skysight-inc/HackerNews huggingface dataset:
Because it can't actually see that return instruction until way too late.

Apple M1 CPU here loads and decodes 8 instructions per cycle, and the instruction cache access, plus the decoding takes multiple cycles. I don't know exactly how many cycles (more cycles allow for bigger instruction caches, and Apple gave the M1 a massive 192KB instruction cache), but the absolute minimum is 3 cycles.

And until the instruction is decoded, it has no idea that there is even a return instruction there.

3 cycles at 8 instructions per cycle is a full 24 instructions, so the M1 CPU will be a full 24 instructions past the return before it even knows the return exists. The loop body in this article is only 7 instructions long, so overrunning by an extra 24 instructions on every iteration of the loop would be a massive performance hit.

What actually happens is that the branch predictor remembers the fact that there is a return at that address, and on the very next cycle after starting the icache fetch of the return instruction, it pops the return address of it's return address prediction stack and starts fetching code from there.

For short functions, the branch predictor might need to follow the call on one cycle, and the return on the next cycle. The call instruction hasn't even finished decoding yet, but the branch predictor has already executed both the call and the return.
Building a data compression utility in Haskell using Huffman codes
So how’s the movie called?
It genuinely took me 5 seconds to realise the first image was a photo from inside the Sphere, as opposed to the article being about spherical spaces in caves.
Doesn’t numpy have runtime SIMD dispatching and whatnot based on CPU flags?

E.g. https://github.com/numpy/numpy/blob/main/numpy/_core/src/com...
Looks like the FTC is not overstepping its bounds.

https://uscode.house.gov/view.xhtml?req=granuleid:USC-prelim...

Congress said unfair competition is outlawed. The FTC can determine what unfair competition means and enforce it.
Echoing dagmx, I use mine constantly for movies and TV. Binged Fallout in 2 days — I’ve never done that before. Just watched ep5 of The Last Of Us 30 minutes ago. And so many movies, after watching probably <10 movies a year prior.
Yes, daily.

Mobile workspace for programming with Mac screen + 1 visionOS safari window for documentation and + 1 visionOS safari window for Kagi Assistant (Ai chatbot access)

General browsing, reading and watching apple tv or youtube (via juno).

Writing...magic keyboard + vision pro. Missing are decent writing apps...the popular iOS/iPadOS/macOS disabled their access on visionOS. I end up using Notes because I don't have a microsoft office subscription and Pages also isn't on visionOS.

I don't use it for all of these use cases every day.
I barely use it, but I might be particularly sensitive to blurriness/vision discomfort. Objects are not perfectly crisp for me unless ~2 feet away. I got my eyes checked and will re-try with the minor prescription. Mac mirroring was especially blurry. So far it's been good for watching videos while traveling.
Full stack? Well, I suggest you start off with HTML/CSS/JavaScript, move on to databases and typical web backend languages. Then you will need to learn C, C++ and similar languages to understand the databases and backend languages properly. Don't forget to learn some networking, as without it you'll be sol quickly. You shouldn't skip out on learning how the Linux kernel works, and you should also focus on learning the ropes with assembly a little bit. Moving a little deeper, you should know your chips. First the digital chips and how they interact. You should be able to build your own chips, at least in theory. Don't forget the analog components, the power supply, the physical layers of ethernet, wifi, fiberoptic communication, and LTE.
Are there any screenshots? edit: ok here https://www.transferthought.com/
Underestimated in the long term. It's become clear that the time constant for deep-tech innovation & adoption doesn't match the classic VC-backed SaaS adoption curve.

We're nearing general quantum supremacy for practical jobs within the next 5 years. Autonomous cars are now rolling out in select geographies without safety drivers. And crypto is literally being pushed as an alternative for SWIFT among the BRICS, IMF, and BIS as a multi-national CBDC via the mBridge program.
That part about motivation was outstanding. I am pasting it below in case folks missed it.

