skysight-inc/HackerNews huggingface dataset:
> If we could count on companies to act at all for ends other than short term profits

We can for many companies, because many companies are small businesses that are run by their actual owners, who have an incentive to think long term.

The companies for which we can't are companies who are run by non-owners and whose owners--the shareholders--have little or no effective control. Such companies generally would not exist in an actual free market, because they are so inefficient. They exist in our current society because of the huge costs of compliance with government regulations, which protects very large companies from competition, particularly if they can get into the business of being government contractors, as most of them do.

> We know what companies will do without laws around worker rights and safety protections. We know what they do without food safety rules.

We do know these things, but what we know is not what you are implying. The narrative that big bad companies in a free market do all these bad things is a myth. The companies that did them were only able to do them because they were propped up by governments and insulated from competition. For example, the coal companies that exploited miners had exclusive mining rights granted by governments. The railroad companies that exploited workers had exclusive rights to key railroad routes granted by governments.

> We know what they do without any anti-monopoly laws

Yes, we know that too: they provide products and services of higher quality, and a lower price, than their competitors. Then their competitors complain to the government about anti-trust violations and the government breaks up the companies, resulting in higher prices and lower quality products and services.

In cases where there actually is an argument for the "monopoly" company serving customers badly--for example, Microsoft--the government does not break the company up for anti-trust violations. Instead it does some show lawsuits that have no real impact.
This is really neat. It reminds me of my use of Notational Velocity from (holy cow!) almost 20 years ago. https://notational.net/
Reporting AI: Build reports with natural language
I don't get why every tech company wants to IPO especially profitable ones. I'd much rather stay private with my money factory, and if I need some loans to expand, then so be it. Microsoft did it for Azure, look how well that worked out. I feel like a lot of companies do worse after going on the Stock Market. The stock market is where long term companies go to be screwed on a whim.
Well today's an excellent day to learn something new :D

This wiki page has more info on the difference between percentages and percentage points: https://en.wikipedia.org/wiki/Percentage_point
I think they will eventually find that having a huge number of tiny teams will result in silos, replicated work, and massive inefficiency as they try to scale up. This averages out as 15 3 man teams. Yikes.
My favorite size of engineering team is 1
Join a bank or whatever a dinosaur company.
Gen Z's Most Trusted Source for News
Yup, Ctrl twice.
I was making an analogy - I am an "NPC" in the real world and I am somewhat concerned about people abusing very powerful LLMs to cause me harm. As another example, I was very distraught with what the internet did to Microsoft's Tay, and while I'm not 100% happy with OpenAI's approach to safety, I feel much safer with it than without it.
Yeah. Increases in team size typically mean an increase in specialization and a decrease in productivity because of the larger amount of communication that must be done to keep everyone moving in the same direction.

I was responsible for a lot more at a small company, and I only had to convince one other person if I wanted to do something within my corner. At a large company, I may have to convince representatives from teams representing > 100 people.

That takes time.

But as an organization, we can solve problems that weren't possible to solve at the small company due to lack of resources and lack of people who could sit heads down and work on a problem for 6 months due to their other responsibilities.
Apple Silicon M1 will be supported for many years by Asahi Linux, since it was the original platform for reverse engineering.
No chance. Linux on the desktop will never happen because of fundamental structural issues (lack of funding, no motivation for open source devs to make it good enough for normal people, many naysayers in the community not wanting it to work well for normal people).

RISC-V doesn't have any structural issues that would mean it can't succeed. In fact it already is very successful, but it's mostly in embedded and invisible CPUs at the moment which is why it might not seem like it.

