skysight-inc/HackerNews huggingface dataset:
No, a bayonet as back up weapon hasn't been effective in a long, long time. We've seen it as a desperation weapon, during Stalingrad and other battles where both sides logistics were stretched beyond - far, far beyond - their breaking point. Fuel, shells, tanks, bullets, then shovels, axes, knives and teeth . . all were weapons of war in the great inferno of the early 20th century . . but no one, ever, would substitute a bayonet for a spare mag on an autofire weapon. Even basement clearing.

I can see, in some circumstances, a case for knives as close-in weapons during reconnaissance or other stealth missions, but even in those circumstances, an army with functional supply systems will pack a DeLisle Carbine, a "hush puppy", a Welrod pistol, or one of the many often homebrewed stealth weapons enterprising recon squads often baked up.
3.5 can't do this. It's a feature of paid ChatGPT running GPT-4.
If a goat was revealed randomly instead of intentionally, there is no advantage to switching. See the "Monty Fall Problem" in this paper already linked elsewhere in the discussion: https://web.archive.org/web/20230706235720/https://probabili...

To use the 100 door extension some people find helpful:
If Monty always reveals the 98 doors that don't contain goats, then 99% of the time (every time you picked a non-car door), the other door will have the car. If Monty is opening a random set of 98 doors, then 98% of the time Monty will reveal the car, 1% of the time you'll have picked the car on the first guess, and 1% of the time the car is in the other door. When you're in those 2% of cases where no car is revealed, you have a 50/50 shot of being in either of the 1% states where that happens.
Exactly.

The article of course is focused on nVidia which is making money (unlike the dot com businesses) but the 'bubbleness' will happen if AI startups that aren't making money go public anyway. If there isn't a way to get retail investors involved then just the rich people will get fleeced.

That said, it was interesting to watch the nVidia recovery from the bitcoin miner demand dropoff. One hopes they would react quickly and effectively when the bubble bursts and demand for tensor chips drops by 16 to 20dB.
But who would substitute a no risk asset for a high risk one? This is a specious comparison.
If the government funded a custom edition of Time, I would sure expect it to be public domain.
Makes use of https://rapids.ai/
Why would you assume that? The normal interpretation of “another” is that that there were others before it.
I think its a bubble but the numbers companies like nvidia are putting up right now are no lie, and from tech companies perspectives its better to invest in a potential trend than to sit on the sidelines, which is why everyone continues to buy GPUs and launch their own AI enabled products in hopes of catching this wave.

However, ML/AI has always been a cornerstone of most big tech companies, but it was usually in understated products or used in ways that might not be directly b2c or b2b like ChatGPT. For example, meta/google/netflix ad/video recommendation systems are likely mostly ML based, and have been for a few years. Most of the photography on your phone is using some form of CV/ML, Amazon probably uses ML for logistics planning, AV companies have been using ML models in perception and behvaior for years, generative models have existed for a while, etc.

ChatGPT just brought the idea of AI and ML to the forefront of investors and the general public as a "tangible" and fairly practical use of "AI." So I don't think AI is necessarily a "bubble" since theres potential for pretty high utility products, but I do think the market might have an overly optimistic view on the success of AI (specifically generative AI) monetization, which only a few companies have arguably successfuly done (mostly the AI "suppliers" like nvidia, OpenAI, etc). Its yet to be seen how Chatbots/generative models will actually impact the bottom line or streamline processes at businesses without necessarily "solving" AGI.

ML/AI existed before this "bubble" and will continue to exist after. In my opinion it was only a matter of time before we got here, though I'm not sure if this time if its over or under priced. I disagree with the claim that AI itself is a bubble, since it has actual utility.
And here is a clip of the relevant scene, from the episode "Much Apu About Nothing" (1996), https://www.youtube.com/watch?v=xSVqLHghLpw

Quoting from its IMDB entry at https://www.imdb.com/title/tt0774432/characters/nm0810379 :

Lisa : Dad, what if I were to tell you that this rock keeps away tigers.

Homer : Uh-huh, and how does it work?

