skysight-inc/HackerNews huggingface dataset:
For what it’s worth, perfect freehand (the library I wrote that they’re using for the digital ink) does not use Bézier curves at all to determine the position of points or create the “stroke” polygon that wraps the input points. Curves are only used to draw the line around that stroke polygon.

I tried to incorporate simplification into the perfect freehand algorithm but eventually gave up because I could not find any “stable” algorithm that would not cause the line to jump around as you drew / added points to the end. As far as I know none exist. Most apps that use simplification solve this problem by simplifying the line only after you’ve finished drawing, however for handwriting or artistic drawing this is especially bad as it “undoes” legitimate decisions you’ve made about where to place detail. In addition, the perfect-freehand algorithm simulates pressure based on density of points, so simplifying the line (ie removing points) without creating a corresponding pressure curve will cause a reduction in line variation, which is part of the fun of using it in the first place!

I’d love to learn more about what the canva team has done here though. Freehand drawing is a fascinating problem space, even without the ml / shape recognition stuff.
You just proved the parent’s point.

Greetings from the Netherlands where EV sales share is hitting 50%!
That doesn't help the ambiguity at all.

You have to know contextually that the dollar sign and the digit are part of the name before you can read any sentence that contains the name. That guarantees that you will be confused at least once!
Audio buzz emulation has been done already with a Gameboy emulator and from what I could recall when trying it, it was pretty dead-on. I wished I could remember which one it was but indeed, it's a rarity that emulators emulate some of the more esoteric, some might say 'unwanted', faults of the system.
I legitimately cannot take anyone seriously who claims to be a windows power user. It is a dumpster fire of an operating system.
People lie to their IRBs all the time, unfortunately.
I used my Atari computers on a black-&-white TV for years until I finally got a Commodore 1702 (JVC)... and it was like looking at candy.

The Atari and that monitor had separate luma & chroma ("S-video") so it was sharper than anything else most people could buy at the time. Most CRT simulators introduce too much degradation by comparison. This one looks pretty good.
Stellantis offers buyouts to roughly half of U.S. salaried workers
I would assume so too, except that initially De Beers was arguing that natural diamonds were more pure than the lab-made. When lab-made beat every criteria, 3C and whatnot, created by the diamond cartel, the cartel started to argue for impurity.
> and I'm also not a slave responding to calls/texts.

You do you, but I enjoy talking with my friends and family and I don't see responding to them as "slavery".
Extremely informative. Thank you very much for taking the time to educate me. I deeply appreciate it.
Thanks Ryan. You made me love Rails and to be passionate about coding. I’m still working every day in it and still love it every single day. I have a one man SaaS that is providing for me and my family for the last 9 years.

You played a big part in it. Thank you!
I was pretty excited about the potential for better quantum simulations (and still am) but interestingly enough, at least in specific areas like protein folding, transformers and LLMs and all have made such huge leaps over the past few years that the bars gotten higher for quantum computers, at least in protein engineering. Plus probably some waning in interest when you go from "man it would be great if quantum computers solved this" to "oh wow these AI models can approximate solutions to a lot of the cases I care about I should focus on that now."

Obviously having an analytic solution to a protein structure would be awesome but when protein design tools are approaching double digit success percentages (depends on the protein) in creating designs that work in the lab - that's a lot more compelling than waiting for IBM!

Quantum is still cool though and people should do it since we'll outgrow classical computing for this stuff soon enough (what if two proteins?)
Yeah, ok. People who base marriage on ultimatums don’t tend to get married anyway.
Humans have intentionally been eating insects as delicacies for as long as humans have existed. Every time the prime-number cicadas emerge there's long lines at shops serving them in various ways (commonly chocoalte covered, often other ways as well).

I'll trust your judgment on Beyond Meat vs dung. I'm sure you have much expertise on the nuances involved in dung's flavor, and certainly much much more experience than me in tasting it.
(Shoot Spotter, another thing literally and correctly predicted by Deus Ex back in 1999. In the game it's deployed in Hong Kong though, not in NYC.)
Is there actually a limit to how much data you can output? Modern state sizes are so big you'll never get a repeat in billions of years.

We also mix entropy in continually. Even if your sources only give you 1 bit per minute, within a few hours you'll have way too much entropy for attackers to decode anything before that window.

Plus, if you're using hashing, you can't figure out previous states from the current state.

Then on top of it people generally also use hardware RNGs built into all modern chips. On Linux I believe they don't trust it by itself but mix it with other sources.

Actually building a PRNG yourself would still be hard though!

