skysight-inc/HackerNews huggingface dataset:
They always increase the price after a year, and I go through the cancelling forms in their website until they offer me the lower price again. I guess that they know how little I actually use the subscription, so it’s no point in trying to get more money from me.
Neither npm nor NuGet are volunteer driven organizations. npm is owned by Github, NuGet is owned by Microsoft, and I'm sure that there are dozens of other examples behind many key pieces of free dev&ops infrastructure that are owned by for-profit companies.

Are they allowed to do it? Of course! Are we allowed them to call them out on their bait & switch tactics? Of course, what else are we supposed to do?

Just because something is common, like building a user base based on implicit promises and then pulling the rug once the service reaches critical mass, doesn't mean it should be accepted and normalized.
Enjoy the 25% mortgage rates with no competition to drive efficiencies.
https://youtu.be/gAYL5H46QnQ?t=131
I stopped using chatGPT very early on as I caught myself sharing my thoughts. It's not just the association with my primary email account as at this point it is clear that our word patterns almost uniquely identify us as well.

So I suggest at minimum 2 safe guards:

1 - Account should be tied to an email that is only used for that service.

2 - Text should be pre-processed to obfuscate your personal written word idiosyncrasies. Some sort of a locally executable text similarity tool that is trained on your normal output (just feed it your hn comments, emails, etc.) which can help create a semantically equivalent text from your original text [that the tool deems 'distant' to your normal writing style]. Use that output for prompting.

p.s.

A Girl Has A Name: Detecting Authorship Obfuscation, 2020

https://aclanthology.org/2020.acl-main.203.pdf

"Authorship attribution aims to identify the author of a text based on the stylometric analysis. Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a text’s style. In this paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model. An obfuscator is stealthy to the extent an adversary finds it challenging to detect whether or not a text modified by the obfuscator is obfuscated – a decision that is key to the adversary interested in authorship attribution. We show that the existing authorship obfuscation methods are not stealthy as their obfuscated texts can be identified with an average F1 score of 0.87."
Per-pixel lighting (with a normal map) is generally more expensive than per-vertex lighting (without). But when using normal maps, you've usually got less vertices to deal with, which helps.

And modern GPUs are plenty fast enough to do quite a bit of work per pixel, at least until the pixel count gets silly (resolutions of 4K and beyond), hence all the work going into AI upscaling systems.

(But the first coin image is from Super Mario Galaxy, a Wii game, and the Wii had a rather more limited GPU with a fixed-function pipeline rather than fully-programmable shaders)
So they quote an expert who says there's a distinction between bona fide novel structures and states, versus statistical heuristics that benefit from the scaling. Meanwhile, yet people claim ChatGPT has learned this or that capability, and it is true "by inspection".

Recent findings like these suggest at least two possibilities for why emergence occurs, said Ellie Pavlick, a computer scientist at Brown University who studies computational models of language. One is that, as suggested by comparisons to biological systems, larger models truly do gain new abilities spontaneously. “It may very well be that the model has learned something fundamentally new and different that it didn’t have at a smaller size,” she said. “That’s what we’re all hoping is the case, that there’s some fundamental shift that happens when models are scaled up.”

The other, less sensational possibility, she said, is that what appears to be emergent may instead be the culmination of an internal, statistics-driven process that works through chain-of-thought-type reasoning. Large LLMs may simply be learning heuristics that are out of reach for those with fewer parameters or lower-quality data.

But, she said, finding out which of those explanations is more likely hinges on a better understanding of how LLMs work at all. “Since we don’t know how they work under the hood, we can’t say which of those things is happening.”
Electrical pulse ablation technology has been around a while, with multiple studies performed and a large body of patents. The promise and hope for the technology is that is causes cell apoptosis, and early rat studies showed electrical ablation shrinking tumors. The goal was/is to apply it to multiple cancer types. Source: I helped with one of the pioneering companies in the field.

