MurphyA/hacker-news huggingface dataset:
by Etheryte | 2025-08-05 16:07:43+00:00
> I don't think there was ever a period of time where creative works were just created for sake of itself rather for sharing it with others at masse.

Numerous famous writers, painters, artists, etc counter this idea, Kafka being a notable example, whose significant works only came to light after his passing and against his will. This doesn't take away from the rest of your discussion point, but art always has and always will also exist solely for its own sake.
by flenserboy | 2025-08-05 16:07:54+00:00
the tipping point came when boolean searches no longer worked. from that point on search results on Google have been what they want you to receive, not what you ask for. (it could also be argued that the real end of Google came when the long tail disappeared from results — that is where the real gold could be found, if one was patient, especially when it came to exact searches — & giving useful results could not be tolerated, whether due to restricting access to certain sites or info, or because more ads needed to be put in front of the eyes of users.)
by wolttam | 2025-08-05 16:07:58+00:00
It's hard to see how there will be room for profit as this all advances

There will be two classes of media:

- Generated, consumed en-masse by uncreative, uninspired individuals looking for cheap thrill

- Human created, consumed by discerning individuals seeking out real human talent and expression. Valuing it based merely on the knowledge that a biological brain produced (or helped produce) it.

I tend to suspect that the latter will grow in value, not diminish, as time progresses
by kasey_junk | 2025-08-05 16:08:19+00:00
Every. Single. Time. You say you get productivity gains from ai tools on the internet someone will tell you that you weren’t good at your job before the ai tooling.

Perhaps, start from the assumption that I have in fact spent a fair bit of time doing this job at a high level. Where does that mental exercise take you with regard to your own position on ai tools.

In fact, you don’t have to assume I’m qualified to speak on the subject. Your retort assumes that _everyone_ who gets improvement is bad at this. Assume any random proponent isn’t.
by cleak | 2025-08-05 16:08:29+00:00
I’ve found I do get small bursts of 10x productivity when trying to prototype an idea - much of the research on frameworks and such just goes away. Of course that’s usually followed by struggling to make a seemingly small change for an hour or two. It seems like the 10x number is just classic engineers underestimating tasks - making estimates based on peak productivity that never materializes.

I have found for myself it helps motivate me, resulting in net productivity gain from that alone. Even when it generates bad ideas, it can get me out of a rut and give me a bias towards action. It also keeps me from procrastinating on icky legacy codebases.
by addisonj | 2025-08-05 16:08:55+00:00
Really impressive... but wow this is light on details.

While I don't fully align with the sentiment of other commenters that this is meaningless unless you can go hands on... it is crazy to think of how different this announcement is than a few years ago when this would be accompanied by an actual paper that shared the research.

Instead... we get this thing that has a few aspects of a paper - authors, demos, a bibtex citation(!) - but none of the actual research shared.

I was discussing with a friend that my biggest concern with AI right now is not that it isn't capable of doing things... but that we switched from research/academic mode to full value extraction so fast that we are way out over our skis in terms of what is being promised, which, in the realm of exciting new field of academic research is pretty low-stakes all things considered... to being terrifying when we bet policy and economics on it.

To be clear, I am not against commercialization, but the dissonance of this product announcement made to look like research written in this way at the same time that one of the preeminent mathematicians writing about how our shift in funding of real academic research is having real, serious impact is... uh... not confidence inspiring for the long term.
by cogman10 | 2025-08-05 16:08:56+00:00
The best positive take I could make for the Trump policy is that the negative consequences of it are somewhat too late.

Solar, wind, and battery, even without the grants, is already quite cheap. You'd have to fine the industries to really slow down deployment at this point.

That means the 7B is mostly saved money with not too much negative impacts.

(I still think it's a bad idea, don't get me wrong, but it's probably not the worst thing in the world).
by quantumHazer | 2025-08-05 16:09:01+00:00
Machine learning as it is needs human data and input to progress further.