Lisa : It doesn't work. It's just a stupid rock.

Homer : I see.

Lisa : But you don't see any tigers around, do you?

Homer : Lisa, I'd like to buy your rock.
The main hurdle is lack of multilingual support in open weights models.
Front page? Or buried where only the junkies will see it?
There's a lot of shitty products with an AI label stapled on, either based on using OpenAI API without a clear articulation of value of feasibility, or by training hopelessly limp models with all the wrong assumptions, bad or insufficient data and no real validation.
But that's how the game is played and everyone knows that. If only a few of those companies succeed beyond any dreams, that will be enough.
>There's no "pass-by-reference", never_has_been.jpg. With pass-by-reference what you're actually passing is a value, of a pointer. So you're "duplicating" a value: of the pointer.

thats in c , i think c++ has references
These are uncharted territories for sure. I’m not sure if it’s a bubble though. The valuations, even though gobsmacking and ridiculous, are grounded in some reality.

For example, Nvidia’s PE ratio has actually improved. The stock is technically cheaper now than it was before. Assuming of course that it is able to keep performing at the same level. Something I am hugely skeptical about that.

I have a working theory (unfinished and likely has faults):
The automation of decision making in trading is the reason behind this. Which is also why I feel a repeat of 2000 will be unlikely as fewer humans are involved directly in the loop. Things are going to seem more plausible now, as the algorithms ensure that by looking at hundreds of signals before making buy/sell decisions. Our guts may be picking up on something uncanny that has not been accounted for in the algorithms yet. Only time will tell what that is.
Would be great if people could share their app demo, host & model used/trained for better context.
because it's a tale of intrigue!
Well, some people do.

Paying with coins for EU hosting and US goods while in Russia is quite convenient these days.
> want to copy out its contents as a byte string.

That object should take care of it. Even if the parent object is reclaimed, the byte string should independently persist for as long as is necessary. Since byte strings don't contain pointers to anything, reference counting could be used.

The refcount could be in the parent blob, such that when its nonzero, the blob is pinned against reclamation.

Of course you can't just share out the internals of an object, such that GC doesn't know about them. It doesn't matter if it's a foreign object set up with FFI or something built into the run time.
Ennis house
Not only that. The most important aspect is that you can have explicit constraints in the outputs, so that your system does not go outside the operating envelope it is designed for. For example you can state that I want you to stabilize my system given that my motor can only apply up to this force. And by the way do not exceed system velocity of x km/h.

We can also have objective to explicitly define how much more important to us accurate tracking vs stabilization speed.
Author and former Navy SEAL David Goggins sues Amazon over bootleg books
Already done decades ago. It is called perl
I once worked on a legal case management database. One of the clients was getting sued over breast implants, and they used the database to catch scammers who sued fifty times (once in each state) over implants they never actually had.
Might be interested in https://www.youtube.com/watch?v=wgUiZ7E19eM
https://archive.ph/GQRsG
I disagree, there is so much to build on from here.

I also feel the same way about Apple Vision Pro.
The pin and unpin could be tied to a reference count in the byte string object that was extracted. When blob's get_data is called to get the byte string, its pin count is bumped up. When the byte string is reclaimed by GC, it bumps down the blob's pin count.
it's all pretty well put together nowadays honestly.

here's a dead simple way : (1) download LM Studio, install it[0] (2) download a model from within the client when prompted (3) have a ball.

the program is fairly intuitive, it takes care of finding the relevant files, and it can even accept addendum prompts and various ways to flavor or specialize answers.

Learn the basics there, take what you learn to a more 'industrial' playground later on.

[0]: https://lmstudio.ai/
> Which is why multivitamins seems to be a bit of a scam and studies can't find any benefit from them.

As someone who has had scurvy before, I can guarantee you some people can find a benefit from them. Of course if you expect them to make you "healthier" (as if this is a one-dimensional property, which is problematic to begin with) regardless of your health to begin with you may find yourself unhappy with the results.
Our diligence paid off too, those satellites are still up there unbothered.
Survivorship bias is huge in all "but I made 50x" because in order to do this, you have to have not selected the 100 other AI projects which tanked, along the way. And by the time there is 1 clear leader, you can't stonks on it because the price rose for the ones who got in before you.