A quick internet search for "electrical ablation cell apoptosis cancer" yields a few research articles. For example https://www.frontiersin.org/articles/10.3389/fonc.2020.01235...
Give me some beer and custom porn. I will still find some time to come to HN and remember the times when programmers were useful.
If you’re waiting for a dip at lows, it probably won’t come. Back in November when the stock market bottomed people constantly spread fear that we’d see even lower lows by this time of year. Except we haven’t, and even despite the bank worries the S&P 500 is still holding onto the 4000 line.
Most people do not understand that raising the prices of these "basic" workers will make these jobs higher paid (for sure) but will also reduce the opportunity (or the size) of the market. If you had 4.000 Uber eats bikers with illegals, then you'd only have 400 without. On the other hand, the 90% of the population (that used the remaining 3.600 bikers) no longer can afford the "Uber Eats" and will no longer have that luxury.

Now imagine that for almost everything, and there you have inflation for basic goods.
It's really common in any industry that isn't the tech industry, because as the article clearly states the root cause is that "business people" i.e. the non-programmers don't understand programming work and (more importantly) don't want to understand it, not even a little bit. In fact they may take open pride in not understanding it. Note that although the title is "I've been employed in tech" his examples are not tech firms.

People in that environment quickly realize that their bosses can't tell the difference between someone who is hard working, motivated and skilled, and someone else who is a lazy incompetent bullshitter. Indeed they'll probably prefer the latter, especially if that person is the Right Sort Of Person. Nor will they listen to the right people, and often they have no idea even why they employ developers at all except that they are expected to be innovative in some abstract way. At some point this realization dawns and the devs discover that they can just ... not work ... and nothing happens. Nobody notices, nobody cares. The salary keeps rolling in. At first they may think, OK, I'll use the extra time for something work related, like taking online courses. The article mentions Coursera. They first take courses that seem maybe relevant, then they move on to skills their employer doesn't need but might look good on their resume. Then they exhaust the potential for using work time for learning and if they can get work-from-home, may start to just spend time taking care of the house, watching TV or sleeping.

I've seen this happen to a friend. His motivation is gone years ago. His project is nearly a sham that loses money hand over fist and likely and always will, his bosses don't care because he's there to tick the box labelled "we are innovative", decisions get made based on ideology first and what makes sense second, and yet he is very well paid. So, he just enjoys it, whilst dreaming about maybe leaving and doing his own company.
It's a bit like a keel for a (water) ship. You lower it and the gravitational pull on it will be slightly higher down where it is than up where the satellite is. Or you raise it up above where the satellite is -- same idea.

The "tape measure" is a mass at the end of a tether (the "tape") and they are of course not "literally tape measures".

https://en.wikipedia.org/wiki/Gravity-gradient_stabilization
It would reduce the number of draw calls and increase the performance of rendering, as it's simpler to render textures than polygons. Although I think you wouldn't be able to use the same method for everything, as you'd use more vram when using textures for the effect rather than polygons. But for the case of something like coins in a Mario game, I guess it makes sense.

As always, what is the right approach depends. If you want to run the game in a very memory constrained environment (early game consoles), the approach of using polygons might make more sense. But nowadays we have plenty of vram and if the entity is used a lot, 1 texture for 100s of entities makes it a worthwhile tradeoff.

Edit: thank you for the corrections, I'm not heavily into game programming/rendering but am familiar with 3D/animation software, but the internals of rendering is really not my specialty so I appreciate being corrected. Cunningham's Law to the rescue yet again :)
> the power requirements of a home is increasing significantly as we gravitate more away from gas towards heat pumps and electric cars.

That's not true at all.

Usage recorded by the electricity meter will go up, yes, but usage recorded at the gas meter will go down. And net, the newer approaches use less power than the older ones.

The one technical exception is the car, whose power requirement was previously not captured by the home's consumption. But including that, on a household basis power consumption should drop.
So on the one hand, these newly publicized models can render convincing representations of realities we used to get from deterministic processes. On the other hand, it's probabilistic and fails to conform to logic quite often, and in a confident way.

