skysight-inc/HackerNews huggingface dataset:
https://en.m.wikipedia.org/wiki/Shikata_ga_nai

why is this flagged? this is the reason you don’t hear many japanese complaining about it
Pål Wessel Has Died
I'm sad to report that Pål Wessel, original co-author of the Generic Mapping Tools [1], died March 26th

https://www.generic-mapping-tools.org/
The flip side of all this is a used Tesla can be a really great option right now for some folks - bulk of depreciation is over, maintenance/running costs are some of the lowest in the industry (ignoring EV insurance costs I guess, but thats another topic...). The traction battery/motors/drivetrains regularly get to 300k miles. I'd have no problem at all recommending a used 3/Y at the right price to someone if EV fits their lifestyle etc.
Desktop is one of our most requested features:
https://github.com/shorebirdtech/shorebird/issues/397

I suspect we still have many months of work to do for iOS and Android code push. Mostly a lot of features to build for larger customers.

After that, I'm not sure. We have a long list of ideas. Do you have particular needs around Flutter you'd like us to address?
Since this is SQLite SQL, I copied it into a Gist (to give it CORS headers) and now you can open it directly in Datasette Lite (Datasette in WebAssembly running entirely in the browser):

https://lite.datasette.io/?sql=https://gist.github.com/simon...

Here are the 65 hardcoded opcodes: https://lite.datasette.io/?sql=https://gist.github.com/simon...

And the 64 instructions: https://lite.datasette.io/?sql=https://gist.github.com/simon...
It's really up to the players. SuperGMs these days are somewhat addicted to draws because it's a very safe result in a tournament setting and in terms of rating. Therefore these players tend to favour less risky and more calculable openings. They care more about avoiding a loss than they do about winning.

The idea that the large amount of draws is because players are so strong now, is mostly a myth. It's really just psychology and game theory at work.

For a perfect illustration of all my points, look at Aronian vs Grischuk from the 2018 candidates tournament. Here both players chose to play into complications, and the resulting game was wildly complex, with both players making several suboptimal moves simply because the position was just too complex even for two of the strongest calculators in the game at the time.

And in the end, they still ended up constructing a draw by repetition when all 3 results were still possible. Both players had good winning chances, yet the fear of losing finally overtook them and they collectively bailed out of the game.

It's not that players are now so strong it's almost impossible to win, the players just aren't as willing to seek out the necessary positions.
Our culture is saturated with this trope. Compare:

1. Health nut who lives on salad, low-fat yogurt/everything, jogs endlessly, frets about his body being a temple[1]

2. Sex, drugs, good food, rock & roll

Miserly wholesomeness vs. exciting, short-lived unwholesomeness.

[1] The point here is the self-denial. Some people intrinsically likes these things; however, they would then not fit the trope
If you define odds being good as "the odds are good for the ones that choose to study CS", sure. But if you define the odds as "women overall", 20% is a relatively poor number in my opinion. Yes, we're getting better and yes, it takes time. But I don't think we can pat ourselves on the back here. That the women who decide to work on tech do well is (in my very unscientific and unproven) opinion just an indicator that the women who do join tech are on average more skilled than the men who decide to join tech.

That's for a myriad of reasons, but the main one being that men gravitate to tech more, so even if they're not a huge talent they still might choose a career in tech, whereas women might prefer a different career unless they have a very strong calling.
My only question is... why?, I understand pretending to be something official when you're really not at all so you can profit off the confusion, that trick is a dime a dozen on the internet... but zsh? Really? Surely there are more profitable ventures wearing the skin of a free shell for Unix operating system?
For that stuff, yeah you're correct.

What I've seen is issues with the implementation of those libraries in a project.

I don't remember exactly, but I was playing with someone's wrapper for some kind of machine learning snake game and it was taking way longer than it should have on back of the napkin math.

The issue was using either a dict or a list in a hot loop and changing it to the other sped it up like 1000x.

So it's easy to think "yeah this library is optimized" but then you build something on top of it that is not obviously going to slow it down.

But, that's the Python tradeoff.
I got no prob paying for apps (I buy/subscribe to all of them). I simply hate it when I pay them, and they merely 'hide the add' but still 'talk' to adjust, googletagmanager, and so many other advertisers/trackers.
Yes, there are several. See this blog post from Databricks describing the landscape of LLMs trained on AMD hardware for example: https://www.databricks.com/blog/training-llms-scale-amd-mi25...

The most interesting one IMO is OLMo from AI2, which is truly open. You can read their blog post about it (https://blog.allenai.org/hello-olmo-a-truly-open-llm-43f7e73...) but basically it is open everything - they released everything you need to reproduce their weights (training data, training code, evaluation code, and weights) with a friendly (Apache) license.
Not necessarily even for managed languages. Sure, you can in theory just package an ARM runtime, but the devil is in the details.

