skysight-inc/HackerNews huggingface dataset:
Like with music generation models, the main thing that might make "open source" models better is most likely that they have no concern about excluding copyrighted material from the training data, so they actually get a good starting point instead of using a dataset consisting of youtube videos and stock footage
I don't think it's that idiosyncratic? Everywhere I've lived:

1. A deceit is an attempt to make somebody believe something false.

2. A falsehood is a statement which is false.

3. A lie is a deceitful falsehood.

Regardless of whether they miscommunicated about hats vs cats, and regardless of whether they were being deceitful in the process, for the statement to be a lie it would also have to be a falsehood, implying they have at least one hat which is not green.

Yes, these problems require suspension of disbelief, especially given the shorthand "he's a liar" always meaning that the person is often deceitful (usually with other negative implications), but the problem statement being that the "person always lies" is pretty clear and doesn't require special pattern recognition or other mental gymnastics, and it's not that different from the suspension of disbelief you invoke when playing a game of chess and not literally sending a knight to murderously dethrone your opposition.
If you don’t have this extra feature, all your safety depends on you. That still is the case.
iOS 18.2 second RC Released
JSON is a human-readable data exchange format. There is a good reason human-readable data exchange formats are so popular - they can be read and understood by humans! So it seems pretty absurd that a format designed to be read by humans doesn't support comments. If you're really concerned about performance or size, you should use a more efficient binary format for data exchange like protobufs, or heck, BSON (binary json)!
I expect this kind of thing is actually how it's going to work longer term, where AI is a copilot to a human artist. The human artist does storyboarding, sketching in backdrops and character poses in keyframes, and then the AI steps in and "paints" the details over top of it, perhaps based on some pre-training about what the characters and settings are so that there's consistency throughout a given work.

The real trick is that the AI needs to be able to participate in iteration cycles, where the human can say "okay this is all mostly good, but I've circled some areas that don't look quite right and described what needs to be different about them." As far as I've played with it, current AIs aren't very good at revisiting their own work— you're basically just tweaking the original inputs and otherwise starting over from scratch each time.
>We’re three decades in with the web and it’s pretty clear that voluntary payment nudges don’t work. Most people won’t pay for free content after they’ve consumed it.

Humans have no problem tipping, the issue is that a single article or piece of content is rarely worth than a fractional amount of whatever your preferred currency is, and it costs more than that amount to transfer that currency. Sponsorships work because you pay normal amounts of currency to receive a larger future block of content.

To make tipping work, you need a system that supports allowing you to deposit normal sums of money but then dole it out in micropayments, and also not charge more than those payments for processing the micropayments. A payment system like Paypal, that maintains an internal balance that is used instead of just transferring from one bank account to another (zelle, venmo), could handle this but I don't think the financial incentives are there for them to bother with it.
Himalaya is not interactive: while Pine is a TUI, this is a CLI.
Very useful to integrate emails in scripts easily.
One thing I've never figured out is, what is the difference between backups and replication? And, does restoring from backups always mean losing more _recent_ data than replication?
With tools like Apple Intelligence and its genmoji (emoji generation) and playground (general diffusion image generation) I expect it to also take on some of the current entertainment and social use-cases of stickers and GIFs.

But that’s probably something you don’t pay for directly, instead paying for e.g. a phone that has those features.
Same goes with DALLE. It was cool to try it the first week or so but now the output is so much worse than Midjourney and stable diffusion. For me it can’t even generate straight lines and everything looks comic-ish.
Nameserver lock in. While I use Cloudflare right now for most things, I value the decoupling. Cheap way to make sure that my services aren't in total control of my domain.
They might not have much of a choice but to shut down all .io domains. This video explains a little: https://www.youtube.com/watch?v=1yJ6AZsUlpc
Exactly.

So far ChatGPT is not for writing books, but is great for SEO-spam blogposts. It is already killing the content marketing industry.

So far Dall-E is not for making master paintings, but it's great for stock images. It might kill most of the clipart and stock image industry.

So far Udio and other song generators are not able to make symphonies, but it's great for quiet background music. It might kill most of the generic royalty-free-music industry.
I'm glad someone else said this. Hopefully we can get rid of that terrible disruptive camera too.
I wonder if it's because of the proximity to the UK, and that it influenced the choice of where to put it.
It's a pretty binary thing in the sense that "bad physics" pretty quickly decoheres into no physics.