It definitely needs some time to mature for application class processors but I can't see any reason why it won't.
It's impossible to solve with LLMs. You keep adding RAGs until you're back at a non-LLM implementation. LLMs are probabilistic. Most programs, including SQL, need to be deterministic to provide value.
That's the made up definition of affirmative action, not the actual one.
Where "acceptable bounds" equals whatever the current proportion is.
For 649 this machine will run circles around anything in the same price bracket.
15 teams means 15 first-line managers, 5 second-line managers, 2 directors, and 1 VP (I am assuming the manager teams have the same rule of 3/team). In total 23 managers for 47 people.
Seems useful, unfortunately seems to lack many features for my country
It’s more a matter of interpretation than correctnesss. If we’re not being deliberately obtuse, “absolute” and “relative” would go a long way toward disambiguation here.
Put it into the control system of a machine tool used in production. It will run unchanged for decades, if not longer.

The gear shop I worked in had a 1970 GE computer that used punched mylar tape controlling a 50 HP CNC lathe up to 2019 when they were acquired.
Curious what people use besides Stripe (or Paypal) or if they are planning on moving away from it.
Until 1988, my college (Georgia Tech) used to mandate a required course for the entire student body called Drownproofing[0][1] :)

[0] https://en.wikipedia.org/wiki/Drownproofing

[1] https://www.isye.gatech.edu/news/surviving-drownproofing-101
This is a great deal. I use a 2017 intel MacBook Air as a daily driver for browsing and light coding (takes about 15 minutes to generate a stable diffusion image).

It only has 8GB of ram, but is still chugging along.

I can’t believe these are cheaper than iPhones.
Comparing a "boring" 2020 vintage car to simple software is insane since that boring car is full of complex software, not to mention it's a network of at least 40 separate computers. A 2020 model probably has a bus firewall or gateway as well.
When you IPO, you’re getting wealthy off of Other People’s Money flowing in creating liquidity. When you have to buy out existing cap table folks (either through enterprise cashflow or financing), it becomes harder. An artifact of ZIRP evaporating after entire businesses were built on the VC IPO flip model (and profitability becoming king over growth at any cost).

Very similar to the PE crunch currently in progress for the same reason: interest rates that rose fast and will remain higher for longer.

It will take time for everyone’s expectations and actions to reach the new macro reality, with current participants attempting to "find a way out" that is most favorable for the circumstances.
There is no reason why this feature is % based.
What alternative we have? 0.7% just to trigger a charge every month it makes not sense.

For international card is 0.7%+3.9%=4.6% fee for payment!
It's way too much, but it's pretty funny that David Gerard is the target of an obsessive hit piece.
Here's another example - everytime I see a person I go into "con-man" mode, so all relationships are the same to me. Is this analogy better?
I was suspicious of this, and they should really include more. But I have 30 tabs open on my 2017 intel version and this m1 is ever better.
Outer solar system distances are large. Pluto occasionally orbits within Neptune's orbit, but can be up to about 1.5 times more distant (49 AU vs. 30 AU at aphelion). Which means you'd be braking with 40% of the voyage to complete, worst case.

Your larger problem is that there aren't very many outer-solar-system planets, and their orbits don't align in ways useful to gravitational slingshots (for acceleration or deceleration) or aerobraking very often, as in on the order of many decades or centuries. The Voyager "Grand Tour" missions (launched in 1977, primary encounters from 1979 through 1989, both missions ongoing presently in the Heliopause) relied on one such rare alignment. Those occur at roughly 175 year intervals:

"When is the next Outer Planet lineup (Voyager)"

<https://space.stackexchange.com/questions/5075/when-is-the-n...>

Getting to Pluto is hard. Stopping at or orbiting it is harder. That said, there are at least three proposals for such a mission. One is a fusion-enabled pluto orbiter and lander (the propulsion system envisioned is still theoretical). Another a "hop, skip, and jump" mission utilising Pluto's own highly tenuous atmosphere for aerobraking. A third is called "Persephone" of which I have no further details on propulsion, though Wikipedia links several very vague descriptive articles.

<https://en.wikipedia.org/wiki/Exploration_of_Pluto#Future_mi...>
I found LLMs to be quite useful, so the time saved is worth the effort to double check answers.

