MurphyA/hacker-news huggingface dataset:
by metalman | 2025-06-07 10:10:46+00:00
modify, not "improve", perhaps speciate, perhaps become like insects with physicly different specialised castes, perhaps fail to understand the complecity and end up unable to outperform natural selection, but most definitly the game is on and the game is open ended, and the technology is fairly strait forward ,useing tools that do not require the ulra high precision and refinement of elecro/mechanical technologys, so containment and control of bio/genetic technology will be impossible. the main constraint on unhinged out of control developments is that iterating will remain slow, and require carefull planing and methodical implimentation over generational time spans, but costs will be low, and any sufficiently motivated small group/company/country can join the"fun", which is another point, this will be more "art" that science, as there is no equivalent to other types of engineering, so cloning will be atractive, but will present serious problems if large numbers of cloned indivuals are attempted. I leave out ethics and morality and refer anyone to the blank stare response that will be given by the types of people who are, right now, giving, while they go about realising there ideal society.
by matu3ba | 2025-06-07 10:10:46+00:00
> LLVM will improve to the point that we don't need further annotation to enable positive optimization transformations

That is quite a long way to go, since the following formal specs/models are missing to make LLVM + user config possible:

- hardware semantics, specifically around timing behavior and (if used) weak memory

- memory synchronization semantics for weak memory systems with ideas from “Relaxed Memory Concurrency Re-executed” and suggested model looking promising

- SIMD with specifically floating point NaN propagation

- pointer semantics, specifically in object code (initialization), se- and deserialization, construction, optimizations on pointers with arithmetic, tagging

- constant time code semantics, for example how to ensure data stays in L1, L2 cache and operations have constant time

- ABI semantics, since specifications are not formal

LLVM is also still struggling with full restrict support due to architecture decisions and C++ (now worked on since more than 5 years).

> At that point though, is there really a purpose to using a low level language?

Languages simplify/encode formal semantics of the (software) system (and system interaction), so the question is if the standalone language with tooling is better than state of art and for what use cases. On the tooling part with incremental compilation I definitely would say yes, because it provides a lot of vertical integration to simplify development.

The other long-term/research question is if and what code synthesis and formal method interaction for verification, debugging etc would look like for (what class of) hardware+software systems in the future.
by diggan | 2025-06-07 10:14:47+00:00
> That requires that people care enough to collect that material in order to have it transported to the facility that can degrade it. The amount of plastic in the environment indicates that this is clearly not the case.

Or that governments care enough to create laws and incentives for people to collect it.

Besides, there are many places that don't have as much plastic as others in their environment, so clearly it's possible to avoid in some way. Have to figure out how and why, but I'm guessing the researchers kind of feel like that's outside the scope of their research.
by cdrini | 2025-06-07 10:15:11+00:00
When I use a normal LLM, I generally try to think "would I be able to do this without thinking, if I had all the knowledge, but just had to start typing and go?".

With thinking LLMs, they can think, but they often can only think in one big batch before starting to "speak" their true answer. I think that needs to be rectified so they can switch between the two. In my previous framework, I would say "would I be able to solve this if had all the knowledge, but could only think then start typing?".

I think for larger problems, the answer to this is no. I would need paper/a whiteboard. That's what would let me think, write, output, iterate, draft, iterate. And I think that's where agentic AI seems to be heading.
by alkonaut | 2025-06-07 10:15:42+00:00
Yes if it can be deanonymized then it’s not anonymous. Almost a tautology that.

You can’t send the telemetry over http without revealing an ip, but obviously that ip can’t be stored as part of the telemetry data. That’s PII and not anonymous at all.

Important: if I collect anonymous telemetry you better trust me that it’s anonymous when I say it is. Because if you don’t trust me on that then you can’t run the software at all (if it’s a piece of software that relies on web requests in some form at least). Otherwise why would you even trust that my opt in is respected? You have to trust software vendors of software that makes http requests. It’s as simple as that. You can use open source or try to inspect packets. Or firewall the software. But if it does (for example) one update check on startup which is common, then it’s almost impossible to tell whether it contains telemetry data. Because even the bare minimum request “this is FooApp 2.9.1 are there any updates” contains important usage stats: it’s +1 for the use counter and +1 for the v2.9 use counter!
by diggan | 2025-06-07 10:16:52+00:00
> Over here in Latvia they established a deposit system where drinks cost more to buy at the store but you get that money back (store credit, or you can just donate it) when you bring the bottles/cans to a drop off point.