Looking backwards saying "but this guy I know did 100x" is not informing of how people can know now, that Amazon and not Alexa by Digital is the one to pick. (note: they are just labels, alexa was not an amazon competitor. the point is digital was acquired, and died. Amazon didn't die. At the time digital was big, you would think it was a sure fire bet. It wasn't)
Pop doesn’t seem to have been updated in years, what’s going on?
We were serialising/deserialising a large c++ class that consumes about 100 megabytes of memory.

The reason we chose protobuf over capnproto was not performance but because it was already being used in other projects.

However I implemented both and didn't really do a deep comparison of performance. Basically protobuf serialisation/deserialisation was a few seconds fast. However, I didn't use capnproto as intended, in that I deserialised from the capnproto objects into an actual object which involved copying; I don't really see using the native format directly all that useful in most applications, but maybe I'm missing the point.
It’s common for new colleges to offer free tuition.

In order to be accredited, you need to show you can produce a decent class of students. But how do you get the students if you’re not accredited?

Free tuition.

The accreditation can be retroactive, so they might be getting an accredited degree for free.

I don’t know if this was the case at UC Irvine Law School.
Another option is to use the p5.js library. They also have a nice online editor, at https://editor.p5js.org/, which makes it easy for students to get up and running quickly.
The dotcom era bubble specifically refers to Webvan-era companies, which were almost universally over-funded and over-valued based on outrageous speculation about the potential of the internet.

Instacart was founded about a decade later, and its margins are pretty slim by tech company standards (net income ~$100m on $2.5bn revenue) so I think it's pretty clear that in the year 2000, there's no possible way that Webvan could have succeeded even if it had done everything right.

Extrapolate this logic to AI, and the parallel is clear: The current generation of AI over-promises and under-delivers at enormous cost, and yet billions are invested under the premise that quality will go up and costs will go down enough in the next few years to build viable businesses.

If those investors are correct and we're just a few years away from useful, cost-effective AI, they'll make their money back. If they're wrong and we're actually a decade away, not a single AI product that hits the market within the next few years will survive.
> goad the US into a regional conflict?

and by doing so, spread the resources that would've been devoted to ukraine thinner. And by proxy, take attention away from china's regional ambitions.

It's clear that these events aren't happening in isolation.
I've tried code-llama with Ollama, along with Continue.dev and found it to be pretty good. The only downside is that I couldn't "productively" run the 70B version, even on my MBP with M3 Max with 36GB of RAM (which interestingly should be enough to hold quantized model weights). It was simply painfully slow. 34B one works good enough for most of my use-cases, so I am happy.
> I wonder what the talented people at OpenAI or Mistral tell themselves.

“We are doing something amazing, we need no permission”. Also, “we are good people and we have to guard this from bad actors”.

IOW, they are fooling themselves (and following the money and military contracts).
Good comment, I agree. However, I think that if it turns out LLMs don't pay for themselves or aren't a great investment for MSFT and Google and whatnot, it's not hard to pivot some of that tech to less exciting b2b type ML ventures.

Maybe everyone doesn't need an LLM assistant, but every business would love it if AI can optimize their sales pipeline 5%, reduce their manufacturing defects 3%, increase their utilization 10%, etc.
Some people also cook because it’s cheaper, or healthier.
Are there any sample documents for these types of attacks? I'd love to test my own service.
The Dotcom era was a bubble because a ton of tech companies had business models that were terrible or sometimes just ahead of their time; but investors were eager to get in on these newfangled internet companies that were all going to the moon. It wasn't economical for pets.com to ship 50 lbs bags of dog food in 1999. Amazon made it because back then they just sold books (and maybe cds?) which have always been fairly affordable to ship.

https://en.wikipedia.org/wiki/Dot-com_bubble
This issue of Implementing demonstrates step by step how to build a search engine akin to those utilized in ecommerce platforms like Amazon! Share your thoughts!
Lexical (https://lexical.dev/) is really nice to use and doesn't use Prosemirror or CKEditor.
A bubble for investors just means they're anticipating more returns than will be possible to recoup.