Synthetic data can be useful until a certain point, but you can’t expect to have a better model on synthetic data alone indefinitely.

The moat of GDM here is YouTube. That have a bazillion of gameplay and whatever videos. But here it is.

The downside I can see is that most people will stop to publish content online for free since this companies have absolutely no respect whatsoever for the humans that created the data they use.
by gbalduzzi | 2025-08-05 16:09:02+00:00
Because it would be more predictable, easier to memorize, less verbose, easier to use for developers coming from other modern languages and more comfortable to work with.

The fact that they are chaotic since 30 years ago is not a valid reason for keeping them chaotic right now.

Also, I'm not even arguing they should change the existing functions, that would break all existing code for almost no reason.

I think they should "simply" support methods on primitives, and implement the main ones in a chainable way:

"test string"->trim()->upper()->limit(100);

[0,1,2]->filter(fn ($n) => $n % 2 === 0)->map(fn($n) => $n * 2);

I would love this so much
by galleywest200 | 2025-08-05 16:09:19+00:00
I was an early adopter of Kagi, and paid for about two years. But somehow I missed that they give money to Yandex, and I felt too guilty continuing to give them money because I want to support the people of Ukraine. So I cancelled recently.

If Kagi stopped using Yandex, or somehow allowed accounts to configure a way so that their funds do not go to Yandex (unsure if this is even possible to split accounts this way) then I would sign back up immediately.
by ltbarcly3 | 2025-08-05 16:09:30+00:00
During the early neolithic, when people were digging holes with sticks or their bare hands to plant seeds, the annual risk of starvation is estimated to be 5% to 15% in a bad year.

Periodic famine every 5-10 years would kill 20% or more of the population. 40% to 60% of skeletons from that period show chronic malnutrition. Humanity survived because when a region would starve out it would be back-filled by populations from neighboring regions. Despite women having a baby every other year on average, populations only increased very slowly and inconsistently.

Could you explain how it's better?
by yomismoaqui | 2025-08-05 16:09:54+00:00
The question is, why are you doing art?

- Because you enjoy it

- Because you get pats in the back from people you share it with

- Because you want to earn money from it

The 1st one will continue to be true in this dystopian AI art future, the other not so much.

And sincerely I find that kind of human art, the one that comes from a pure inner force, the more interesting one.

EDIT: list formatting
Show HN: EACL – ReBAC Authorization in Clojure and Datomic Based on SpiceDB
by pgt | 2025-08-05 16:09:59+00:00
Hi HN, Petrus here from Cape Town, South Africa. I spent the better half of 2024 integrating our Clojure-based cloud provisioning platform at CloudAfrica[^1] with SpiceDB[^2], a scalable ReBAC based authorization system based on Zanzibar, Google's globally-consistent authorization system.

The problem with any external authorization system is modelling & synchronization: SpiceDB has a text-based schema DSL to define Relations & Permissions and models Relationships as 3-tuples of `[subject relation resource]`.

However, in Datomic we have attributes like `:account/owner`, `:server/account`, `:vpc/account` and `:vpc/viewers`. Figuring out which Relationships to create or delete in SpiceDB when anything changes becomes a diffing + batched sync problem, especially when you have an impedance mismatch between your data model and your permission schema.

I realized that if we modelled our AuthZ Relationships directly in Datomic, all our syncing problems would go away: you can simply tail the Datomic transactor queue and instantly create or delete Spice relationships in real-time. But once you do that, you might as well model the permission schema as well, and if you go one step further, you can implement the permission graph traversal directly in Clojure to avoid a network hop. And that is exactly what EACL is.

EACL (Enterprise Access ControL) is an embedded authorization library that lives next to your data in Datomic and avoids an external network hop. EACL is suitable for small-to-medium scale, while giving you the option to migrate to SpiceDB in future when you need more scale and consistency semantics.

EACL implements the SpiceDB gRPC API as an idiomatic Clojure protocol (`IAuthorization`), using Datomic as a backing graph store. So you can add sophisticated authorization to your Clojure project on day one and migrate to SpiceDB later.

