itself, ward off predators, not accidentally die in the myriad ways things die, reproduce itself etc. It's way more specific. It doesn't seem unreasonable to think that the complexity would be lower given it's not trying to achieve nearly as much.
Feynman Lectures on Physics (1964)
The sellers are but one part of the larger market system at large.

If SLC NAND went extinct, that's because both the sellers and the buyers (read: customers, aka end users) didn't see value in reliability as much as other factors like storage density and price-per-bit.

You, as someone who does want reliability above all else, are an outlier.
One could argue it was the other way round. It was done to break cash (since it cannot be tracked) and boost digital payments.
The article begins with:

Engineering leaders have long sought to improve
the productivity of their developers ...

A goal every organization can support, no doubt.

What the rest of the article fails to discuss is how upstream-processes determine developer productivity far more than the three dimensions identified. To wit, the clarity and focus of "what and why" for an effort determines the "how and when." Which makes the answer to "knowing how to measure or even define developer productivity" no longer elusive, but instead quantifiable.

Few, if any, qualified developers I have worked with have a poor DevEx when the work needed to be done is well-defined (what) and can be explained such that a solution is identifiable (why). Solutions almost always flow from these (how), with an ability to communicate the work effort possible (when).

Skip the up-front investment by stakeholders on "what and why", "how" will remain nebulous, and "when" will be just a guess.
For me the n-sphere problem makes intuitive sense if you think of it as a subset of the volume of a unit n-cube:

No matter what n is, a unit n-cube has volume 1^n = 1. So the problem is equivalent to: what proportion of that 1 is inside the n-sphere? And then think about how the difference between a unit cube with a unit cylinder vs. a unit sphere in it: the cylinder takes the same proportion as a circle from a square; the sphere takes a subset of that because instead of just pushing the circle through the cube, you rotate it around a point.

A rigorous proof would show that the series in fact trends to zero, and not just monotonically smaller values, but for intuitive purposes, that's good enough for me.
The Hidden Dangers of the Decentralized Web
I think domain verified namespaces would make a difference. If I own example.com, it makes sense for my namespace to be @example.com (pypi.org/@example.com/project).

If all namespaces were domain based it would reduce impersonation and confusion while making it much easier to assess trustworthiness. If I can definitively associate a namespace across GitHub, PyPi, the internet, I can get a much better idea of whether or not I should trust someone.

I also think that could evolve into some type of attestations about money already being spent. I call it collateral attestation.

For example, if I use GitHub Sponsors to donate to a project, GitHub could attest to that. If I donate $50 to the PyPi project, they get an attestation that someone donated $50 to them and I get an attestation that I donated $50 to someone.

Even if it's only domain validated namespaces, at least that's something that costs a little bit of money every year and it scales without a lot of human intervention compared to most other ideas.
If referring to TinyScheme being used for sandboxing, that still uses garbage collection.

https://github.com/sungit/TinyScheme/blob/master/tinyscheme-...
As someone who straddles both Python and Java world, I don't seem hear this kind of malicious packages problem with Maven (central) as I do with PyPI. What could be the difference? Perhaps Java world is full of old farts like me doing mundane thing with a handful of well established libraries.
Yup. I've been loving it since i switched over like 6 months ago
CDC and FDA recommend clinicians and patients stop using and discard EzriCare Artificial Tears and two additional products made by the same manufacturer, Delsam Pharma’s Artificial Tears, and Delsam Pharma’s Artificial Ointment.
Getting your fiber to go straight (in the great circle sense) from point A to point B can be quite challenging given the presence of mountains, water, and people or countries that control the land in the way.

For what it’s worth, hollow core fiber is available now, although at least the one vendor I found is very very expensive. I haven’t found an entirely clear answer as to the group velocity in hollow core fiber (it’s not in the data sheets), but it ought to be pretty fast. (The group velocity in vacuum-filled waveguide is not the speed of light in a vacuum — it’s slower.)
As opposed to a statistic like lines of code written. As opposed to ‘experience’ in the prescriptive sense like ‘The developer experience here uses Docker and Webpack and therefore is modern and therefore is good’. As opposed to ‘experience’ like years-of-experience in a certain role/technology.

I’m not the author, but I found that to be a highly important distinction for them to call out. It is about the way that each individual developer feels during each individual day, in a way that is hard to capture with statistics and summaries and lists of technologies and descriptions of processes.
> I agree musicians aren't generally IP maximalists, but most musicians aren't generally capitalists either, so that doesn't say much.

Songwriting etc seems like a very capitalistic enterprise to me. You evaluate the market then invest X hours making something that is then judged on the open market. This is very different from most people simply work for someone else.

The parts of the music industry that’s IP maximalists on the other hand tend to simply buy existing IP rather than create it. They essentially face zero risk from infringing on others copyright because they aren’t creating anything. While that’s a form of investing there is several forms of investment in feudal society like making loans etc. Investing on it’s own doesn’t necessarily mean capitalist.