For example, it's typical that packaging tools only build packages for the platform they run on. So if you run the tool on Windows Intel, you get packages for Windows Intel. Want ARM? You need to run it on Windows ARM. Don't have such a machine? Tough cookies.

This is one of the problems that motivated me to write Hydraulic Conveyor, and so it can build packages for every platform portably including Windows Intel and ARM regardless of what OS or CPU you run it on. It knows how to download all the different runtimes for all the different OS/CPU combinations and make and sign all the packages in parallel, without any native tooling. It knows how to strip out native binaries that appear in managed dependencies, but don't match the target. It makes download pages that detect what CPU you have (on Chrome, Safari lies unfortunately), or for Windows an installer that detects the user's CPU and downloads/installs the right arch for the user. So it handles all these details and lets you release for hardware you don't have. But that's a fairly unique party trick. For every other system you're going to be reliant on multi-platform CI, which may or may not support every target (ARM Macs took a long time to come to GitHub Actions), and in which adding extra platforms increases the cost of every build - sometimes by a lot!

Apple relied not only on Rosetta but also their whole fat binary infrastructure, and they have sticks like notarization and carrots like the App Store to encourage devs to do the work. Also Mac apps tend to be well maintained and gaming is not so common. MS don't have the same advantages, so apps will presumably be running in emulation for a lot longer and have more compatibility warts.
23 years in and all my (contract) work comes from my network. Parent comment is the advice you need.

It's never too late to start, but it is hard to do on demand. Also the way you behave within a company will follow you. If you're NotAnAsshole™, can do the job and I've worked with you I'll help you get your next job for the rest of your career.
Distribits
heh, with power strips that have very long cables, I've seen them plugged into themselves a few times as well.
I don't know how it's developed since, but from what I remember that was how it started - the AIs weren't following the standard moves (joseki) that we'd built up over centuries and human players were thrown off by the nonstandard responses that were working better than expected.
To elaborate more, when we say that a signal is the linear combination of infinitely many frequencies, if those frequencies are multiples of a fundamental frequency (that is, a countable set of frequencies), then we are talking about a Fourier series and the signal must be periodic

But if those frequencies span a whole continuum (rather than frequencies f, 2f, 3f, 4f..), that is, an uncountable set of frequencies, then this signal is non-periodic and we can't talk about a Fourier series anymore, we must use the Fourier transform
We're getting to it, slowly but surely. Wheels of justice...

FSD is quite an amazing example indeed - they are supposedly unveiling a Robotaxi in 4 months, while he spent the past 10 years saying that each
Tesla ever produced will turn into a Robotaxi with an OTA update.

All of this is fun and games, but you can only imagine, what kind of accounting shenanigans this guy is doing behind the scenes where nobody can watch, if he's willing to lie so blatantly in the open.
Hunter gatherers had more leisure than farming peasants. Surely, one can spot the trend.
I don't think it's low effort at all. In fact, I totally see where the author is coming from despite loving FSD and using it often.

Driving is a very personal activity. There are nearly infinitely many variables driving every decision during every drive, and the default experience that people will want from something like this is "someone that mostly drives like me and never makes mistake because computers".

FSD is not that. It will be a while before it is that. Until then, I can totally understand someone "never using this again" (for a while) over a few fumbles that FSD made that they would not have (in most cases).

Tesla has to know this because they also know that if even 10k Tesla buyers decide to subscribe to FSD, that's an extra $2M/month that can be (mostly) shoveled back into training infrastructure!
Talking directly to the kernel / driver / firmware.

As others have said, George Hotz is doing his best in reverse-engineering and skipping layers.
Google to Tone Down Message Board After Employees Feud over War in Gaza
I've seen his nano GPT implemented using JAX, now we have C/CUDA. I'd love to see if nano GPT could be doable in Mojo. I took a stab at a Mojo conversion of his Wavenet project (Andrej's zero to hero course) and I gotta say... python has so many nice features lol. Stating the obvious I know but what you see done in 6 lines of python takes so much more work in other languages.
Begging EuroRust to acknowledge independent workers
Meanwhile, for the modern Microsoft implementation: Booting up a fresh Windows 10 laptop. It wants me to select my country from the dropdown. I type in the first letter of my country. Nothing happens.
Hooray!
> That said, because they've gotten so good, chess at the top levels is pretty boring

Yeah, I feel the same thing about Magic formats when the pros play. When a format is new and people are discovering, and they have to rely on their gut, and make educated guesses. That's when it's fun to play and watch.
How would C track references for heap-allocated data originating from (A)RC-based language?

I don't think what you say on .NET correlates with reality - you are not supposed to pass managed objects across FFI (you just can't, it won't let you unless you do unsafe cast shenanigans) - the correct way is to either marshal the data or use blittable representation (plain structs can be passed as is, either by reference or value, same goes for arrays/pointers of primitives). On the rare occasion where unmanaged code needs to keep a GC object alive, it is passed via special GCHandle but that's an exception not the rule.