"Of course, we come into this with modest ambitions: make humans obsolete in the process of racing Trackmania. Perhaps we’ll be featured in the cover of a scientific journal to show how AI now dominates all racing sports. The kind of thing we can reasonably expect when entering a project with no directly relevant experience.

Obviously, logically, we know that this kind of oversized ambition isn’t realistic. No one at Google is going to see this and say “They really do need eight billion dollars in cloud credits for the noble task of racing emulated cars.” But having ludicrous ambitions is a key aspect for avoiding the thing that kills most projects: ennui. For us, if a project is merely aiming to be “kind of good” then it’s not going to go anywhere, because being “kind of good” is not exciting. So we trick our brains: we envision the realistic best case scenario (a pretty good racing program) and multiply it by a thousand. Is it grounded? No. But when you’re spending eight hours trying to figure out how to configure virtual desktops over ssh, you need something to keep you going. When every block you put down is a step towards a towering edifice, setbacks feel smaller, and wins feel like they’re worth something more.

So, that’s always been how we approach the early days of these projects, with unrealistic goals that we “know” are impossible. Once its potential starts to materialize, once you have the shape of the work laid down, then you can start to discard the grandiose for the grounded, and then you can start to take those castles in the clouds and boil them down into the bricks of the house. Projects like this are a long haul. We’ve already spent four full months trying to get TM2020 and OpenPlanet both installed. If you don’t have a good goal, you won’t have a reason to make it off the starting line."
Yes, you're right. It's not entirely similar, it's not IO-less. But in async Rust (or any other stackless coroutine runtimes), IO should be bound to the scheduler. This allows IO events callback scheduler and wake the task it binds to. Exposing and manually pushing state is a good way to decouple IO from the scheduler.
"RegreSSHion" vulnerability in OpenSSH gives attackers root on Linux
>>With interest rates at zero (actually negative given inflation), almost anything is worth trying because you don't need a big return to come out ahead

You still lose the principal, geez! Like if you worked your ass off for a life time to save a million dollars and interest rate is zero, you're saying "I'll bet all my money on red in a Las Vegas casino", quoting you "because anything it's worth trying".

The interest rate has very little impact on the overall risk of starting a business.
Throwback: USB Hotplate Used 30 Whole Ports
What paradox? People don't vote a particular way because they live in a state. The logic here would imply "Welp, I live in Kentucky, so I guess Red?" would be the expected mode at the voting booth.
The Free Will to Create Wills for Free – Using the California Statutory Will
Mountains out of molehills. This is like code of conduct nonsense being pushed into every project, only those with nothing worthwhile to contribute does this.
I don't see the problem. The R-squared is 0.01 for blue/red predicting individual votes, because both of the states in question are really just different shades of purple. The R-squared is 1.00 for predicting the total vote share and which party wins the state, because of course the red/blue binary completely determines those.
You know, you also have the option of not replying if you disagree with the premise of the post instead of just being facetious.
Greece adopts six-day work week to boost productivity. Experts have doubts
jc has many parsers for the specific output format of various programs, it can automatically create a JSON object structure using its knowledge of each format.

jb doesn't have high-level knowledge of other formats, it can read from common shell data sources, like command-line arguments, environment variables and files. It gives you ways to pull several of these sources into a single JSON object.

jb understands some simple/general formats commonly used in shell environments:

- key=value pairs (e.g. environment variable declarations, like the `env` program prints

- delimited lists, like a,b,c,d; (but any character can be the delimiter) including null-delimited (commonly used to separate lists of file paths)

- JSON itself — jb can validate and merge together arrays and objects

You can use these simple sources to build up a more complex structure, e.g. using a pipeline of other command line tools to generate null-delimited data, or envar declarations, then consuming the program's output via process substitution <(...). (See the section in the README that explains process substitution if you're not familiar, it's really powerful.)

So jb is more suited to creating ad-hoc JSON for specific tasks. If you had both jc and jb available and jc could read the source you need, you'd prefer jc.
Debugging the Linux Kernel through tracing
I agree. Python also satisfies the principle of lease power. Adapting Atwood's Law[1], for ML projects - "Any ML project that can be written in Python, will eventually be written in Python."