But that's only matters to investors.

Something could break one or two major assumptions undergirding the current form of capitalism, but if it caused the returns to evaporate, that would still look like a bubble to an investor. But capitalism would have changed, and it would still have been hugely impactful for regular people
In 1976, the UK had to be bailed out by the IMF.
> Almost any approach will work if perfectly executed by a team with a perfectly matching skill sets

Will it though? The assumption that any project no matter what it is will always succeed if only the team is right seems naive and at the core of many unreasonable critiques of any framework for project execution.

Failure is a part of the reality of projects, and when executed correctly agile will let you identify and pivot away from failure early. While more ridged frameworks will keep trying to expand resources, extend deadlines until they explored under the load of living up to ever increasing promises they made to justify the growth.
> the ability to use StringLike conditions

so much depends

upon

a regular

expression
my problem with WSL: you need to install the same version of your environment twice: on windows and on WSL. For example: if you have python installed on windows, then you can't run the executable of the python interpreter in a bash shell running under WSL.
The New Building Blocks of Observability
https://www.datacamp.com/tutorial/fine-tuning-llama-2
Ask HN: Does a stunt in academia lower my industrial profile?
I currently hold a prestigious title in a big tech company (not top tier, but still very big). I earn A LOT of money, to the point that it feels a bit indecent compared to what other people contribute to society.

Long story short, the company is going sideways due to poor leadership and I got an opportunity in a local university (second tier, too) to be a tenured professor and responsible of a research unit (a few professors). This is much less paid (by a large factor), but would allow me to perform interesting research on energy, sustainability, and work with smaller companies.

My main worry is that I want to keep a good industrial profile to go back to roles similar to mine. I plan on collaborating closely as advisor or co-CTO with startups in parallel with my tenure at university.

Do you see this "stunt" as a big liability/risk? Do you have any other advice for me making this move?
it's irrelevant whether they're "cryptographically" random, all that matters is that account IDs are not controlled by the user and therefore have no logical relation to any access-control policies the user may wish to implement
Reminds me of Dell Inspirons with dedicated Home Media Centre buttons. I had one which even had a tiny remote.

I think the “every PC” is the obvious headline grabbing statement from the VP. At the business end, Dell has made machines for every type including the Linux only Ubuntu pre-installed XPS line, which were highly recommended when they came out. This is just them covering the AI consumer base.
"Dell promises every PC will have graphics/FPU/USB whether you like it or not"

It's not unreasonable to expect AI hardware processing units to be standard in future CPUs or chipsets.
Google steps up Microsoft criticism, warns of rival's monopoly in cloud
If you don't care about the details of how those model servers work, then something that abstracts out the whole process like LM Studio or Ollama is all you need.

However, if you want to get into the weeds of how this actually works, I recommend you look up model quantization and some libraries like ggml[1] that actually do that for you.

[1] https://github.com/ggerganov/ggml
Someone who wants put their money to work.

Lots of cash doing nothing on the sidelines is risky in the long term because inflation. A "risky asset" is risky in the short term because of volatility.
For transparency monitoring there's also https://crt.sh/?q=news.ycombinator.com which doesn't need a login, is free and has RSS support.
deepseek-coder 6.7b is seriously impressive for how quickly it runs on an M1 Max. There’s a few spots where it still doesn’t fare quite as well as ChatGPT but it’s a small tradeoff considering that it’s fully local and doesn’t even spin up my laptop’s fans.
Texas earthquakes starting to rival California
Those abstract numbers don't help us evaluate the value of a company.

Let's look at the addressable market:

The sum of wages in the USA alone is about $100T per year. How much is it worldwide? Let's say $400T.

That is the current TAM when we expect computers to replace humans like cars replaced horses.

Capturing only 1% of that TAM would mean $4T in yearly revenues.