Swift has its own high level representation for such objects which are very much not FFI compatible. ARC in Swift is a feature that requires compiler involvement to work correctly, and its structs are not FFI compatible unless they are "trivial". Swift also has its own more advanced Swift Library Evolution ABI, which is strictly not C ABI you expect out of your regular library and has its own calling convention and semantics.

Overall, there seem to be strange misconceptions about how these languages work so it would be best to check with the documentation first:

.NET P/Invoke (FFI):

- https://learn.microsoft.com/en-us/dotnet/standard/native-int...

- https://learn.microsoft.com/en-us/dotnet/core/deploying/nati...

Swift non-C ABI:

- https://github.com/apple/swift/blob/main/docs/LibraryEvoluti...

- https://www.swift.org/blog/library-evolution/

Swift ARC:

- https://docs.swift.org/swift-book/documentation/the-swift-pr...

- https://github.com/apple/swift/blob/main/docs/ARCOptimizatio...

(I don't actually know if any other other platform, besides Swift itself, implements Swift ABI support, but .NET is going to have it in .NET 9 - likely the first non-Swift platform to do so)
My dishwasher sends the water into the waste disposal built into the sink. If the waste disposal has a lot of food that hasn't been grinded it up, it seems to slow the draining of the dishwasher so not all the food gets drained before the rinse cycle. So I learned to run the waste disposal for a couple seconds before starting a dishwashing load.
See my comment on this here: https://news.ycombinator.com/item?id=39973816
I found the culture on OGS, particularly wrt moderation, to be pretty great (as a newcomer, and I exclusively play 9x9).

I've read about KGS but I've never figured out how to engage with it. (I know it to be the OG, srs bzns venue, though).
> Google owns the OS

Google is still beholden to consumer privacy laws. I’m sure if you turn off location data in your Google account they won’t track you. The guys in the Room 641A will.
"Hey, Boss, I want to spend money on something that we already have. The benefits that spending will return are conditional, probabilistic, and long term, if any."

Its not an easy sell.

Several people have done fine work in providing example sales pitches, articulating the benefits of supporting open source generally and maintainers specifically. Even with that kind of help, it's still a hard sell.

Many business are run for today with no regard for tomorrow, stiff suppliers and contractors and come back next quarter whining about "but if you won't extend more credit you'll be taking hundreds of jobs!" Actual legal obligations don't mean much to those people; they'll never care about a commonwealth.

The better type of business still has far more things to do with their money than care for an apparently healthy and vibrant common ecosystem. Usually, the best they can do some public relations spending on par with the rest of their charity work.
Are there specific words for being anti-black, or anti-asian, etc, rather than just 'racist'?
Taking a peek inside the package it seems to mostly be the libraries - CuFFT alone is about 350MB for example, twice over for the debug and release versions. I'm guessing those are probably fat binaries pre-compiled for every generation of Nvidia hardware rather than just the PTX bytecode, which would help to speed up fresh builds, at the expense of being huge.
Is there a good example where FSD has risen to the level of lies (like they were never intending to build it), rather than just being typical really crappy estimation by software people? (that they were just woefully delayed in when they've actually released it). I think it's the latter since it's clear that they've been continuously working on it for years.

I opted not to buy it for exactly that reason, I just didn't believe the "coming in a couple years" – I thought we were further away from it.
Just tried Google Classroom on my iPhone Pro and animations are indeed choppy and scrolling feels weird
It would be great if someone created a tutorial around this explaining exactly how it works and how to do a test training run. I’m aware it’s not feasible to train a “real” model on personal hardware but it would be nice to have a practical learning experience. I’m not sure if there are good alternatives for that.
If I was a professional player of any sort of game that AI can play then I would never play against AI.

Just be a human, play against other humans. Who cares what AI can do?
Definitely true. The only way this is not true is if by some miracle we get an emergent property at a ridiculously large scale, and even then it could be something that could be simplified, which means that scale is at best a way of stumbling upon general intelligence. However, biological brains are incredibly efficient, with very small animal brains demonstrating robust mechanisms of awareness and learning. There are severe cases of brain conditions where the majority of the brain is missing, yet these people can still show awareness and basic emotions.

We know gut bacteria affects the brain and how emotions are also linked to the state of our bodies, I think there is a knowledge gap in our understanding of intelligence that involves the necessity for embodiment.