I saw one of these models doing a Minecraft like simulation and it looked sort of okay but then water started to end up in impossible places and once it was there it kept spreading and you ended up in some lovecraftian horror dimension. Any useful physics simluation at least needs boundary conditions to hold and these models have no boundary conditions because they have no clear categories of anything.
The Depression of 2026 (2012)
I've used this as a bit of a thought experiment, and also think it may do more harm than good--but a part of me wonders if it may be just thing to create change. A non-profit that works a bit like haveibeenpwned.com but with data sold by data brokers that anyone can look up, with corresponding source attribution. At one point, long ago I was of the idea that all data should be public/exposed or none of it (this ship already sailed with data brokers and such. Don't know how it could be undone).

The problem I keep running into is a real world take on the Trolley problem[0].

Do you publicly publish all data, which:

1. Reduces its sellable value

2. Makes people aware of how much they are being tracked and profiled

3. Gives back a small bit of agency over ones data by knowing where to send delete/remove request to make data brokers honour local laws

However, doing so would also:

1. Give easy access to abuse victim data, putting them in further harm

2. Give actual stalkers an easier path to their targets

3. Other harm that I can not fathom at this point in time

I don't know the answer, maybe mask the address part, or do like Strava and set a blocking geo fence around home/work addresses. For location tracking keep it months behind and remove/mask anything remotely related to health services (mental and physical).

[0] https://en.wikipedia.org/wiki/Trolley_problem
Using Bitwarden on a Samsung device, it is hit or miss. Tried everything possible. If you have some magic to make it work everywhere, let me know.
I would guess there is more than one computer :)

Pixar's stuff famously takes days per frame.
"the worst feature of C is that switches don't break automatically before each case label. This code forms some sort of argument in that debate, but I'm not sure whether it's for or against"

https://www.lysator.liu.se/c/duffs-device.html (the original, better description)

https://en.wikipedia.org/wiki/Duff%27s_device

https://xkcd.com/1053/
Topic Blockers
... not quite. I worked directly with the folks involved on getting more RCA details public. This customer used a single product on GCP, a specific type of VMware hosting, and the "subscription" to that product failed, which turned those resources off. It's more like turning off all their VM's, rather than deleting their entire account, identities, access structures, etc.
This is yet another attempt to posit NISQ results (Noisy Intermediate Scale Quantum) as demonstrations of quantum supremacy. This does not allow us to do useful computational work; it's just making the claim that a bathtub full of water can do fluid dynamic simulations faster than a computer with a bathtub-full-of-water-number-of-cores can do the same computation.

If history is any guide we'll soon see that there are problems with the fidelity (the system they use to verify that the results are "correct") or problems with the difficulty of the underlying problem, as happened with Google's previous attempt to demonstrate quantum supremacy [1].

[1] https://gilkalai.wordpress.com/2024/12/09/the-case-against-g... -- note that although coincidentally published the same day as this announcement, this is talking about Google's previous results, not Willow.
I did quantum computing research in university. We did meaningful work and published meaningful research.

Around 50% of our time was spent working in Overleaf making small improvements to old projects so that we could submit to some new journal or call-for-papers. We were always doing peer review or getting peer reviewed. We were working with a lot of 3rd-party tools (e.g. FPGAs, IBM Q, etc). And our team was constantly churning due to people getting their degrees and leaving, people getting too busy with coursework, and people deciding they just weren't interested anymore.

Compare that to the corporate labs: They have a fully proprietary ecosystem. The people who developed that ecosystem are often the ones doing research on/with it. They aren't taking time off of their ideas to handle peer-review processes. They aren't taking time off to handle unrelated coursework. Their researchers don't graduate and start looking for professor positions at other universities.

It's not surprising in the slightest that the corporate labs do better. They're more focused and better suited for long-term research.
Person of interest in shooting of UNH CEO ID'd as an ex-Ivy League, CS grad
I understand this isn't a bug because the length is checked. But it would seem wise to remove uses of strcpy() so automated tools don't complain.
I'm continually shocked at how well companies can continue on despite serious internal problems. I do think it usually catches up with them though, and there are a lot of "terminally ill" products out there that are going to die eventually. I've seen those projects first hand, and I've seen the slow decay of the product that happens when a company loses control of its tech stack. It can take years.

I think that cuts both ways though, because I think even the business people would be shocked at how well things can seem to work despite serious dysfunction. If an entire team dies off but 3 months later things seem "fine," the business types will just assume everything is as good as it ever was. I know it's popular these days to say that good tech and good business are not always the same thing, but I do think that many of those same businesses would be a little worried if you actually convinced them that their tech was coming off the rails and was only going to get worse. Some businesses just want to be acquired and don't care what happens after that, but not every business thinks they're on a clock.

If you're big enough, like say you're a trillion dollar megacorp, then important teams folding up and being reborn is just part of your ecosystem. I've seen big tech power through churn for years with nothing but human wave tactics until the business climate changed enough that the team in question became less critical, and that's when they let it die.
> Also, what is the relationship, if any, between quantum computing and AI? Are these technologies complementary?