Internally, EACL recursively traverses the permission graph using direct index-based calls (via `datomic.api/index-range`) to efficiently answer `CheckPermission`, `LookupSubjects` and `LookupResources` queries.

The goal for EACL is to provide best-in-class authorization for Clojure & Datomic applications with <10M entities. It is especially suited to Electric Clojure[^3]. EACL has been open-sourced under the AGPL, but we are likely to relicense it under a more permissive licence. EACL is used at CloudAfrica, a regional cloud host based in South Africa.

[^1]: https://cloudafrica.net

[^2]: https://authzed.com/spicedb

[^3]: https://electric.hyperfiddle.net/
by stillpointlab | 2025-08-05 16:10:13+00:00
I'm usually the one defending AIs in the comments, but this article hits home for me. I find myself zoning out when I read long tracts written by AI. I absolutely hate filler and many LLMs just fill up space with text.

In my own usage, that has meant that even when I use LLMs to help with prose, I write the text and use the LLM to review and provide feedback. In some cases I will copy a sentence if the LLM version is better but generally I just ask for opinions. I explicitly request the AI _not_ to write. When it re-writes entire paragraphs of my prose I actually experience a deep cringe feeling.
by hajile | 2025-08-05 16:10:26+00:00
Chaining requires creating a class and ensuring everything sticks to the class and returns it properly so the chain doesn't blow up. As you add more options and do more stuff, this becomes increasingly hard to write and maintain.

If I'm using a chained library and need another method, I have to understand the underlying data model (a leaky abstraction) and also must have some hack-ish way of extending the model. As I'm not the maintainer, I'm probably going to cause subtle breakages along the way.

Pipe operators have none of these issues. They are obvious. They don't need to track state past the previous operator (which also makes debugging easier). If they need to be extended, look at your response value and add the appropriate function.

Composition (whether with the pipe operator or not) is vastly superior to chaining.
by hnuser123456 | 2025-08-05 16:10:30+00:00
I think there are three factors to this: 1. What to code (longer, more specific prompts are better but take longer to write), and 2. How to code it (specify languages, libraries, APIs, etc.) And if you're trying to write code that uses a newer version of a library that works differently from what's most commonly documented, it's a long uphill battle of constantly reminding the LLM of the new changes.

If you're not specific enough, it will definitely spit out a half-baked pseudocode file where it expects you to fill in the rest. If you don't specify certain libraries, it'll use whatever is featured in the most blogspam. And if you're in an ecosystem that isn't publicly well-documented, it's near useless.
by flyinglizard | 2025-08-05 16:11:04+00:00
I share your feelings. Also couple that with a populist and cynical political climate that can’t create effective regulations even if it wanted, and that by its very appetite for scale AI thrives at the hands of the few that can feed it and you get something quite bleak.

My only hope is that we could have created 100k nukes of monstrous yields but collectively decided not to. We instead created 10k smaller ones. We could have destroyed ourselves long ago but managed to avoid it.
by garciasn | 2025-08-05 16:11:07+00:00
I had it priced out by 5 different vendors. Only one of those 5 was in any way truthful about the reality for my particular home: "you will likely only get 15% of what others with panels might due to the shape of your roof and tree cover now and especially in 10 years." That said, WITH grid-kickbacks (all of which are not at all guaranteed), according to 4 of them, I would be looking at a net zero cost in 30-36 years.

I'm not even talking about the fact that panels MAY act like a pool for resale. Some people DO want them--again depending on your locale--most, at this point, do NOT where I live.

I was looking primarily for cost reduction and a very small percentage of saving the environment or whatever you want to call it. But; depending on your locale, home structure, etc, solar may not at all be that. If you're leaning more on the side of energy independence and eco-friendliness, maybe it's a better fit for you.
by zbrw | 2025-08-05 16:11:15+00:00
I believe that the corpus of video data to train on with video far exceeds that of 3D data. It's also much cheaper to produce video data. So I'd expect that this is probably the quickest way forward from a current world state perspective.