Our bodies are potentially doing a big part of the "computations" that make up our ability to have general intelligence. This would also explain a lot of how lower level animals like insects are able to display complex behavior with much simpler brains. AGI might be such a hard problem because it's not just about recreating the "computations" of the brain, but rather the "computations" of an entire organism, where the brain is only doing the coordination and self-awareness.
I believe it was Tolstoy who said something to the effect of "Happy families are boring, because they're all the same. Unhappy families are interesting because they're all different."
It is great, just wish the install size wasn't so huge (40MB on Linux).
I wasn't at all trying to say his work was simple. I was trying to say "deepmind were trying to build an AI that gets good at games without anything in their structures being specialized for the game, lightvector asked what if we did specialize the model on Go". And he did some wonderfully clever things.
That doesn't sound right. Their public demo ran on 568 LPUs because they had Mixtral-8x7B and LLaMA-70B (45B and 70B respectively). IIRC their cards each have slightly over 200MB of SRAM so this almost exactly checks out.

A 7B model would then be able to run on about 60 LPUs. Even at $20,000 per card that would be only $1.2 million and I highly doubt the cost is actually that high, that's just what DigiKey says the cost of an LPU is, if you're trying to buy just one :)
Most cases are resolved out of court, for a multitude of reasons. One being damage to public reputation and employee morale.
So are you saying that now was not the time for a major release? Or are you saying that the major release had too many breaking changes?
I find it really hard to imagine people following famous AI personalities as much as real musicians if they know that the AI are AI. Humans are social creatures.

I guess at some point there will be a popular "musician" who is relieved to be an algorithm, and we'll see what happens.
It won't follow even simple, non-ethnic specific instructions such as "draw three women sitting at a cafe", it re-writes and completely forgets the original number of women, and adds a lot to the query that wasn't there.
I think the reference to Homebrew may have been using it as a cautionary tale, not a success story. ie. implying the Unix environment that OS X/macOS provides is so messed up that even a project attempting to remedy the shortcomings ended up producing the rather unhinged brew.
At an org I worked at, we (the whole org) were notorious (internally) for just not being able to execute big projects. In late 2022 2 of the top-level big priority initiatives got "pivoted" (eyeroll) because they just weren't "going". And allegedly a big part of the problem was that nobody wanted to admit that things weren't going just swell on these big projects, so they gussied up their status reports, and then when this was done systemically across every person in the entire project, you got a project that looked real good until it mysteriously missed deadlines, and after enough of that, finally the curtain pulls back.

Now, management and leadership were most affronted by the state of affairs, I assure you, and many words were spent extolling virtues and lambasting vices and sin. But let me assure you, it's not that they love eloquent speech, they were simply not allowed to say the real reason for any of it because the real reason was a trait of the org that the founder thought was a key to their success.

They run a meritocracy, you see, and a pay for performance comp philosophy. They run performance reviews very tight, and getting a passing mark is publicly said to be something to be proud of. The natural result being that everyone lives in constant fear, and being put on a big risky project is a great way to not get to vest your whole equity grant. So everyone plays defensively, the smartest people dodge the hard/big projects, and so on and so on.

And they'll never fix it because they are not allowed to. How tragic.
Why aren’t the people conducting this test in jail? Who gave them the authority/permission to run these tests? Geoengineering is literal cloud cover for further GHG emissions.
Yeah I don't remember ever seeing them saying it was ready. When I bought the 3, they basically said it was a few years away and you can pre-pay now to help fund the building of FSD.
> The context you gather, the way you gather it and your prompting matters.

But your API provider gets to see most of that because you send it to them along with your prompts. I don't know what the ToS for these AI providers are like, but it seems like they could trivially copy your prompts.
I think it's not so much that academics don't value aesthetics or don't want to change as that the alternates are NOT _strictly_ better. It's all tradeoffs. And we are used to changing template to submit to different journals, so it's not that much of an issue.

Extensive or summarised background? -- You don't want extensive background material padding every paper out with several more pages, so once you're familiar with 'the literature' (the key background details), often you can quickly skim and get an idea of the baseline of the paper just from specific citations

Citation formats? -- some citation formats are better than others...I usually publish in journals using IEEE/numeric, which can be denser but requires flicking to the Bibliography...names can make it easier to recall the gist of a paper if it's a famous one like the 'Hinton' or whatnot, but can really pad out content if you have strings of names inline

Papers too short/page limits? -- You don't want pages and pages of content, otherwise the paper is likely contributing _more than one idea_, and so is less focused (and also kind of dilutes the review process). If a paper contributes A and B, but B is weak, do you still let it through if A is great? If the contributions are distinct, you can easily have an A paper and then request some more work for B.

2-column? -- 2-column and dense can make it easier to quickly jump from section to section, or get more information on a single page...so when reading in print format (or digitally with full-page view), you can easily see what was said earlier on a page, whereas 1-column tends to have much larger font so less content-dense.

I don't think we are at optimum, but I don't think ANY new format that's been proposed gives such a noticeable benefit to some of these areas as to overtake. Fully digital with hovers and links and stuff may be useful, but would completely regress when printed out for reading and annotating.