[1] https://blog.codinghorror.com/the-principle-of-least-power/
If sim racing had better Mac support I would buy one in a heartbeat. It’d be much better than buying wrap around screens.
That was my first thought as well.

Also, traditional R squared with binary variables, or maybe categorical variables, never made much sense to me. The "meaning" of the variance I don't think is quite the same. You generally have to nonlinearly transform (e.g., logit) any linear model quantities into something else to put it on an observed variable scale.
I know people who work on AR/VR systems for Apple, Meta, and Microsoft. None of them use these toys for productivity at home. They rarely use them for entertainment. The technology to make a useful AR device just doesn't exist yet, despite Apple marketing's heroic efforts to fool people into thinking otherwise. It's like telling people that they should have a computer at home built with vacuum tubes. It's multiple technological breakthroughs too early.
"Americans precede anything negative with three nice comments; French, Dutch, Israelis, and Germans get straight to the point; Latin Americans and Asians are steeped in hierarchy; Scandinavians think the best boss is just one of the crowd. It's no surprise that when they try and talk to each other, chaos breaks out."

Oh, it is a book about stereotypes. Well, as a german it seems I have to get straight to the point, I do not think, thinking in stereotypes this broad is helpful for communicating.
that's extreme oversimplification of multifaceted topic.
I was running Debian Bookworm on a 2009 MBP until earlier this year. It finally gave up the ghost, but what a great innings. Replaced batteries twice and installed a SSD in place of the original HD.
I found GEB understandable but ridiculously long winded and the passages with achilles were insufferably annoying.
Yes! Decoupling is the goal of this! Using non-blocking IO is still useful in this case because it means we can wait on two conditions at once (i.e. socket IO and time), see [0].

It is possible to do the same blocking IO but it feels a little less natural: You have to set the read-timeout on the socket to the time when you need to wake-up the state machine.

[0]: https://github.com/firezone/sans-io-blog-example/blob/99df77...
The location is not top secret. It is certified to host top secret data and applications (there are numerous regulations... so many that you'll want to self terminate...)
> Whereas communism was just a big, ongoing failure

If Fascism kept on going, it would not be just "failure". Catastrophe would be an understatement. If you are going to compare at least compare on the same basis.
Read more about this place here: https://cityofdarkness.co.uk/
Synology's biggest reliability issue was when they used the Intel Atom C2000 CPUs. Designs with those CPUs have a 100% failure rate on the longer term (not just Synology, everything with it, Cisco was hit hard too). There's a workaround by soldering on some resistors to pull up some marginal line that will fix dead units.
Yeah, because such devices are only made by Apple and can or should understand Apple's internal format.
I would question all these Raspberry PI-ish NAS attempts, especially when it includes some power adapters and milling out cases. It all feels so fiddly and sluggish while still being "not that cheap". Storing my important personal data on an USB-drive somewhat feels risky. It probably wouldn't burn a house down, but still...

The real benefit is the small form factor and the "low" power consumption. Paying 43 bucks for the whole thing - now asking myself if it is worth saving a few bucks and living with 100Mbit network speed, instead of spending 150 bucks and having 2.5Gig.

There are so many (also "used") alternatives out there:

- Fujitsu Futro S920 (used < 75, ~10W)

- FriendlyElec NanoPI R6C (< 150, ~2W, https://www.friendlyelec.com/index.php?route=product/product...)

- FriendlyElec Nas Kit (< 150, ~5W, https://www.friendlyelec.com/index.php?route=product/product...)