Granted it might have to do with how I use LLMs. If you just blindly ask a question you increase the chance of hallucinations.
If you give a lengthy input, and the output is highly dependent on the input than you will get better results. Think email re-writing, summarizing, translation.
Hype is fading, so usage decreases.

But you must admit that it is still useful, and usage will not drop to zero.
That's not going to happen, unfortunately, because a lot of the surveillance powers are explicitly authorized or otherwise not ambiguous, and did not rely on Chevron deference.
Because the employees at these companies don’t share in the “money factory” mentality. Personally I’d rather work somewhere and get paid well and have stability, but unfortunately most businesses just pay “market” which is on par with those who offer options.
My graduate research was in this area. My lab group developed swarm robots for various terrestrial and space exploration tasks. I spent a lot of time probing why our swarm robots developed pathological behavioral breakdowns - running away from construction projects, burying each other, etc... The issue was so fundamental to our machine learning methods that we never found a way to reliably address it—by the time I left, anyway. No matter how we reconfigured the neural networks, trained, punished, deprived, or implemented forced forgetting or fine-tuned, nothing seemed to eliminate the catastrophic behavioral edge cases—nothing except for dramatically simplifying the neural networks.

Once I started seeing these behaviors in our robots, their appearance became much more pronounced every time I dug deeply into proposed ML systems: autonomous vehicles, robotic assistants, chatbots, and LLMs.

As I've had time to reflect on our challenges, I think that neural networks very quickly tend to overfit, and deep neural networks are incomparably overfitted. That condition makes them sensitive to hidden attractors that cause the system to break down when it is near these areas - catastrophically.

How do we define "near"? That would have to be determined using some topological method. But these systems are so complicated that we can't analyze their networks' topology or even brute-force probe their activations. Further, the larger, deeper, and more highly connected the network, the more challenging these hidden attractors are to find.

I was bothered by this topic a decade ago, and nothing I have seen today has alleviated my concern. We are building larger, deeper, and more connected networks on the premise that we'll eventually get to a state so unimaginably overfitted that it becomes stable again. I am unnerved by this idea and by the amount of money flowing in that direction with reckless abandon.
When you're a VC, you expect 9 out of 10 to utterly fail. So you need that 1 out of 10 to turn into a high-growth Unicorn.
this is a great article - Informative and interesting!
It's not helpful to say the term oversubscribed if you mean something different than the existing meaning. Just make up your own word for what you mean or use a different word.
I'd be real curious to see their ROI calculations on this strategic move. Laying off those 1800 employees will cost them $260 million, before factoring in hiring costs for 1800 highly technical replacements.

Salaries for experienced SWEs in Boise are cheap. Looking at levels.fyi there's a recent salary from an Android engineer with 5 years of experience grossing 143k. If they're looking for FAANG talent they won't even be able to poach a college grad for that amount.
> a mixture between a crossword puzzle and archaeology

Cue cave escape scene chased by huge rolling boulder.
The one where you incorrectly claim they lost a class action case, when in fact they reached a settlement?
This is a common argument to support the usage of LLMs: "Well, humans do it too."

We have many rules, regulations, strategies, patterns, and legions of managers and management philosophy for dealing with humans.

With humans, they're incorrect sometimes, yes, and we actively work around their failures.

We expect humans to develop over time. We expect them to join a profession and give bad answers a lot. As time goes on, we expect them to produce better answers, and if they don't we have remediations to limit the negative impact they have on our business processes. We fire them. We recommend they transfer to a different discipline. We recommend they go to college.

Comparing the successes and failures of LLMs to humans is silly. We would have fired them all by now.

The big difference is that computers CAN get every single question correctly. They ARE better than humans. LLMs are a huge step back from the benefits we got from computers.
Is it enough for a teenager to edit video? That's probably one of the most demanding tasks an average teen might need.
> 1. curl | sh, seriously

It's pretty much guaranteed to work cross-platform, and if you're worried about it you can save the script and view it yourself. You're about to run their binary on your machine, why are you concerned about the script you're downloading?

