skysight-inc/HackerNews huggingface dataset:
Which creates shallow artists who just want it to "look pretty now." Not saying there isn't a place in the world for that, but there are always trade offs with tools that manipulate your human vision of art, and to say "there are too many denoise filters, just pick one for me" will be severely limiting when you realize that what you really wanted was grain removal, but that's not how your AI denoise filter works.

Again, there's room in the world for all manner of software uses. But to argue that Darktable is bad because it gives too many options, misses the goals of a great many artists, which is to understand what's happening to the pixels they captured in the field.
I didn't see that, but that's also a non-standard and unclear way to cite a source, which is why I and many others didn't see it.

Also, you didn't address my main criticism, which is that you made a claim that's stronger than what your source says.
RAM is cheap, my time is not. VSCode is the best game in town (for me), and my 32GB computer has no problem with its RAM requirements. Even 8GB would be enough for VSCode depending on what else your toolchain requires.
I believe the Commodore 64 power supply I am using I ordered from c64psu.com. I did not evaluate the quality in any way, but years down the line it hasn't failed me. So, at least working replacements are available, and while they're not necessarily cheap, it's probably worth it considering you're definitely right about the fact that the Commodore ones tend to fail in pretty ugly ways.
>Much of South India and Eastern India was already British ruled by the early 1800s.

No they were not. They were run by private East India Company. With the revolt of 1857 and quashing of the rebellion, the official Raj by the British Royals began

https://en.wikipedia.org/wiki/Indian_Rebellion_of_1857

You are still bickering over pedantic stuff. Britain profited from slave trade. They just didn't want to use the term "slave" and used "colony" instead.
Just use ChatGPT to summarize its own output. It’s like running your JPEG back through the JPEG compressor again!
When I'd watch the financial news on TV, they would always bring on the "technical analyst", show a graph of the stock price, and then hand-draw some lines on it, and then spew out various technical terms for it guaranteed to impress.

Me, I always regarded technical analysis as drawing pictures in clouds.

If any of those analysts were worth spit, they'd be working for a hedge fund, not the network.
> bit like Tom Hanks suddenly playing a vicious assassin

FWIW, Tom Hanks was in Cloud Atlas, where he played at least one evil character (though it was Hugh Grant I remember as more vicious, which was also out of type for him, at least at the time).
OpenAI's stance is "any data we can get our hands on is fair use for AI training". They aren't hiding anything.
I work in internal tools development, aka platform engineering, and this is interesting:

> That's part of the problem. But companies also are unwilling to pay to do any of the things that you've described. There is no punishment or fine that is actually punitive. Protecting (short term) profit is more important than protecting users' data --- it's even more important than protecting the (long term) profit potential of a company with a good reputation.

Frankly, any company that says they're a technology or software business should be building these kinds of systems. They can grab FOSS implementations and build on top or hire people who build these kinds of systems from the ground up. There's plenty of people in platform engineering in the US who could use those jobs. There's zero excuse other than that they don't want to spend the money to protect their customers data.
Thank you! Please let me know how your second attempt goes.

AI and no code platforms will merge into new high-level software creation tools. We already view Glide as a hybrid—try our AI component for the bottoms-up view. Our top-down AI approach will soon be live in our onboarding.
We've had to invent the term because "junk food" isn't well defined or understood. A lot of ultra processed food wouldn't be considered junk food by many people and some junk food like cakes and pastries might be considered processed but still not be considered "ultra processed".

As a new term, the definition of "ultra processed" is still being narrowed down and clarified, but at least it isn't saddled with several decades of past definitions or ideas of what junk food is or isn't.