Nvidia's $2T market cap seems tame in face of the size of the opportunity.

But there is another point: A new technology not just replaces an old technology. It 10xes the usage. There are way more cars on the road now than there were horses back then.

The size of the market will grow beyond those $400T. We will build way more stuff when robots carry out all the work. Most of planet earth is unused at the moment. That will change.
From extended family and/or family friends in the South Block - in the 90s, after the Cold War ended, the IAF began using DEC ALPHA machines and was in talks w/ the Clinton admin about importing AEW&C systems from Grumman to replace the juggard Hawker Sidleys.

After the Pokhran-2 tests, the radar+AEW&C sales ended and DEC/Compaq cancelled federal Indian deals at the time, and India based Sales reps for American firms switched to IAI, Elbit, and Rafael which ended up winning IAF deals from then on (some corruption played a role as well in this at the beginning)

A lot of the wing commanders from that era work as lobbyists for Israeli firms now and most of the cream of the IAF, DRDO, and SCL in the 1980s-90s work as mid-upper level Engineering or Product Management in the US now after leaving in the 90s-2000s like my parent's generation.
It simply doesn't have the ability to visit a specific URL or answer questions about it. For that reason + a couple more niceties I use https://olly.bot
Julia solves this pretty well by having a gc_preserve macro that tells the compiler that an object has hidden references within the scope.
This is the essence of Rusts lifetime analysis; a pointer to an object can't be live for longer than the object itself is.

In this particular example, you'd make an object with a finalizer and hide the raw pointer inside of it. Then you can only touch that pointer by going through a Rust object which participates in lifetime analysis, and it'll clean it up when it's done. Any more attempts to touch that object/pointer will fail to compile.

Expressed that way it makes sense that some people call it "GC at compile time".
Building a Search Engine with Elasticsearch and Node.js
Open Directory of broadband performance by location https://broadband.money/broadband-audits
Another one to one side, is a hollerith card sorting machine in the original Michael Caine film of "The Ipcress File" -Len Deighton used to refer to tech in the books, and wound up being gifted one of the first word processing systems by a contact somewhere like IBM, to write on: industry noticed he liked the tech.

Nicholas Freeling did the same with CERN/DESY type places and the CAMAC crate in his book "Gadget" -he asked people what you call the kind(s) of tech they used, and they gave him something to use. Could have been "Unibus" and "pdp11"
Needs [video] (ca. 3 min).

Spoiler: The answer seems to be intense exercise, which seems to cause less fat to be stored directly around the organs and more under the skin, which leads to more normal cholesterol levels.
Bitcoin is at a two-year high.
This is a very nice resource: https://github.com/mlabonne/llm-course
The numbers Nvidia are putting up may not be a lie but they are certainly achieved in a very odd and incestuous way such as using GPUs as collateral for loans. Worst still is companies pretending these cards won’t depreciate rapidly. The closest analog is a previous hamster wheel Nvidia was involved in with crypto mining before Ethereum switched to proof of stake and gutted the crypto video card mining industry.

https://longportapp.com/en/news/100635792

Let’s let Google’s new AI results tell us the signs of a bubble and I see all of these with the current AI grift.

Rapid price increases

High valuations

Speculative behavior

Increased media attention

Lack of fundamentals

Prices rise regardless of news

Other asset prices are soaring, too

New traders say that old investors "don't get it"

Stock valuations in the top percentiles

The stock market was on fumes before the current AI hype hit and we all crowded into 7 stocks. Wall Street needs this to keep the cycle going, but I don’t think it’s enough.
Managing FFI will always require cooperation with the GC if there is one. If the GC doesn't expose adequate APIs for doing that cooperation, that feels like more of a design problem with that GC than a fact of nature. You shouldn't be trying to "trick" the compiler/runtime to keep your thing live until you've finished using it: you should tell it when you need to keep it live until, and it should listen to you.
Wendy's planning 'surge prices' based on fluctuating demand
It's all money laundering. The creators are probably Russian oligarchs and mobsters. There is no Satoshi, just Ivan, Vladimir, and so on.