AI is quite good in producing the meaningless drivel needed for quantum computing related press releases.
I could maybe throw into the contest:

- "a gallon". Not purely US thing but almost. It can be anything from 3.8 to 4.4 liters, depending mostly on what are you measuring.

- writing digits. Is it "1", is it "7", is it "i", is it "l"? Why do it to yourself, while their printed fonts are pretty much the same as everywhere else...
Build systems are sort of like type expressions, templates, or constant expressions in a programming language. Either a program compiles or it doesn’t. What might happen when you change the code in some unlikely way isn’t immediately relevant to whether the program works now, so it’s easy to skimp on that kind of checking until things get out of hand due to greater scale.

Also, in Starlark, any runtime check you write is a build-time check and calling fail reports a build-time error, which is good enough for users, but not for understanding how to use Starlark functions within Starlark code.
Does access to arbitrary parallel universes imply that they divide up the computation and the correct answer is distributed to all of the universes or in such a collection, there will be sucker universes which will always receive wrong answers ?
a somewhat counterintuitive argument is this: AI models will make the overall creative landscape more diverse and interesting, ie, less "average"!

Imagine the space of ideas as a circle, with stuff in the middle being more easy to reach (the "cliched average"). Previously, traversing the circle was incredibly hard - we had to use tools like DeviantArt, Instragram, etc to agglomerate the diverse tastes of artists, hoping to find or create the style we're looking for. Creating the same art style is hiring the artist. As a result, on average, what you see is the result of huge amounts of human curation, effort, and branding teams.

Now reduce the effort 1000x, and all of a sudden, it's incredibly easy to reach the edge of the circle (or closer to it). Sure, we might still miss some things at the very outer edge, but it's equivalent to building roads. Motorists appear, people with no time to sit down and spend 10000 hours to learn and master a particular style can simply remix art and create things wildly beyond their manual capabilities. As a result, the amount of content in the infosphere skyrockets, the tastemaking velocity accelerates, and you end up with a more interesting infosphere than you're used to.
Trellis – 3D mesh generative model
> the idea is very obvious but they haven't done it because they don't have the wherewithal to do so

Fascinating! I wish I had the knowledge and wherewithal to do that and become rich instead of wasting my time on HN.
Rustdoc search; searching functions by type signature
Advent of Code: Discussion on LLM Cheaters
This is the conundrum of AI generated art. It will lower the barrier to entry for new artists to produce audiovisual content, but it will not lower the amount of effort required to make good art. If anything it will increase the effort, as it has to be excellent in order to get past the slop of base level drudge that is bound to fill up every single distribution channel.
Saw this submitted a few days ago [0], but it's a very impressive demo and would like to see it get discussed here.

[0] https://news.ycombinator.com/item?id=42342557
This, people want micropayments but the market won't figure out a way to provide a service for that.
> I think the pursuit of fidelity has made the models less creative over time (...) their output is ever more homogenized and interchangable.

Ironically, we're long past that point with human creators, at least when it comes to movies and games.

Take sci-fi movies, compare modern ones to the ones from the tail end of the 20th century. Year by year, VFX gets more and more detailed (and expensive) - more and better lights, finer details on every material, more stuff moving and emitting lights, etc. But all that effort arguably killed immersion and believability, by making scenes incomprehensible. There's way too much visual noise in action scenes in particular - bullets and lighting bolts zip around, and all that detail just blurs together. Contrast the 20th century productions - textures weren't as refined, but you could at least tell who's shooting who and when.

Or take video games, where all that graphics works makes everything look the same. Especially games that go for realistic style, they're all homogenous these days, and it's all cheap plastic.

(Seriously, what the fuck went wrong here? All that talk, and research, and work into "physically based rendering", yet in the end, all PBR materials end up looking like painted plastic. Raytracing seems to help a bit when it comes to liquids, but it still can't seem to make metals look like metals and not Fischer-Price toys repainted to gray.)

So I guess in this way, more precision just makes the audience give up entirely.

> they will know that it looks like every other AI generated cyberpunk cityscape and mentally file your creation in the slop folder.

The answer here is the same as with human-produced slop: don't. People are good at spotting patterns, so keep adding those low-order bits until it's no longer obvious you're doing the same thing everyone else is.

EDIT: Also, obligatory reminder that generative models don't give you average of training data with some noise mixed up; they sample from learned distribution. Law of large numbers apply, but it just means that to get more creative output, you need to bias the sampling.
DIY Imaging Spectrophotometer (2009)
The past few years' innovation in AI has roughly been split into two camps for me.

LLMs -- Awesome and useful. Disruptive, and somewhat dangerous, but probably more good than harm if we do it right.

'Generative art' (i.e. music generation, image generation, video generation) -- Why? Just why?