Additionally, video seems like a pretty forward output shape to me - 2D image with a time component. If we were talking 3D assets and animations I wouldn't even know where to start with modeling that as input data for training. That seems really hard to model as a fixed input size problem to me.

If there was comparable 3D data available for training, I'd guess that we'd see different issues with different approaches.

A couple of examples that I could think of quickly: Using these to build games, might be easier if we could interact with the underlying "assets". Getting photorealistic results with intricate detail (e.g. hair, vegetation) might be easier with video based solutions.
by brettgriffin | 2025-08-05 16:11:25+00:00
I was making $35/hr (~$70k) a year as a department manager in a grocery store while I still in college. And that was 20 years ago.

I just helped a friend's wife (late 20s) apply and interview for a general administration position in a tier 3 city. She didn't study anything important and sort of just floundered for a few years after college doing various temp jobs. She ended up getting the job makes about $90k now. They thought I did some sort of a magic trick or something.

That seems great until you realize that fast food and grocery stores are paying new hires $22-27/hr (so 45-54k). Again, if you have any degree of professionalism and work ethic you can move into some low ranking managerial position that is closer to $30-45/hr.

In short, $75k-100 is not a lot of money anymore and is pretty easy to get. It requires ignoring this learned helplessness and working hard.
Ask HN: What's your biggest success–or failure–using AI?
by zyruh | 2025-08-05 16:11:43+00:00
Curious what real-world wins (or faceplants) people have had using AI tools.

Have you used AI for…

Trading crypto or stocks?

Making big business or product decisions?

Automating workflows or building side projects?

Generating content that went viral (or flopped)?

Anything else that changed your trajectory—for better or worse?

Would love to hear stories, tools you used, lessons learned, and whether you'd do it again.
by mft_ | 2025-08-05 16:12:14+00:00
I recently dumped Google on my phone because of the awful dark pattern they'd put in with a popup trying to generate an installation of the Google search app.

While I'm usually good at avoiding such things, this one somehow worked on me and was insanly frustrating: you click the wrong button, the App Store pops up, you switch back to the web page, go back, and then (via a redirect) the same thing happens again. (Whoever implemented that deserves punishment.)

Anyway, between this and also that for many a technical topic Google search results are just so full of nonsense sites that asking the question of an LLM is actually the rational approach despite the risks of hallucinations, maybe it's time to give Kagi a go...
Ask HN: Most Important Set of Books?
by max_ | 2025-08-05 16:12:18+00:00
There two books I constantly consult thought the year.

1. Incerto by Nassim Taleb

2. Pocket Oracle by Baltasar Gracion

1) Is very popular (though not throughly read)

2) Is not known much. But I would say its the most important book todo with "association with humans"

My view of the world changed completely by reading these books.

But I came across them completely by mistake.

Maybe other people have books they feel the same about?

Please share some titles.
by sureglymop | 2025-08-05 16:12:19+00:00
I do also believe that those who are often looked at or referred to as 10x engineers will maybe only see a marginal productivity increase.

The smartest programmer I know is so impressive mainly for two reasons: first, he seems to have just an otherworldly memory and seems to kind of have absolutely every little feature and detail of the programming languages he uses memorized. Second, his real power is really in cognitive ability, or the ability to always quickly and creatively come up with the smartest and most efficient yet elegant and clean solution to any given problem. Of course somewhat opinionated but in a good way. Funnily he often wouldn't know the academic/common name for some algorithm he arrived at but it just happened to be what made sense to him and he arrived at it independently. Like a talented musician with perfect pitch who can't read notation or doesn't know theory yet is 10x more talented than someone who has studied it all.

When I pair program with him, it's evident that the current iteration of AI tools is not as quick or as sharp. You could arrive at similar solutions but you would have to iterate for a very long time. It would actually slow that person down significantly.