> a society that abolishes private property

I was suggesting it as a prerequisite rather than the only need. Obviously a communist society can enforce it’s laws without being capitalist.

That said there are many online communities which promote creativity etc based on acknowledgement and social credit rather than ownership. I don’t know how you could end up with a stable capitalist post scarcity society without any form of private ownership, but it’s one of those things we don’t have any direct evidence for either way.

Academia may actually be relevant model here. Mathematics isn’t owned by the individual who discovers it, but they still control and can invest in the means of production Aka themselves. Calling that capitalist doesn’t seem to fit, but it seems closer than the other options like socialism, anarchy, etc.

Hopefully that’s taken as food for thought rather than just being a ridiculous argument.
Yeah sounds like you are in constant hypervigilance mode which is part of the limbic system for fight or flight. It is exhausting. It can be very hard to identify the root cause especially if it is a state that developed from childhood but its a coping mechanism to stress. It was likely adaptive for something in your past but that you likely don't need anymore... and as you say some events you've experienced so you might even have PTSD. But you can train your brain as they say...
I would imagine that this project was a Not Invented Here sort of thing when Redis was presented as an option.

Total conjecture on my part.
I personally find value because it saves me time. I’ll ask chat-gpt to write something slightly more complex than boilerplate code for me based on some requirements. Because I’m an “expert” I can read/run the code and message back improvements and tweaks until to arrives at something satisfactory. It certainly doesn’t always produce correct code from the start and I often encounter syntax errors or code which doesn’t work. However, I’ve found it pretty good at remedying those issues when I describe what’s wrong.

It produces something which is a good enough starting place. Sure, I could have written the code myself because I already know how. But I’ve found it saves me time and require minimal effort.
The brain does a lot of hierarchical prediction with sense data. When new information comes in, it references "something" and adjusts the sense data likewise.

That's why when you shift your eyes quickly, you see blurred images pass by. In reality, you should be seeing complete black because the brain doesn't actually process visual information that shifts so quickly.

But your brain "knows" it should see...well something. And so it fits that blurred passthrough as compensation. Completely made up data. But not ungrounded data, data that seems like it should fit according to that "something". That "something" being referenced is a world model.

That's a sub-conscious example but you can think of a lot of times people consciously reference "something" to better take actions or make decisions.

For Othello-GPT, training seemed to induce a learned board state that the model referenced at every Inference to make moves.

It seems that fully accurate or not, deep predictions might require "something" to reference. Else perhaps you might need to derive everything from first principles at every turn. Not very efficient.
Or....."I don't think that is true."

;)

Or "See Spot run."
America still brain drains best, issue is really PRC talent production so great post academic reforms that limits of US immigration / global brain drain efforts is losing competitiveness to sheer numbers that stay in PRC.
I hate how India delivers competent public services. It is oppressive & authoritarian.

The oppressive Indian Railways make it difficult to move around in the nation, while the amazing private network of high speed trains in the US is lightyears ahead.

I love the complex tax code of USA. An incompetent govt like India's would never be able to produce software as pleasing to use as Turbotax.

Allowing private companies to monopolize public contracts is great for the health of the nation. Pizza for school lunches is why Americans dominate sports around the world.

India should take inspiration from the free nation of USA. No surveillance here. What is a Snow den ?
> Making that ocean deeper is not a trivial problem that we can just throw more compute or data at.

You can't possibly know that, given that we don't actually understand how LLMs work on a high level.

> We've pretty much tapped out that depth with GPT4

GPT-4 is three months old and you're confident that its working principle cannot be extended further? Where do you get that confidence from?
Why not? Your brain is already making predictions about what you expect to see and hear as a part of your perception of reality anyways.
GPT-4 is pretty amazing but I, too, feel this is being overhyped. For me, a sobering example is how OpenAI does math (eg [1]). Specifically, the model clearly doesn't really understand multiplication and "learns" it from training data. This tends to get the first few and last few digits right for a simple multiplication with 6-7 digit numbers.

Now you can solve that with plugins (eg training the model to recognize math problems and have access to a calculator) so it's a solvable problem but you realize there's an extremely long tail of such problems. It goes to show that GPT-4 isn't "magic" and we still have a long way to go.

[1]: https://www.reddit.com/r/OpenAI/comments/12donja/gpt4_and_ma...
This paper is startling in terms of a world model and how it seems to emerge in a specific example in the step from GPT3.5 to 4. https://arxiv.org/pdf/2303.12712.pdf

One of the prompts is "Here we have a book, 9 eggs, a laptop, a bottle and a nail. Please tell me how to stack them onto each other in a stable manner."

Gpt3.5 is hopeless. It says start from the bottle, balance the nail on top of it, then the eggs (somehow) then the laptop, then the book.