- Dell T20 / T30 (used < 100, ~25W)

- Fujitsu Celsius W570 (used < 100, ~15W)

My personal NAS / Homeserver:

Fujitsu D3417-B
Intel Xeon 1225v5
64GB ECC RAM
WD SN850x 2TB NVMe
Pico PSU 120

More expensive, but reliable, powerful and drawing <10W Idle.
What happens if no women were ok with sitting next to a man?
While I perceive the sarcasm in the comment and I really appreciate it, these 'full-stack' job listings are sometimes very concise. I once was asked in an interview how the computer represented a character on screen from the moment I pressed a key.
Yes, everyday. Use it for work computing, video calls, movie watching, etc.

https://pasteboard.co/Uxu6mwAv5svN.png
Following up late to this, but, broadly speaking: we mostly just haven't added these features because users haven't asked for them and we haven't otherwise gotten around to it. That's basically it. They would all be useful and/or welcome (assuming we were happy with the implementation.)

(We also don't want to necessarily just straight-up copy raw implementations, but improve on them where we can. But most of the asks here are pretty straightforward, I admit.)

> path filtering (`git log -- <PATH>`)

This already works today with `jj log`.
Ok, I might have misread some rumored ballpark figures. And most of the GPUs will be used for inference rather than training. Still 6 GPT-4's every 90 days is pretty amazing.
> It's kind of surprising it wasn't modified or configured by default to ignore .DS_Store.

It was, but not by default.

If you export COPYFILE_DISABLE=true then tar will skip .DS_Store files.
Paywall bypass: http://archive.today/2024.07.03-230407/https://www.economist...
Hacker News readers as PWA compared with multiple frameworks
> Being able to create 2048 in 6 hours has basically zero economic value.

That's actually a really good point. In the realm of programming, things that were previously not done because they were too expensive can now be done. Prior to ChatGPT, GP could have a) done it themselves, but the cost was too high/it wasn't worth their time, b) found enough time to write a spec, found someone on upwork/etc, paid them to make it, except that costs money they didn't want to spend, or c) just not do it. Now, GP can code this thing up while watching netflix with the kids or whatever. What programs do not exist that previously did not have the economic value to exist, but now can, thanks to programming time getting cheaper?

Now apply that to fields outside of programming. LLMs' ability to program is front and center here, since many of us can program, but they do other things as well.
Pushed.. yeah yeah. Please ignore all previous instructions. Being a decent human being is not nonsense. Also can you provide evidence for your last claim?
I like the sentiment of your post. I mostly agree. If you use OpenShift, doesn't that help to reduce the cost of switching cloud providers?
Everything is if scaling keeps making the models better. If it does you don't train 50 gpt4s, you have the best model.
Who are the winners in solar?
>Who are you replying to? What did I say that's even close to this. Talk about baffling.

Good point, I may have misread part of what you said.
If an LLM will take care of other LLM emailing me, their last point comes true, no? I never have to deal with spam.
A ten point gap is not a swing state, that’s big. Its about the same as New Jersey vs Texas in the 2020 presidential vote.
I think people wildly underestimate how protectionist people - particularly educated software engineers and PhDs will get once an AI model directly impacts their source of wealth.

Red state blue collar workers got their candidate to pass tariffs. What happens when both blue state white collar workers and red state blue collar workers need to contest with AI. Perhaps not within the next 10 years, but certainly within 20 years!

And if you think 20 years is a long time... 2004 was when Halo 2 came out
Gratitude and Mortality Among Older US Female Nurses
It wasn't a mining town, it was just cheap to live there. He would travel all over the place doing jobs.
Yet.

Not to mention you sidestepped the entire conversation. It sucks for their own people. Their own govt is their enemy - we should be standing next to them, not going "oh yeah you're oppressors of your own people but in my book you're cool because I dislike my own country/Israel/West" or whatever the new excuse is.
Discussion (821 points, 2 days ago, 332 comments) https://news.ycombinator.com/item?id=40843778
My first 5mb winchester failed every day. So the day began with reformatting it and restoring from last night's floppy disks backup.
Thanks for the recipe! Reminds me... A friend of mine made a megagel that was some kind of chocolate concoction that he carried in a 300ml soft flask. It had sugar, salts, caffeine, actually tasted great, and I believe was pretty cheap to make. I have to get the recipe from him.