It would be cool if modern devices had a solid state radiation detector for this purpose. Not so much because they actually need it, but anything security related often sells well, and it might let them put radiation sensing in consumer devices. I wonder how much stuff people would find!
Sent via UDP to a small custom Python server which feeds it into InfluxDB, then Grafana for visualization.
Right. Have you considered perhaps that there's entire industries out there plagued by organizations that use automated tools to muscle out consumers from access of general goods with with no intent of using those products themselves and only act as an artificial intermediary to resale said goods and inflate the price for their own benefit? This plagues concert tickets, shoes, clothing, computer parts, sometimes evening necessary goods like cleaning wipes and toilet paper.

Are those people just good hard working honest capitalists that create a market opportunity and deserve to use their products as they damn well please too?
When the price for the labor is so low for decades (as evidenced by the wage stagnation in so many fields, usually ones with physical labor), disincentivizing any young people from pursuing them, then the problem is pay shortage.

It is still more attractive to look for a 8-5 Mon to Fri desk job.
Which image are you talking about?

I've played with Retro Emulators that add screen reflections and scratches and bezels that look like the original artwork on the arcade cabinets, and... I love it.
Are you going to re-prompt the model with the (possibly very large) context that is available to the user every time they make a query? You'll need to enumerate every resource the user can access and include them all in the prompt.

Consider the case of public GitHub repositories. There are millions of them, but each one could become private at any time. As soon as it's private, then it shouldn't appear in search results (to continue the ElasticSearch indexing analogy), and presumably it also shouldn't influence model output (especially if the model can be prompted to dump its raw inputs). When a repository owner changes their public repository to be private, how do you expunge that repository from the training data? You could ensure it's never in the training data in the first place, but then how do you know which repositories will remain public forever? You could try to avoid filtering until prompt time, but you can't prompt a model with the embeddings of every public repository on GitHub, can you?
AFAIK while AMD didn't produce the 387 there was alternatives from for example Cyrix and IIT at the time.
> The code hasn't, but it would be a lie to say that the application hasn't

It could become less secure too, as WAF software itself can have vulnerabilities or misconfiguration.
> Now I'll admit these ideas are quite broad

Ayup. Especially for small teams working with large piles of software (lookin' at you WordPress) that are insecure out of the box. The ideas are also constrained mainly to fixing SQL injections, which are only an aspect of security.

* Isolate components in case of a breach
That's great but it doesn't fix a breach, it just limits the scope. Better than nothing but if a WAF stops the breach from happening ...?

* Immutability
Cool for those teams that have control of their entire infrastructure. However this also only solves those cases that are caused by mutability.

* Static analysis to look for stuff like devs forgetting to use prepared statements.
Definitely! Sure! But again, if you're using a piece of software off the shelf a lot of that is out of your hands.

* Restricting API endpoints to limit access to necessary tables
Another great idea if you are in control of your software. Hacking into a fairly large project like WordPress to effect these changes (if they aren't already) would require a large team and a ton of maintenance. Basically it'd be a fork.

Do any of these help against a DDOS, or even accidental DOS caused by search spam? Nah, but a WAF at the edge stops the latter in its tracks. I'm not saying a WAF is a panacea for all ills, and yeah I bet a rethink would mean better-built web app firewalls, but discouraging their use would cripple most of the long-tail of the web and honestly that's where all the cool stuff is anyway.
I think that ship has sailed. Our internet traffic has been hoovered up at the telco for decades (and shared with several other governments), our cars tell half a dozen "partners" which businesses we visit, our phones keep track of who we are in proximity to and report it all back to the world's largest ad company, and opening a brokerage account to buy stock involves scanning both my face and driver's license, but this is what would be going too far? Using drones to look at loud noises that might be gunshots?
There's no coming of age moments and achievement milestones in life.

We left behind a structured world where you are expected to do certain things by certain ages and you got social respect for it.

Now we are free to do anything, knowing nobody outside your social circle will really understand what it is you did.
Locks are fun! (Can you tell I'm Dutch? ;) )
So I read through the materials on mesh shaders and work graphs and looked at sample code. These won't really work (see below). As I implied previously, it's best to research/discuss these sort of matters with professional graphics programmers who have experience actually using the technologies under consideration.

So for the sake of future web searchers who discover this thread: there are only two proven ways to efficiently draw thousands of unique textures of different sizes with a single draw call that are actually used by experienced graphics programmers in production code as of 2023.

Proven method #1: Pack these thousands of textures into a texture atlas.

Proven method #2: Use bindless resources, which is still fairly bleeding edge, and will require fallback to atlases if targeting the PC instead of only high end console (Xbox Series S|X...).

Mesh shaders by themselves won't work: These have similar texture access limitations to the old geometry/tessellation stage they improve upon. A limited, fixed number of textures still must be bound before each draw call (say, 16 or 32 textures, not 1000s), unless bindless resources are used. So mesh shaders must be used with an atlas or with bindless resources.