It's become clear that ultra processed foods have major negative impacts on health in ways other foods don't. My guess is that as research into ultra processed foods continues we'll eventually end up with even more new terms to drill down into specific types of foods, production methods, and even marketing practices that are harmful/unhealthy.
Same In Sweden where this has been a fixed point on television for decades. Christmas Eve - julafton in Swedish, when Christmas is celebrated here - means "Kalle Anka's jul" (Donald Duck's Christmas) on TV, 15.00, 1 hour long. New year's eve means "Grevinnan och betjänten" (the Countess and the servant) which is the Swedish name for this skit.
Looks great, well done
Past performance is no predictor of future returns.
This seems like a non-sequitur unless you’re assuming something about the amount that people use models.

Most web servers can run some number of QPS on a developer laptop, but AWS is a big business, because there are a heck of a lot of QPS across all the servers.
Simulating Dark Mode on iPhone
Just Words (YC W24) Is Hiring
Let’s normalize offline web apps, then. Your examples at the end go back to needing technical chops, and in the end, everything gets bitrot.

The biggest problem is with apps that show you content. Web sites give the user more control over what content to save, better exposure to scrapers and APIs, standard navigation to every other web site.
Not necessarily. Manufacturer recommends maintenance procedure but airlines can vary them, see American Airlines 191. https://en.m.wikipedia.org/wiki/American_Airlines_Flight_191
This comment has ChatGPT explain the problem which is surprisingly understandable - https://news.ycombinator.com/item?id=42559401
> How many desktop PCs aren't certified to run Windows 11? Can you hack it and make it run with esoteric commands and dubious registry edits? Sure, but then you're in the same spot Linux users are.
> How much hardware do people throw away because manufacturers don't write updated drivers anymore? Right, you're back in the just about everything works but situation again, except in Windows.

A significant amount of goalpost moving is happening here. My original point holds true: general Desktop Linux, installed on $randomly-bought-general-class-desktop-or-laptop-machine, is not the generally solid experience that Windows is.

> When you add in and then Windows includes telemetry and data mining that Microsoft won't allow you to turn off and won't tell you what is included I can't see why anyone would want Windows.

I wasn't even debating these points with you. I don't think anyone should use Windows either, but here we are.

> The Steam Deck literally does not have these problems. It's the the Linux Desktop, perfected!

It is a gaming machine that happens to run Linux, and you have done nothing to convince me otherwise.
Can you substantiate this a bit more? Do you have a link?
Yes! We should be extremely cautious about embracing approaches that make LLMs even more inscrutable. Having CoT, however unreliable it is, is nonetheless a huge boon for model evaluation that we should not give up so lightly.
I see, I read the docs slightly too quickly. Still, though, I would have expected a conspicuous warning about exceptions in those calls, because MS is in on C++ (so they can't hide behind "but we expected only C") and apparently(?) the APC machinery doesn't catch and block exceptions in user code.
You bring up a great example. Mozilla poured tons of resources and had very smart people working on Rust, Servo, and related tech projects to improve Firefox. Where was the surge of market share?

We’re at a point where the core functionality of browsers is very mature. It’s unlikely that any amount of investment will produce a browser that is significantly faster at things like JS execution or rendering compared to Chrome.

So alternative browsers add things like better ad blockers, more privacy protections, or maybe LLM summaries to enhance the core browser experience instead.
Why it's worth paying attention in math class.
S&P 500 posts 23% gain for 2024
> In software, a single bit-flip or typo can lead to catastrophic failure

That can happen in physical engineering too, if it's not done right. Likewise, if a single bit flip can lead to catastrophic failure, that's an indication that the system was poorly engineered.

The problem with software is not so much that it is fundamentally different in terms of its failure response (though there certainly are differences -- the laws of physics provide important constraints on hardware that are absent in software) but rather that it is so easy to copy, which tends to entrench early design decisions and make them difficult to go back and change because the cost is too high (e.g. syntactically-significant tabs in 'make'). But even that can and does happen in physical design as well. The Millennium Tower in San Francisco is the best example I can think of. The World Trade Center is another example of a design that failed because the failure mode that destroyed it was not part of the original design requirements. When the WTC was designed, no one imagined that someone would fly a jet into them some day. Today's adversarial environment was similarly hard to imagine before the Internet.