AKA "Container-deposit legislation" (or "Pant" as we call it in Sweden and maybe also Germany?). Seems to work very well, and you also have a ton of people collecting cans that others throw in the environment, as they'll get money for it.

Kind of wish we had it here in Spain too, as the environment and the sea ends up with a lot of cans and glass bottles. Seems like such an obvious idea to have nationwide.
by orwin | 2025-06-07 10:17:51+00:00
When you see a few satochi being actually exchanged, on chain, do you think 'he bought groceries' or do you think 'he paid his yearly Onlyfan access/his ketamine stash'? In my experience, it's the second (I'm not in the US so it might be different). Not that I assign any moral superiority to either actions, but BTC itself as a payment system and not store of value isn't usually used for groceries, can we agree on that?
by DonHopkins | 2025-06-07 10:19:08+00:00
Deep Mind (Google’s reinforcement learning lab), Deep Seek (Alibaba’s LLM initiative), Deep Crack (EFF’s DES cracker), Deep Blue (IBM’s chess computer), and Deep Thought (Douglas Adams’ universal brain) all set the stage...

So naturally, this thing should be called Deep Spike, Deep Spin, Deep Discount, or -- given its storage-free design -- Deep Void.

If it can accelerated nested 2D FORTRAN loops, you could even call it Deep DO DO, and the next deeper version would naturally be called Deep #2.

JD Vance and Peter Thiel could gang up, think long and hard hard, go all in, and totally get behind vigorously pushing and fully funding a sexy supercomputer with more comfortably upholstered, luxuriously lubricated, passively penetrable cushioned seating than even a Cray-1, called Deep Couch. And the inevitable jealous break-up would be more fun to watch than the Musk-Trump Bromance!
by alkonaut | 2025-06-07 10:22:49+00:00
The shitty thing isn’t phoning home, the shitty thing is doing what’s not described on the label.

If a piece of software says “this will do X if you run it” and then it does X then I don’t see the complaint (yes I realize lots of software uses dark patterns or doesn’t say what it does, especially windows, but _in principle_ I don’t think anonymous telemetry with good clear opt out/in is evil).
by madeofpalk | 2025-06-07 10:23:55+00:00
> more and likely reports a lot of this back to ms

Isn’t this the literal definition of FUD? Fear, Uncertainty, and Doubt.

I would like to hope the orange site approaches this topic with more substance. Do the analysis of network traffic to see what gets sent home. Decompile the binary to check it out for these sorts of things. Don’t just write your anti-MS fanfic and pretend that it’s something meaningful.

People and object detection are pretty baseline features for a photo management app these days IMHO. I like that my photos app automatically finds all the photos of my dog.
by Jensson | 2025-06-07 10:24:13+00:00
> if the desired result is achieved (movement through water) it doesn't matter if it happens in a different way than we are used to

But the point is that the desired result isn't achieved, we still need humans to think.

So we still need a word for what humans do that is different from what LLM does. If you are saying there is no difference then how do you explain the vast difference in capability between humans and LLM models?

Submarines and swimming is a great metaphor for this, since Submarines clearly doesn't swim and thus have very different abilities in water, its way better in some ways but way worse in other ways. So using that metaphor its clear that LLM "thinking" cannot be described with the same words as human thinking since its so different.
by sargstuff | 2025-06-07 10:27:24+00:00
Non-physics Much Ado about Shrodinger's Cat. Just tool(s) for quickly building higher order associations/abstractions from 'base term information'.[1][2][3]. aka dynamically generate a unique catlan number(s) for given Tromp lambda calcui as way of reducing tree height/lisp parentheses down to a single pair while dynamically computing/recomputing the determinant (appropriate base / number symbols ratio) to minimize length between parentheses.