However, there is such a big spectrum of ability in this field that I could actually see this increasing for example my productivity by 10x. My background/profession is not in software engineering but when I do it in my free time the perfectionist tendencies make me work very slowly. So for me these AI tools are actually cool for generating the first crappy proof of concepts for my side projects/ideas, just to get something working quickly.
by 0cf8612b2e1e | 2025-08-05 16:12:38+00:00
How big are all of the design files for a modern chip? 10MB? 100gig? Billions of transistors, but surely a lot of compressible redundancy.

Not that I think you can just plug in a thumb drive and download as you please, but just a sense of scale on how much data describes the design.
by thephyber | 2025-08-05 16:12:39+00:00
The important things to remember about these claims/articles is that LLMs are useful for a wide variety of tasks. An engineer doesn’t only code, but also has to learn, search, gather / define requirements, write tests, troubleshoot, read/review other people's code, deal with project management tools, document (both for developers and for customers).

Also, one underestimated aspect is that LLMs don’t get writer’s block or get tired (so long as you can pay to keep the tokens flowing).

Also, one of the more useful benefits of coding with LLMs is that you are explicitly defining the requirements/specs in English before coding. This effectively means LLM-first code is likely written via Behavior Driven Development, so it is easier to review, troubleshoot, upgrade. This leads to lower total cost of ownership compared to code which is just cowboyed/YOLOed into existence.
by bigyabai | 2025-08-05 16:12:40+00:00
Absolutely not. They are trying to protect you, can you imagine how awful Safari would be if it let you sideload such nasty extensions? It would be just like Chrome, absolutely no market variation to speak of! Despicable.

It's really AdGuard's fault for failing to fit their functionality within the arbitrary constraints Apple decided was suitable for a runtime.
by tracker1 | 2025-08-05 16:12:44+00:00
It's kind of wild that PHP gets a pipe(line) operator before JS finalizes its' version... of course they've had multiple competing proposals, of which I liked the F# inspired one the most... I've stopped relying on the likes of Babel (too much bloat) or I'd like to be able to use it. I used it for years for async functions and module syntax before they were implemented in node and browsers. Now it's hard to justify.
by mvieira38 | 2025-08-05 16:12:53+00:00
Kagi is still a good solution for other use cases. For the privacy-oriented you have Privacy Pass, for the familymen you get very good parental control options, for college and gradschool you get the Academic Lens and the very underrated feature of "lensing" the AI assistant.

The Translator outperforms Google Translate and DeepL in my experience, too, and provides very nice context and such for translations. Kagi Maps might become the premiere OpenStreetMap interface, too, with amazing integration of other resources. Just one anecdote about it: I just checked in Kagi Maps a local restaurant I added a few weeks ago in OpenStreetMap, and on Kagi they somehow have some interior photos that circulated in a local magazine. Amazing stuff
by glenstein | 2025-08-05 16:13:01+00:00
>It's basically what every major AI lab head is saying from the start.

I suppose it depends what you count as "the start". The idea of AI as a real research project has been around since at least the 1950s. And I'm not a programmer or computer scientist, but I'm a philosophy nerd and I know debates about what computers can or can't do started around then. One side of the debate was that it awaited new conceptual and architectural breakthroughs.

I also think you can look at, say, Ted Talks on the topic, with guys like Jeff Hawkins presenting the problem as one of searching for conceptual breakthroughs, and I think similar ideas of such a search have been at the center of Douglas Hofstadter's career.

I think in all those cases, they would have treated "more is different" like an absence of nuance, because there was supposed to be a puzzle to solve (and in a sense there is, and there has been, in terms of vector space and back propagation and so on, but it wasn't necessarily clear that physics could "pop out" emergently from such a foundation).
by getnormality | 2025-08-05 16:13:05+00:00
It seems implied here that the main things that happen online are unconstructive praise and constructive or unconstructive criticism? So I wonder what's going on with the fourth possibility, constructive feedback that is neutral-to-positive. Why does that box go unmentioned? Are we somehow especially bad at, or uninterested in, doing stuff that fits in this category?