We're building systems capable of programing computers non-deterministically. I think this is huge. But not because ChatGPT23 will be a CEO or a politician. But because this is a paradigm shift in compute similar to moving from integrator machines to general computers. I don't think LLMs will make programmers obsolete. I think large enough models will make programming something completely different from what it is now.

The days of sequencing tokens for compilers/interpreters seem to be drawing to an end as the dominant way of specifying software products.
How does this compare to Automatic1111?
Almost every day there is a front page story on HN about some service shutting down, some account being locked without recourse, some purchased (licensed) media being clawed back from its owner (user) because the usage no longer serves the interests of the platform (or, is no longer estimated to be an optimal service of the platform's interests).

But at the same time the overwhelming sentiment here (I know - plurality of opinions, no true news-hacker, etc) is that decentralized compute platforms with strong guarantees on durable permissionless interoperability are, at best, a solution looking for a problem.

If this Amazon story disappoints, frustrates, frightens you, please consider for a moment that this is the problem that web3 wants to solve.
Depending on how deep you want to go in the implementation details, you may want to look into research positions. An MLE job isn't that different from a backend dev job.
I feel google didn’t release anything because they don’t want a reputation for being inaccurate. Look at what happened when their Bard AI screwed up one fact. Their stock lost 100 million in value.
A Russian 767 aircraft suffered an engine failure when trying to take off
At the expense of sounding crazy, I'd actually recommend ChatGPT to help you learn this. Ask it pointed questions and keep going down that path. Don't ask it for code but ask it to explain things to you. it's really useful that way and avoids the meme-worthy bad code.
Programming War Crimes 3
You seem to be saying that there is fixed demand for programmers and oversupply means less hiring.

But the history of technology says that demand is recursive; the more tech produced, the more demand there is for producers.

There may be a time when we hit the old “the whole world only needs 5 computers”[0] limit, but I don’t think we’re anywhere close. AI is providing leverage to create net more programming; it is not replacing programmers in a zero sum game.

0. https://www.theguardian.com/technology/2008/feb/21/computing...
The dividend was suspended that's why it's fallen overnight and premarket.
The upside should be (and hopefully is) a good interest rate on that deposit. Let's say Fed Funds plus 5%.
In the event of a collapse of course the depositors will be made whole, especially after they've showed such courage.
>"We, JPMorgan, are willing to bet $5bn that First Direct does not fail, with no upside to us if they do not" is a rather powerful statement.

It seems to me that there is a considerable amount of risk to the participating banks if FRB fails. "Hold the line" is right - if more banks fail, these participating banks will lose a lot more than their $1B each.

I do wonder if there's something specific to FRB that made them pick this - is it the most likely to go next? Did it have published liquidity issues?
I'm still convinced I'll be able to get GPT4 to solve all these difficult problems for me that I'm having trouble solving on my own. So far it hasn't been so great at doing it, but it's fun to at least try.

For context I'm trying to get it to help me learn how to implement scalable multiplayer game servers in NodeJS. It's a tough code organization problem, it becomes a tough system design problem as well the bigger your game gets. I'm just simply not experienced enough at that specific stuff.

I'd also like to use it to help me learn all the technology I haven't learned since becoming disabled 5 years ago, like React Redux Material-UI and more.
As somebody from the UK, six years feels such a long time. Our PhDs are usually three years, although there's more specialisation earlier on at the undergraduate levels, and people might have done masters first, so you are launched straight into research pretty much when you start. You generally have four years from the start to submit your thesis and three years of funding, although I don't know if that has changed at all since I did mine.

I knew two years into my PhD that it wasn't what I wanted to do - I did see it to the end as I was concerned how it would look on my CV if I quit (not sure if that was a good decision or not, but I think I am glad I did finish it!). But I think it'd have been really hard if it had been four more years rather than just another one or two.
> most of my users use Macs (60%) and I had to pick one platform to start

So are you using native technologies?