> 2. The default theme is so low-contrast that I seriously struggled to read text.

The landing page when I opened the app had an option to choose from about 40 themes. I tried 3 of them and they were no _worse_ than VSCode's defaults.

> but not to open a file.

usual keyboard shortcuts, and system menu bar?

> Where are preferences to turn it off? Where are the preferences for anything?

System menu bar, which links out to some fairly comprehensive docs - https://zed.dev/docs/configuring-zed

I agree on the vim keybindigs and the performance, though.
That's true, which is why I usually go with the Rule of 500 (total invested across all assets >= predicted monthly expenses * 500). My rational is a safe withdrawal rate of 3.5%, 25% tax on all withdrawals, and an extra 10% to be on the safe side. This adds up to 500 (12 / 0.035 / 0.75 * 1.1).

It's good enough to ballpark it, but it doesn't take into account how tax changes over the years (more gains and less base cost) and between accounts (some can have pretty complicated tax rules that can affect each other), how some accounts become available only later in life (retirement funds), expenses/incomes that aren't fixed, etc etc.
B-but Mac RAM is different than PC ram.
The folder thing is something I’ve noticed too. The problem with folders is that in most interfaces you can’t see their content until they’re opened. In some really bad interfaces you can only see one open folder at a time.

Flat lists let you see the entire thing at once. As long as there is some sort of natural sort order, it’s still a “neat” way to organise things.

E.g.:

SomeApp-PRD-Web
SomeApp-PRD-DB
OtherApp-PRD-Web
OtherApp-PRD-DB
OtherApp-PRD-API

Is roughly how I organise cloud resources. You can instantly see that there are two apps, and that both are web+db but the second one also has an API.

If you had folders you’d just see this:

SomeApp
OtherApp

That just tells you that there are two apps, and nothing else. You have to navigate several times to get to the detail you could see at a glance with the flat structure.

Similar concepts apply to code at every level. I avoid overly abstract functions that do their job through seven levels of trivial abstractions for the same reason. A one-page “flat” function can be more readable and maintainable.
The Killer Stalking Sri Lanka's Men
My Nix experience is pretty limited, too. Nix is not great at pinning to specific versions.

If your older project was made in Nix, it’s no problem. You just check out the old copy of the project and you automatically get the old copy of the dependencies.

If your old project needs some specific major version of GCC, going back to like 4.8, there are specific packages in Nix. You just add “gcc48” to your dependencies and you get GCC 4.8. You still get newer versions of e.g. binutils.

If your old project needs a specific version of CMake, I know two ways to get that, but they’re a little ugly.

First method is to import an old <nixpkgs> containing the right version of CMake, and then import that into your environment. You search through Git history of the nixpkgs repository until you find one with the correct version. Yes, this sounds awful. It’s not that bad. I’m not sure how to do this with flakes.

You can also copy the CMake derivation into your project and modify it to compile & build the version of CMake you like. This is the approach I would normally use, most of the time.

There may be easier ways to do this. I’m not sure.
And thank you for admitting you didn’t know something rather than digging your heels in :) Shows a lot of courage and willingness to grow and learn.
Has anyone used OpenBSD or Asahi Linux on the M1? I have no interest in running MacOS on one but if the battery life is good it might make an excellent laptop for Emacs and light web browsing purposes.
The resins used in carbon fiber for example do not biodegrade. New studies are coming out everyday demonstrating that microplastics and nanoplastics of synthetic materials interfere with the Earth's biosphere, in all sorts of ways. DNA transcription errors, hormonal disruption, increase in arterial plaque and heart attack/stroke, etc. Considering the article is largely about composite materials formed using resins, it stands out that no mention is made of synthetic, plastic resins vs the potential for biodegradable, non-accumulating resins.