The 'art' is always good enough to trick most humans at a glance but clearly fake, plastic, and soulless when you look a bit closer. It has instilled somewhat of a paranoia in me when browsing images and genuinely worsened my experience consuming art on the internet overall. I've just recently found out that a jazz mix I found on YouTube and thought was pretty neat is fully AI generated, and the same happens when I browse niche artstyles on Instagram. Don't get me started on what this Sora release will do...

It changed my relationship consuming art online in general. When I see something that looks cool on the surface, my reaction is adversarial, one of suspicion. If it's recent, I default to assuming the piece is AI, and most of the time I don't have time or effort to sleuth the creator down and check. It's only been like a year, and it's already exhausting.

No one asked for AI art. I don't understand why corporations keep pushing it so much.
Show HN: TypeQuery – SQL query builder library built with TypeScript
Description:

I’ve recently built a TypeScript SQL query builder library called TypeQuery and I’m looking for feedback from the community. TypeQuery is designed to help TypeScript developers construct SQL queries in a type-safe and intuitive way, with support for common SQL operations like SELECT, INSERT, UPDATE, and DELETEI’ve recently built a TypeScript SQL query builder library called TypeQuery and I’m looking for feedback from the community. TypeQuery is designed to help TypeScript developers construct SQL queries in a type-safe and intuitive way, with support for common SQL operations like SELECT, INSERT, UPDATE, and DELETE.

Key Features of TypeQuery:

- Type-safe SQL Queries: Build SQL queries with TypeScript’s powerful type system.

- Supports Common SQL Operations: SELECT, INSERT, UPDATE, and DELETE.

- Dynamic WHERE Clauses: Easily create complex WHERE clauses with a Django-like approach.

- SQL Parameterization: Avoid SQL injection risks with parameterized queries.

- Works with Multiple Databases: Compatible with MySQL, PostgreSQL, and SQLite.

I would love to hear your thoughts on:

- Usability: Does the API feel intuitive to use? Are there any features you think could make it easier to work with?

- Performance: How does the performance compare to other query builders or raw SQL?

- Missing Features: What do you feel might be missing in this library? Are there any features you'd expect from a SQL query builder?

- General Opinion: Would you find a library like this useful for your projects?

You can check it out on GitHub: https://github.com/grandimam/typequery. If you like this project, please give it a to show your support and help others find it!

Looking forward to hearing your thoughts, suggestions, and feedback!
The great tweezer takeover: Is fine dining hijacking the soul of San Francisco?
I wrote about a country or worldwide like system before that would do automatic funding to visited webpages:

https://news.ycombinator.com/item?id=40972106

Would be nice for the EU to implement such a feature.
Does "when pigs fly" imply that some day pigs will be able to fly? No; people can understand impossibility when it is used rhetorically in every day speech.

For example I might say, "all the honest politicians are doing a great job", which conveys my actual meaning, "all politicians are dishonest".
The competition is ready to trash the announcement because the 10-month delay gave rise to several viable competitors, and that would still be the case if OpenAI never did the preannouncement. If OpenAI released Sora 10 months ago, there wouldn't be as much cynicism.
I suppose because it is film it is okay that some of these are fictional? "Casablanca", "Life is Beautiful" ... these aren't factual so I'm struggling a bit to see the narrative or purpose of the project.

But that's just me and I in no way want to come across as slighting the work you created. I enjoyed watching the trailer and just seeing if I could guess the film, ha ha.

I assumed initially the project was to put in a chronology actual war (stock) footage — or newsreels, etc.

Then I thought the project would cover films actually made during WWII itself (to perhaps get a sense of the changing sentiment of the times).

(I guess I jump to too many conclusions too fast.)
Most VFX productions take over 2 CPU hours a frame for final video, and have for a very long time. It takes shorter then a month since this gets parallelized on large render farms.
It’s like how there are two types of movie directors (or creative directors in general), the dictatorial “100 takes until I get it exactly how I envision it” type, and the “I hired you to act, so you bring the character to life for me and what will be will be” type

Right now AI is more the latter, but many people want it to be the former
It also happpens when ordering stuff online depending on the level of security used by the online shop.

If your phone die, you replace it or do your operations at the ATM or at the counter during opening hours.
>Honestly this is one of the main usecases of crypto, in my opinion.

The problem is that crypto doesn't provide anything that non-crypto shouldn't wouldn't also provide in a cheaper and easier to implement way.
Fuji Apple study finds genetic mechanisms behind high-yield trees
Learn all about boring tools that make $$
I didn't scroll far enough to see those but that's exactly what I'd expect. Minute "improvements" that are really just statistical noise from a dyno due to tiny variations even if the test(s) were genuine.