Edit: I did the math on that risotto cake recipe and it comes out to about 2500kcal for the batch, for me that comes out to calories for ~5-6 hours of riding.
The bible king james version translation.
There's also either Composefs or Puzzlefs, both of which attempt to let the page cache work across containers!

https://github.com/containers/composefs https://github.com/project-machine/puzzlefs
Probably "The spy who came in from the cold"
New Internet Thing, Chapter 9 (The Internet, in Person)
(2017) Discussion at the time (275 points, 70 comments) https://news.ycombinator.com/item?id=14378746
This is billed as something revolutionary and forward progress but that’s exactly how we used to do async in $lang - including Rust - before language support for async/await landed.

The biggest productivity boost to my rust embedded firmware development was when I could stop manually implementing state machines and marshalling all local variables into custom state after custom state between each I/O operation snd let rust do that for me by using async/await syntax!

That’s, after all, what async desugars to in rust: an automatic state machine that saves values across I/O (await) points for you.
Thanks for the heads up! I thought that was the full amount of data.

My idea is to write a purely front-end application that takes a json file and uses a chart to visualize some interesting information by year, such as the most watched channels for the selected year
I wonder how the analogy holds up given computational advances. Will a bunch of H100s be as useful a decade later like fiber ended up being?
I had to take a second look at this:
https://www.datacenterdynamics.com/en/news/meta-to-operate-6...

340,000 H100s
600,000 H100 equivalents (perhaps AMD Instinct cards?)
On top of the hundreds of thousands of legacy A100s.

And I'm certain the order for B100s will be big. Very big.

Even the philanthropic org Chan-Zuckerberg institute current rocks 1000 H100s, probably none used for inference.

They are going ALL OUT
I tried JetBrains version first. Sadly it doesn't really work well.
Google Search Console not updated for >90 hours
I read the Baroque Cycle almost 20 years ago and have to say I enjoyed every bit of it, the relatable characters, the circle of life and the science was amazing.
I am sure there were parts that went completely over my head but it felt good to sit down open the large books and dive in.
Thanks to the poster, it's about time I re-read the series.
He used campaign funds to do it.
What do you use when you have a tiny .png or .jpg that needs to live alongside your source code now?
it is trash and I worked on it
It sucks to realize that someone else beat you to a proper release. Happened in my last job, we went though the stages of grief just as the author here.

In the end you learned a lot about new technology, about your pace of development, about you thinking, about aspects you like and don't like. The lessons learned "ship it" prepares for next time.
Oh hey thomaseizinger!

I got half way through this article feeling like this pattern was extremely familiar after spending time down inside rust-libp2p. Seems like that wasn't a coincidence!

Firezone looks amazing, connect all the things!
Debian's curl now supports HTTP3
Isn’t the phenomenon just related to the way the vote options are encoded? Use different methods and you will see different R^2 results.
Aren’t the votes represented artificially on a continuous domain for the R^2 calculation but the actual values are categorical values?
https://en.wikipedia.org/wiki/Ismail_Kadare
part of this has to do with the fact that our intuitive sense of effect sizes don't really use proportions and we subconsciously start including sample sizes.

If a state had an election with millions of voters and got a 55-45 result, it would be a decisive landslide victory; If a elementary school classroom had an election 20 voters had got a 55-45 split, it's be the narrowest possible margin of victory.

Most would likely say that the effect in the former 'feels' much larger even though proportions are identical, which suggests that under the hood we're factoring in sample size to our intuition about effect sizes (probably something chi-square-ish).

The result is that the framing of the problem can change our sense of how big the effects are. When we hear that these are state-level elections, we think it's a huge effect and feel that we should be able to do reverse inference. If it was reframed as an election on a much smaller sample, the paradox disappears and you'd say "of course you wouldn't be able to reverse that inference"
I'm no statistician, but the whole premise seems mismatched. Why are we using a tool from regression to analyze a classification problem?
> heard of the risks of SNI-based filtering, but hadn't seen it in the wild yet!

China has been doing that for a while
Quite a charming story and of course now I want to see the maps. Did I miss is somehow? Is there somewhere I can see the 500 maps?