Work graphs by themselves won't work: This feature is bleeding edge shader model 6.8 whereas bindless resources are SM 6.6. (Xbox Series X|S might top out at SM 6.7, I can't find an authoritative answer.) It looks like work graphs might only work well on nVidia GPUs and won't work well on Intel GPUs anytime soon (but, again, I'm not knowledgeable enough to say this authoritatively). Furthermore, this feature may have a hard dependency on using bindless to begin with. That is, I can't tell if one is allowed to execute a work graph that binds and unbinds individual texture resources. And if one could do such a thing, it would certainly be slower than using bindless. The cost of bindless is paid "up front" when the textures are uploaded.

Some programmers use Texture2DArray/GL_TEXTURE_2D_ARRAY as an alternative to atlases but two limitations are (1) the max array length (e.g. GL_MAX_ARRAY_TEXTURE_LAYERS) might only be 256 (e.g. for OpenGL 3.0), (2) all textures must be the same size.

Finally, for the sake of any web searcher who lands on this thread in the years to come, to pack an atlas well a good packing algorithm is needed. It's harder to pack triangles than rectangles but triangles use atlas memory more efficiently and a good triangle packing will outperform the fancy new bindless rendering. Some open source starting points for packing:

https://github.com/nothings/stb/blob/master/stb_rect_pack.h

https://github.com/ands/trianglepacker
Number theory was a big deal already in ancient greece and probably got there from Babylonia, so it's probably safe to say that they didn't live to see the public key crypto usage. :)

But (integer) numbers and their behavior had huge significance in the ancient worldview, and still do even in our days if you look deep enough. And the Pythagoreans et al applied number theory in e.g. music.
There are the deeper principles that are broadly applicable in many fields, generate new practices, and applicable in creative and versatile ways.
Just one example: FIPS compliance is very relevant: RHEL has, k8s by itself does not. And many more...
I think you're generalizing far too much from limited experience. Not all mathematics, even applied mathematics, corresponds neatly to intuitive physics models you build naturally by being an animal that interacts with the world. Quantum mechanics was already given as an example, but I had the opposite experience with cryptography. So much of the training as a CS student was along the lines of "accept that this works" and then practice by implementing something, and sure, it worked, but at no point did it ever become obvious why any of it should work. Until I finally went back and spent a solid year and a half learning group theory and algebraic number theory in a more rigorous way, figuring out all of the proofs we had skipped over, at least trying it myself, but giving up and reading someone else's proof if I couldn't figure it out within a day or two. And in this case, the abstraction definitely came before the application. This is math that was thought to be compeletely useless for centuries, but people worked on it anyway just because they loved math. Lo and behold, it made the Internet possible, so I'm glad someone did it and loved the tedium.
Like I said, it depends very much on how you define computer. To me "processing information" == "computation". And a physical system that performs "computations" is a computer. Therefore, a brain is just one type of biological computer.

In a computer science class, the first thing they should teach is that the word "computer" in the course name is abstract, and not just about the metal slab on your desk. There are fundamental laws that apply to all information processing systems, whether electrical, mechanical, or biological. So it makes sense to put them in the same category at times.

But obviously words mean different things in different contexts, and "computer" might mean something entirely different to a neuroscientist than a computer scientist. But I don't think a neuroscientist would disagree that a brain is a computer using the loose definition I described above
Most enterprises have moved public facing sites to the cloud. And so are making use of the cloud provided WAF solutions all of which are trivial enough for someone to manage part-time.

Also in 20+ years in enterprises have never heard of firewalls being left unmaintained. I don't know how that would pass security audits, why such a critical piece of security architecture would end up in this state or how any in-house development would work given the constant need to make changes to it.
This sounds like letting perfect be the enemy of good. Insect protein would be a new addition to the protein supply and the food supply. It would provide an additional alternative to meat. It might actually reduce the demand for meat, which would be good. I guess I don't see why the option of insect protein would be bad. In fact, it seems it would be good.
Show HN: Stateful light dimmer with haptical feedback using a motorfader
> Zelle has said that 99.9 percent of money transfers go through as users intended, without a fraud or scam report ... Zelle usage has grown significantly since its launch, from 247 million transactions in 2017 to 1.8 billion in 2021.

Their defense is that they only process two million fraudulent transactions a year?
What are you talking about? I remember everyone who was at my wedding.
I'm a bit confused here - all the transmission modes listed require at least 8 seconds to transmit an image, but it also says NASA used SSTV to transmit video at 10fps? How does that work?
Apple – K Cottingham
And the military offers far better pay and upward movement potential (especially the healthcare subsidies for a whole family).
Drawing lines is faster than you think! The perfect-freehand algorithm that I wrote / that canva is using here does not use splines but it does recompute the entire line on every frame. It’s fine at 60fps (and also fine at 120fps) up to a few thousand points on somewhat modern hardware before the paths get too slow to render. The algo itself is just blazing fast, in part because it does not rely on splines (which are much more complex).