It absolutely is possible to engineer software for robustness, reliability, and trustworthiness. The limiting factor is economics, not engineering.
Isn't this general idea what the 100 Rabbits people theorize? i.e with uxn and all that
Alibaba slashes prices on LLMs by up to 85% as China AI rivalry heats up
I'm also reminded of top-_k_ training/sampling, where you throw away the inconsistent samples: https://arxiv.org/abs/2002.06224 "Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples", Sinha et al 2020.

Which seems like it would be roughly equivalent to throwing away gradients if they are inconsistent...?
The deep economic integration with the US has lead us to use this [0] insane flowchart to determine which units to use in which situation. It appears to be a joke at first, but it's accurate.

[0] https://images.app.goo.gl/kUpkGubsWXe9ShP19
These kinds of tricks are still used today. They're not so useful if you need a reciprocal or square root, since CPUs now have dedicated hardware for that, but it's different if you need a _cube_ root or x^(1/2.4).
For sure they will read a pdf and not a google doc.
I don't think so. He doesn't care about the source code or want the source code. He just wants an executable file. If there was the exact same project that was closed source and had an .exe file he would have never even gone to the github.
Why do you keep asking me for a solution, without agreeing with me on the problem at hand?

We dont even agree that residents drive up costs. I dont agree that is a given.

If we assume residents are a cost, the solution is still simple. you just attach the cost of training to the physicians salary. Then, whatever hospital they work at will have pay for the training.

Last, the residency program costs are tinny in comparison to overall physician costs. Back of the envelope math shows the 20 billion program is a few percent physician costs, which are themselves only a part of healthcare costs.
Indeed it makes sense that very primitive organisms had it and passed it on via DNA or even some other form of chemical code. Still some DNAs were flawed and other had more advanced survival techniques.

Still that boils down to: survival has to be a property (or even the definition?) of life.

So maybe the question of life introduction is, when did survivorship emerge?
There's something kinda beautiful about this.
This was the original plan for the iPhone. The web just needed to catch up. But now that everything is app based, it's hard for companies to want to give up space on a user's home screen. Web apps can be added to the home screen, but it's not the default state, like an app is.
Hint at an axion-like particle from GRB 221009A
If I recall correctly, these terms were used more or less interchangeably for a few decades, until 2020 or so, when OpenAI started making actual progress towards AGI, and it was clear that the type of AGI that could be imagined at that point, would not be of the type that would produce singularity.
When I was a kid, China was a lot better integrated with the international community. Right now their relationships are far and few between, rarely featuring first-world nations.

If Russia couldn't beat NATO in a pitched fight against the rest of the world, neither can China.
Unfortunately complex tools will be misused by part of the population. There is no easy escape from that in the modernity of possibilities. Look at the Internet itself.
Lost me at "DSLs".
There is no shortage of material on the topic, but I would recommend "Chip War" by Chris Miller as a complementary read.
The problem is that many Mastodon servers go far beyond what you describe. Your server can blocked because it doesn't block somebody else, for example. Or because it doesn't have such and such in its rules.
Small discussion (16 points, 21 hours ago, 6 comments) https://news.ycombinator.com/item?id=42554922
In the U.S., there is typically a separation between calculus and real analysis. Though, the amount of difference between the two depends on the university.

In calculus, there is more emphasis on learning how to mechanically manipulate derivatives and integrals and their use in science and engineering. While this includes some instruction on proving results necessary for formally defining derivatives and integrals, it is generally not the primary focus. Meaning, things like limits will be explained and then used to construct derivatives and integrals, but the construction of the reals is less common in this course. Commonly, calculus 1 focuses more on derivatives, 2 on integrals, and 3 on multivariable. However, to be clear, there is a huge variety in what is taught in calculus and how proof based it is. It depends on the department.