For carbon fiber, a quick google reveals that polyacrylonitrile is the most widely used resin. According to google it is not readily biodegradable:
https://www.igtpan.com/Ingles/reciclagem_poliacrilonitrila.a....
Yes wow, because it’s not the norm. Before I switched to an iPhone, I needed to change my Android phone every 2 years or so (sometimes less) because they became completely unusable, from hardware issues (broken connectors) to software issues (I had 2 Nexus 5x that died because of a bootloop issue, unacknowledged by Google). Android also becomes extremely slow pretty quickly for some reason.

For comparison I’ve had my iPhone 12 for 3,5 years and it shows no sign of slowness or battery issues or whatever, and I intend to keep it for at least 1 or 2 more years. When OP say it stays functional for several years, they mean it stays as functional as the first day for several years, with no degradation.
Most employees expect an IPO exit if a business is successful. I suspect that "if we hit it, we will share profit with you" would be a lot less attractive for an employee.
Amazon RDS for Db2 - "Amazon Relational Database Service (Amazon RDS) for Db2 makes it easy to set up, operate, and scale Db2 deployments in the cloud" was announced in Nov. 2023 [0]

[0]: https://aws.amazon.com/rds/db2/
I agree that these open ended transformers are way more interesting and impressive than a purpose built count-the-polygons model, but if the model doesn't generalize well enough to figure out how to count the polygons, I can't be convinced that they'll perform usefully on a more sophisticated task.

I agree this research is really interesting, but I didn't have an a priori expectation of what token prediction could accomplish, so my reaction to a lot of the claims and counterclaims of this new tech is that it's good at fooling people and giving plausible but baseless results. It makes for good research but dangerous in the hands of a market attempting to exploit it.
H-index is still a frequently cited measure for an academic researcher's "impact" when comparing individuals across fields -- the idea being that authors with more papers and more citations are "better" (all other things being equal). Papers with higher citation counts also appear more prominently among search results (e.g. Google Scholar).

If you make the analogy between the www: H-index is pagerank, citations are back links, and authors (researchers) are the domain names. Gaming h-index is akin to SEO hacking for academic authors.
Won't stop lobbyists, spouses, their financial advisors, or them from taking gold bars. 95% are still crooks, or campaign finance reform would've passed years ago. This is a performative ruse.
New SEO ebook and more.

"Introducing our brand-new website and blog, brimming with investment insights and strategies! Join a community of like-minded individuals eager to grow their wealth and financial knowledge. Don't miss out—start your journey to financial success today! Visit us now and take the first step toward mastering your investments!"
LLMs would be better nomenclature than AI in this context.

LLMs are not factual databases. They are not trained to retrieve or produce factual statements.

LLMs give you the most likely word after some prior words. They are incredibly accurate at estimating the probabilities of the next word.

It is a weird accident that you can use auto-regressive next word prediction to make a chat bot. It's even weirder that you can ask the chatbot questions and give it requests and it appears to produce coherent answers and responses.

LLMs are best thought of as language generators (or "writers") not as repositories of knowledge and facts.

LLM chatbots were a happy and fascinating (and for some, very helpful) accident. But they were not designed to be "factually correct" they were designed to predict words.

People don't care about (or are willing to accept) the "wrong answers" because there are enough use cases for "writing" that don't require factual accuracy. (see for instance, the entire genre of fiction writing)

I would argue that it is precisely LLMs ability to escape the strict accuracy requirements of the rest of CS and just write/hallucinate some fiction that is actually what makes this tech fascinating and uniquely novel.
I prefer discussion in open web
It's because AI is useful enough despite its current limitations.

Developers work with what we have on the table, not what we may have years later.
> sig. figs. be getting y’all

This isn't an issue with significant figures but with syntax. Rewritten sans symbols, your comment means:

"3 percent increase from 5 percent"

That math actually comes out to a new rate of 5.15%.