For an svg-style approach to ink (as opposed to a raster / dab-style brush) there’s no other option than recomputing the whole line each time. As a bonus, you can adjust the properties after the fact very easily. (You can try that at perfectfreehand.com.)
Interested in the focus on comparison with PyTorch — when it seems like they should actually be comparing to JAX/XLA and IREE.

Do they not know about the latter technologies?
Ask HN: Is building a global cache of all computations feasible?
Thinking about the high cost of training large AI models, and how much duplication in computation there must between such models when trained by company A vs company B (say, training a large language model on the whole internet), I've been wondering if it makes sense for humanity to create a central, global database that stores every computation performed by any connected computer (utilizing either CPU or GPU). This would record the inputs of these computations at a low level (in machine code, for instance) and their outputs (like the result of a summation operation). It would be continually updated with new computations over time.

The primary function of this database would be to act as a sort of "global cache." When a member computer is about to perform a computation, it would first check this database. If the computation has already been done, the computer would simply fetch the pre-computed result instead of redoing the computation. The underlying goal is to save on compute resources globally.

N.B. this does not mean we precompute anything necessarily, but we do store everything we have computed thus far. The hit rate on the cache might be very low for a while, but one would think it'd eventually go up. The way we're going about this (throwing more GPUs at it) just seems awfully wasteful to me.

Has anyone thought about/done any research on this?
I'm not making an argument by analogy, as much as making a prediction of a similar trajectory and similar dissapointment.

That said, there are tons of examples of hyped "current technologies" sold as panaceas that turned into diminishing returns quite fast.
That mitigates a lot, but are companies going to be responsible enough to take a hardline stance and say, "yes, you can ask an LLM to read an email, but you can't ask it to reply, or update your contacts, or search for information in the email, or add the email event to your calendar, etc..."?

It's very possible to sandbox LLMs in such a way that using them is basically secure, but everyone is salivating that the idea of building virtual secretaries and I don't believe companies (even companies like Google and Microsoft) have enough self control to say no.

The data exfiltration method that wuzzi talks about here is one he's used multiple times in the past and told companies about multiple times, and they've refused to fix it as far as I can tell purely because they don't want to get rid of embedded markdown images. They can't even get rid of markdown to improve security, when it comes time to build an email agent, they aren't gonna sandbox it. They're going to let it lose and shrug their shoulders if users get hacked because while they may not want their users to get hacked, at the end of the day advertising matters more to them than security.

They are treating the features as non-negotiable, and if they don't end up finding a solution to prompt injection, they will just launch the same products and features anyway and hope that nothing goes wrong.
Because they are a company outsourced to cheap countries that lost its competitive edge. Average tenure is 1.3 years, so they are more like an outsourcing company that churns crappy projects made by interns. Projects get cancelled due to no promotions
Calif. tech investor's wedding trashed base of world-renowned landmark
Google witness accidentally blurts out that Apple gets 36% cut of Safari deal
The example given in the post was using <div>s rather than semantic HTML elements. The motivation for the principle of using "semantic HTML elements" comes from the lofty ideals of the Semantic Web[0]. However, these principles didn't come from best practice. Instead, someone (Tim Berners-Lee) thought that it was a cool idea and tried to convince everyone of it without demonstrating that it could actually deliver on any of the promises it made. The argument instead is that principles should be based on what is proven to work, rather than what sounds good in theory. I agree with the sentiment in the post.

[0] https://en.wikipedia.org/wiki/Semantic_Web
> You're right of course: that's a one-time cost.

I disagree. This presumes that there's some initial "onboarding" process, and that once that's done you just now will know the structure of the app for all future, never spending more effort on it.

That's only true for trivial apps. What actually happens in bigger codebases is after you stop working in some section of the code for a few weeks, it falls back out of your head and you have to figure it out again. Additionally, without conventions other members of your team are constantly inventing new structures and connections between things.

So really this lack of consistent organization is a continuous drag, not a one time cost.
Is Kubernetes worth it for the avarage homelab user?
> I do think it’s pretty cool that the all-terrain tire option is a custom Goodyear with notches in the sidewall to accommodate the plastic aero covers

The second mortgage that you’ll need to take out when it comes time to replace them will be notably less cool.
Yes the federal regulations make demands in order to connect. The feds can release their hostages and drop the federal regulation demands and deregulate and Texas would surely connect to these interstate grids as they did to Mexico.
But capabilities are not good or bad, no more so than e.g. integers.

What the post talks about is "a good system" of capabilities, that is, a comprehensive, logically sound, lean way to describe capabilities that would be widely accepted and applied. With that, indeed a lot could be achieved.