GPT 4 says put the book first, then the eggs on top of the book in a 3x3 grid, then the laptop, then the nail.
No just HFTs use microwave. Most big DC players (e. g. Equinix) provide access to a MW antenna, as well a dedicated links to PBs and exchanges PoP.
Sam Altman said it himself. He seems like a reasonable source.

If you're familiar with other fields of AI, adding more and more layers to ResNet was the hotness for awhile, but the trick stopped working after awhile.
“We’ve discovered”??

I’ve discovered saving by using the annual plan was a mistake. I’ll avoid that mistake if I try Kagi or something else.

But really miss Neeva’s integrations that were cancelled recently. Having the ability to search my GitHub repos & the internet simultaneously was pretty cool. Add to that Notion, and other services. It was great for the the 4 months it was around but with 8 months left, I feel ripped off but it doesn’t seem worth getting too worked up over.

Just can’t let that happen again.
> The latter requires complex cognitive faculties not present in a mathematical model of the English language.

The training is on images and text, and the output is text. But nothing constrains the model in the middle of those two ends to only model based on language features, as opposed to world features discoverable from the language and images.
Back in the 80s my scout troop used Skin So Soft, diluted with water to like 10:1, as an effective mosquito repellant. As an added bonus, it doesn't irritate the skin like other repellants and has a nice scent (which was probably important for being around a bunch of pre-teen boys).
Taking RLHF into account: it's not actually generating the most plausible completion, it's generating one that's worse.
>this seems like a poor decision by the Japanese government.

Granted the article linked doesn't explain anything: The Japanese government, specifically the courts and law enforcement, are simply exercising demands made by Kadokawa concerning copyright infringement.
I'm skeptical that an "unpublished manuscript" should count. If someone quietly has an idea, but tells no one, it has no value to humanity. If the world wasn't told, then the world has every right to say "that does not count".
This is a thought that LLMs caused me to have: the emotion of surprise is just the brain model of the world discovering that it had been poorly calibrated, or making poor predictions.
> while it's economy is collapsing.

Huh? Funny how 1/5 of the world economy can collapse and it's not even noticable.
The cat has been deprecated half a year ago. ;-)
> A human being who did that without ever playing a game would also start out better than a typical novice.

I am quite skeptical of these arguments along the lines of “imagine a human read everything written on the topic…”.

What humans are doing when they read something is not what neural nets are doing when they read something. Humans are (idealistically) doing something like Feynman’s description of how he reads (or in this case, listens to) a theorem:

“I had a scheme, which I still use today when somebody is explaining something that I’m trying to understand: I keep making up examples. For instance, the mathematicians would come in with a terrific theorem, and they’re all excited. As they’re telling me the conditions of the theorem, I construct something which fits all the conditions. You know, you have a set (one ball) – disjoint (two balls). Then the balls turn colors, grow hairs, or whatever, in my head as they put more conditions on. Finally they state the theorem, which is some dumb thing about the ball which isn’t true for my hairy green ball thing, so I say, ‘False!’"”

Bret Victor’s description of what “really good programmers” are doing is also related:

“[showing the code for binary search] In order to write code like this, you have to imagine an array in your head, and you essentially have to ‘play computer’. You have to simulate in your head what each line of code would do on a computer. And to a large extent those who we consider to be skilled software engineers are just those people who are really good at playing computer.”

I think when we imagine an LLM as a human who’s read everything ever written in chess but never played an actual game, we’re actually tricking ourselves - because that hypothetical human would be ‘playing chess’ inside their head by imagining the pieces and moving them according to the rules they had read[1]. LLMs are not doing anything like that when they read about chess. So it’s a very restricted (or perhaps more accurately, a very different) kind of ‘reading’ that we don’t have any intuition for. Since the ‘reading’ that we do have an intuition for is smuggling in exactly the kind of “modeling the world” ability we’re looking for, it’s not surprising that this argument would incorrectly lead us to believe we’ve found it in LLMs.

1: In fact the very best computer chess is achieved by AlphaZero which was trained exclusively on “playing chess in its head”, and it beats even the most powerful and optimized search algorithms like Stockfish looking 20 moves ahead.
Alright, I give. What the heck is "sapience"?
That's right. FoundationDB and ArangoDB, among others, are also ACID-compliant, I believe.
Seems to be based on RocksDB. But I wonder if the persistence it is like Redis's persistence (where the persistence is just snapshot/txn-log style)

> JunoDB storage server instances accept operation requests from proxy and store data in memory or persistent storage using RocksDB. Each storage server instance is responsible for a set of shards, ensuring smooth and efficient data storage and management.
$25/month is a bit steep still for those of us that use over 1000 searches a month, although I am aware they are transparent about their costs.
I mean technically yes, but the reason I feel it is nothing to cheer about is if you understand how these public funded corporations work in India. They are mostly the worst of both worlds - you get a monopoly with lousy service.

For e.g the case of IRCTC which is the commercial arm of Indian railways and look at the insane price gouging that has been happening.

In the end, people end up over paying for sub-par products/services.