Real analysis focuses purely on proving the results used in calculus classes and would include a discussion on the construction of the reals. A typical book for this would be something like Principles of Mathematical Analysis by Rudin.

I'm not writing this because I don't think you don't know what these topics are, but to help explain some of the differences between the U.S. and elsewhere. I've worked at universities both in the U.S. and in Europe and it's always a bit different. As to why or what's better, no idea. But, now you know.

Side note, the U.S. also has a separate degree for math education, which I've not seen elsewhere. No idea why, but it also surprised me when I found out.
Unfortunately, it's missing a lot of commonly used paid fonts (Operator, ...), but I understand that it is mostly a licensing problem.

For the last couple of years, I am a Cartograph[1] lover. But Connary had changed the license pricing this year, making it a one of the more expensive coding fonts :(

[1] https://connary.com/fonts/cartograph/
Is there any sandboxing to prevent access from the SIM card computer to information on your phone? And if so, absent of some (admittedly not very unlikely) 0day allowing sandbox escape, what would a malicious SIM program be able to do?
Crowdsourced data is the easiest to fake unless you can somehow ensure that you have a completely unbiased population (which is impossible). There's a reason why certain models do so well on upvote-based leaderboards but rank nowhere on objective tests.
Video generation was covered in OP
> They can spit html and css, but not Google doc.

Wow. At this stage, I think people are just searching for excuses to complain about anything that the LLM does NOT do.
Neural networks are generalizing things as part of their optimization scheme. The current approach is just to dump many layered neural networks (at the core) as in "deep learning" to solve the problems, but the networks are too regular, too "primitive" of sorts. What is needed are network topologies that strongly support this generalization, the creation of "meta abstraction levels", otherwise it will get nowhere.

Biological networks of more intelligent species contain a few billion neurons and upwards from that while even the big LLMs are somewhere in the millions of "equivalent" at best. So, bad topology + much less "neurons" and the resulting capabilities shouldn't be too surprising. Plus it is clear that AGI is nowhere close, because one result of AGI is a proper understanding of "I". Crows have an understanding of "I", for example.

And that is where these "meta abstraction levels" come in: There are many needed to eventually reach the stage of "I". This can also be used to test how well neural networks perform, how far they can abstract things for real, how many levels of generalization are handled by it. But therein lies a problem: Let 2 persons specify abstraction levels and the results will be all across the board. This is also why ARC-AGI, while dives into that, cannot really solve the problem of testing "AI", let alone "AGI": We as humans are currently unable to properly test intelligence in any meaningful way. All the tests we have are mere glimpses into it and often (complex) multivariable + multi(abstraction)layered tests and dealing with the results, consequently, a total mess, even if we throw big formulas at it.
Not every, but wasting money is one of the tricks of corruption.
llama.cpp can run LLMs on CPU. iGPU can also use system memory, the novel thing is not that, it's that the LLM inference is mostly memory bandwidth bound and memory bandwidth of a custom built PC with really fast DDR5 RAM is around 100GB/s, nVidia consumer GPUs at the top end are around 1TB/s, with mid range GPUs at around half that. M1 Max has 400GB/s, M1 Ultra is 800GB/s, but you can have Apple Silicon Macs with up to 192GB of 800GB/s memory usable by GPU, this means much faster inference than just CPU+system memory due to bandwidth and more affordable than building a multi-GPU system to match the memory amount.
Just post on socials (that you can still access) about being locked out and then hope for the best?
This is exactly why there are some more "enterprise" machines out there that an arbitrary adversary with physical access can not "abruptly restart" from the outside.

It's a shame that popularly used OEMs still allow "abrupt restart" to be so easy.
That came out a few weeks ago from meta. Large Concept Models

https://ai.meta.com/research/publications/large-concept-mode...
I've watched juniors take their output as gospel applying absolutely zero thinking and getting confused when I suggest looking at the reference manual instead

I've had PMs believe it can replace all writing of tickets and thinking about the feature, creating completely incomprehensible descriptions and acceptance criteria

I've had Slack messages and emails from people with zero sincerity and classic LLM style and the bs that entails

I've had them totally confidently reply with absolute nonsense about many technical topics

I'm grouchy and already over LLMs
I think the theory of domain-specific languages is very valuable, even if there's rarely a need for a full implementation of one.