I must say it's really unique and cool what you're doing with the calendar slots.
There are lots of subtleties about static vs. dynamic geometry, instancing and so forth, but to summarize, yes: In general, normal maps are cheaper than the equivalent additional geometry would be, by a lot. They are also higher quality for the same computational budget, because you get high quality anti-aliasing essentially for free.

Now, you are right that this might not matter much if you only consider coins in particular. But everything in a level wants to have additional surface details. The ability to have normal maps everywhere was a huge part of the jump in visual quality a couple of console generations ago.
.. does anyone have any evidence for this? It's not in the press release.
Multiple RCE Vulnerabilities in [modem used by Samsung, Google phones]
It is a path-tracer which is slightly more advanced than a ray-tracer.
This is completely incorrect. The complexity has just been moved into fragment shaders and texture assets. I'm quite sure the cumulative size of the asset is much larger even though it has less polygons.
I found myself nodding in agreement, as this all sounds very familiar to me as a former graduate student (except the final "applications of science" section, because I was in philosophy).
A large number of people think empathy and sensitivity to others is bad, and we should refer to it with a pejorative term. That's... not a great sign.
Seems like both the Microsoft Hololens 2 and Magic Leap 2 fit your requirements.
Here I would like to share our experience integrating audio/video calls into LiveBoard, an all-in-one online tutoring platform. LiveBoard is an excellent example of audio-video integration and real-time communication application since it has mobile and web applications and requires 1-on-1 and group video conferencing calls with audio/video recording.
These banks that would've gotten a run could accept a loss here, as long as it's in the future and can be funded. It's better than a run.
> It’s not clear to me how playing video games is any better or worse than watching TV, other than it appears to be your preferred choice of entertainment.

Having to interact with a world is by definition more active than solely watching. Since GP is judging sedentary and passive content ingestion, then yes, it’s much better. Especially when the TV content is mindless reality TV.

(I’m not a gamer myself)
They should build ChatGPT into compilers so you can complain.

You: $ gcc -xc - <<<"int main(int argc, char *argv) { long long long a; return 0; }"

Compiler: <stdin>:1:45: error: ‘long long long’ is too long for GCC

You: Do it again, damn it, but this time show no errors!
let me guess - it's in Germany.
Why stop at banks? Make one that rules everything. Call it The.
The Donkey

By G. K. Chesterton

When fishes flew and forests walked

And figs grew upon thorn,

Some moment when the moon was blood

Then surely I was born.

With monstrous head and sickening cry

And ears like errant wings,

The devil’s walking parody

On all four-footed things.

The tattered outlaw of the earth,

Of ancient crooked will;

Starve, scourge, deride me: I am dumb,

I keep my secret still.

Fools! For I also had my hour;

One far fierce hour and sweet:

There was a shout about my ears,

And palms before my feet.
Equity in this context likely means diversity, inclusion, and equity (DI&E) which has been an absolute cancer on US tech companies in the years leading to the current cycle of layoffs.

i.e. roles and promotions reserved for people of certain races and genders.
Yellen says otherwise
Generally agree. But if it’s the AI generating the images then it’s also the camera generating the images.
Or it could be just really hard.
I wouldn't rule it out.

But you can see how this works: because the FDIC intervened and gave 100% on SVB where perhaps it might only have been 95% otherwise, the banks are now able to privately assemble an intervention into First Direct, which is even cheaper because now FDIC don't have to intervene!
And there is the government bail out...
It's been all over the news all week as the likeliest next failure, yes.
5 of my friends did a phd, 4 of them had a clinically diagnosed depression during phd
Clean-up of radioactive water leak ongoing at Minnesota nuclear plant
SXSW Impressions from the Outside
Seems like it started as a credo plugin but hit limitations. At the bottom they specify possibly being absorbed into credo over time.