As I see it, a DSL is just the end-state of a programmer creating abstractions and reusable components to ultimately solve the real problem. The nouns and verbs granted by a programming interface constrain how one thinks, so a flexible and intuitive vocabulary and grammar can make the "real program" powerful and easy to maintain. Conversely, a rigid and irregular interface makes the "real program" a brittle maintenance nightmare.
That's not the point. The point is that even after you install Python on Windows using the official installer, and unless you put that early in the %PATH%, running 'python' on a powershell / Windows terminal will pop up the Windows Store. That's just how fucking stupid Windows is these days.
I've thought that we should be able to move so fast with software, and sometimes I see that successfully, but usually it seems like we leverage computers' superior performance poorly. To pick on frontend, it's trivial to create the next shiny Javascript framework, but what of it? Or, how come refactoring can be so painful, when the semantic change might be small? I think the flexibility and performance of computers is such that we programmers are usually incapable of effectively using them. It's like a 3D optimization problem visualization, looking for the highest peak around, except the cursor moves a lot faster than it can peruse the landscape. It zooms around aimlessly, easily getting to arbitrary places but without the capacity to make sense of them. When the train is in motion, switching tracks is hard, even if that would be the best move.
Yeah, this many words, and not even talking about having to trust the compiler, and how hard that is.
Yep. A good chunk of desktop Linux is thanks to paid Red Hat developers contributing to projects like GNOME. Valve pays for a good deal of Linux's graphics-related userspace code. And another chunk of paid work is handled by non-profits like the KDE organization. There aren't as many weekend hackers keeping your Debian workstation and Pop!_OS laptop running as you might expect.
iOS apps absolutely do support deep links and URLs. Custom URL schemes are the older of the two and let iOS launch the app when someone taps on a URL that begins with its protocol (e.g. "bugmunch://orders?id=xxxxxxx"); the newer is Universal Links, which lets you set your web site up to launch your app from the web page if it's installed on your device.
Nothing and the hilarious thing is that the AI figureheads admit that technology (as in defined by new theorems produced and new code written), will do pathetically little to move the needle on human happiness forward.

The guy running Anthropic thinks the future is in biotech, developing the cure to all diseases, eternal youth etc.

Which is technology all right, but it's unclear to me how these chatbots (or other AI systems) are the quickest way to get there.
The pipeline stages (say: fetch, decode, execute, memory access, register write back), are organised "parallel in space" as transistors on chip. The point of having a pipeline is so the stages can execute "parallel in time".

More generally, parallel in space is interesting because it is a necessary precondition for parallel in time.
Apple does not use HBM, they use LPDDR. The way they use it is similar in principle to HBM (on-package, very wide bus) but it's not the same thing.
If you’re in the Apple ecosystem, I highly recommend the Vinegar extension which replaces the awful custom player with a standard HTML5 <video> element
party like its 1999
If effect, kind of.

The money being invested does not go directly to investors.

It goes to the cost of R&D, which in turn increases the value of openai shares, then the early investors can sell those shares to realize those gains.

The difference between that and a ponzi is that the investment creates value which is reflected in the share price.

No value is created in a Ponzi scheme.

The actual dollar worth of the value generated is what people speculate on.
I completely agree with the sentiment of the article but the general user behavior is changing. Young people are always asking for an app. It's what they're used to. My nephew never even opens his web browser on his phone.
And if they “have to be in person” is that because you are doing programming with specialized equipment?

I can’t imagine hiring a bunch of new grads can actually end up being productive. Back in the day you would open an office in a place where you can get relatively cheap experienced devs like the suburbs of Atlanta (where I use to live).