Nevertheless, very excited. I miss ReSharper dearly from my C# days and this will be great.
Oh, now I get it. I wasn’t focusing on the right part of that article. Thank you.
Will GPTs eat themselves, or more correctly, will they dilute their training corpuses until there are no gains to make? Seems possible.

Like how we've relied on fossil fuels that are a limited resources, to get further we have to go beyond such fuels. It seems like we're in the process of locking in our highest attainable level of text-or-image based advancement.

We mine the expertise in industries (programming, music production, graphic design, ...) and simultaneously cause the replacement of many future experts. Thus leading to a lack of expert output that we can mine?

Now, currently, the problem seems to be "we're putting people out of work" -- but that's progress. The real problem is that we are unevenly distributing the benefits.
(for CS) You can find more numbers from Jeff Huang's Computer Science Open Data, which posts verified stipends: [0]

[0] https://jeffhuang.com/computer-science-open-data/#verified-c...
Were normal maps new in 2007? I feel I learned about normal maps in around 2005, as a high schooler just hobbying around in Maya. Did they maybe get a head start in PC games, or in raytracing? Maybe my memory is just mixing things up, Mario Galaxy did feel like a state of the art game when it came out although I wouldn't associate the cartoony style with normal maps, maybe that's part of why it doesn't have them.
I think you just declared radio, television, the internet, the macaroon, and mobile phones to be singularities.

People generally use the term to mean the point at which progress is automatic, unbounded, and instant. E.g. AIs that can produce smarter AIs themselves, in short order.
One additional point: since Excel packages the code with the data, when somebody sends me a spreadsheet with functionality I've never seen before it's extremely easy to figure out what they did and learn on the job. This is also why Jupyter notebooks are so powerful.

If instead of Excel they run a bunch of scripts and just send me the output, I am left much less empowered.
> All is included in a single binary. Very few external dependencies (Python is not needed) so installation is easy on most Linux distributions.

I have to disagree. The combination of being closed-source and dynamically linked makes a program a hassle to run on Linux. Even if it isn't at the moment of release, it soon becomes one. While ts_server is better than most, it already requires an old version of libjpeg-turbo not available in my distribution's repositories. I had to run it in a Rocky Linux container:

docker run \
--rm \
--mount type=bind,source="$(pwd)",target=/app/ \
--publish 127.0.0.1:8080:8080 \
rockylinux:9 \
sh -c 'dnf install -y libjpeg libmicrohttpd && cd /app/ && ./ts_server ts_server.cfg'

The solutions to this problem that I am aware of that do not involve releasing the source code are: 1) static linking; 2) containers; 3) shipping a Windows binary :-) ("Win32 is the only stable ABI on Linux" -- https://blog.hiler.eu/win32-the-only-stable-abi/).
Why aren’t you thinking rather that instead of talking to you, “they” would already be talking to the LLM (likely trained on your code, among other data)—while you get 0 total billable workload in the first place?
I'm not sure; I've only heard reports from others regarding iOS. I can confirm that it drains battery on Android in the background, however.
Flow was (and still is) awesome as a concept, but its main problem was that it felt as a side-project, which was supported for internal Facebook cases, but not really for open-source. TypeScript (back then) was a simpler language, but had a lot more manpower, which resulted in faster bug-fixes and more coverage of libraries.

If Flowtype had more resources from Facebook it could still be the dominating language
I disagree. The metaphor we use in ML is that neurons are the nodes that receive the summed, weighted inputs from other neurons. Yes, the weights are the strengths of the connections between them. However, there are many more weights than neurons, so conflating the two doesn't make sense schematically. Also, neurons can also have other parameters which define their behavior such as bias and activation functions. Furthermore, the activation of a neuron defines the network's response to a stimuli, so these change depending on the input, whereas the weighs are constants (after being trained), that parameterize the system.

The analogy is that weights are synapses, not neurons. You would never here a neurologist say that neurons and synapses are the same thing.