----------------

[1] : I told AI to make me a protein. Here's what it came up with : https://www.nature.com/articles/d41586-025-01586-y

[2] : AI Models for Protein Structure Prediction : https://frontlinegenomics.com/ai-models-for-protein-structur...

[3] : AI model deciphers the code in proteins that tells them where to go : https://news.mit.edu/2025/ai-model-deciphers-code-proteins-t...
by bombcar | 2025-06-07 10:29:12+00:00
> And the solution is almost always “model, make, and serial number.”

If you've ever spent time in old car forums, you learn that even this isn't enough because of production-line sloppiness.

Serial number re-use is rare, but it happens. Usually because a product had something detected that resulted in remanufacturing, but sometimes other things slip.
by Xmd5a | 2025-06-07 10:29:48+00:00
>Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents

>In this paper, we introduce a novel framework that addresses these challenges by training a smaller, specialized student RL agent using instructions from an LLM-based teacher agent. By incorporating the guidance from the teacher agent, the student agent can distill the prior knowledge of the LLM into its own model. Consequently, the student agent can be trained with significantly less data. Moreover, through further training with environment feedback, the student agent surpasses the capabilities of its teacher for completing the target task.

https://arxiv.org/abs/2311.13373
by ant6n | 2025-06-07 10:31:00+00:00
I don’t get the big hoopla. Here in Germany I’m opting to turn development costs into assets (I simplify a bit). I need assets on the balance sheet, otherwise we’re over-indebted. As long as the development costs are much higher than income (I.e. as long as you’re not profitable), then it shouldn’t matter. And once you are profitable, you pay some more corporate taxes, but aren’t they kind of not too high in the us anyway?
by Jensson | 2025-06-07 10:31:06+00:00
We have had programs that can give good answers to some hard questions for a very long time now. Watson won jeapordy already 2011, but it still wasn't very good at replacing humans.

So that isn't a good way to judge intelligence, computers are so fast and have so much data that you can make programs to answer just about anything pretty well, LLM is able to do that but more automatic. But it still doesn't automate the logical parts yet, just the lookup of knowledge, we don't know how to train large logic models, just large language models.
by vindarel | 2025-06-07 10:31:40+00:00
> good dep management

you can see https://github.com/ocicl/ocicl and https://github.com/fosskers/vend (newer)

> sane beginner defaults

agree. CLISP's REPL is beginner friendly, but we shall not advise it today. I'm doing this: https://github.com/ciel-lang/CIEL/ Common Lisp with batteries included. The terminal REPL has completion and syntax highlighting. You can run scripts easily. You get many common libraries. (beta)
by skrebbel | 2025-06-07 10:33:06+00:00
English is not everybody's first language. And, fwiw, I like to think that I'm as fluent in English as I am in my native language but still the article threw me off too (even while I otherwise liked it).

Notably, it doesn't start with something like "I'm from Belo Horizonte, Brazil, and worked at the local Google branch back in 2007" or something like that. Instead, it says this:

> let us go back in time and space, and journey to tropical Brazil in the distant time of 2007…

I mean if you're from Brazil, this is kinda weird no? Who describes their home country as a tropical place to "journey" towards? It reads like the start of a small anecdotal flashback, and not like the setting of the entire story. It took me many paragraphs to figure out that actually Arizona was the trip, and Brazil was the home base, and not the other way around. I did figure it out in the end, but I can understand why people might be thrown off.
by moralestapia | 2025-06-07 10:33:16+00:00
>If an aircraft diverts to another destination, it won’t divert again.

Hehe, I was once told we couldn't land at our destination A, so we got diverted to B; while on our way to B we were told we are actually going to C; and, while on our way to C, A became available again so the plane did a U-turn and we flew back to A, landing with a ~3 hour delay.

The cause was snow and wind.
by Jensson | 2025-06-07 10:33:20+00:00
If it was good enough to replace people then it wouldn't be too expensive, they would have launched it and replaced a bunch of people and made trillions of dollars by now.

So at best their internal models are still just performance multipliers unless some breakthrough happened very recently, it might be a bigger multiplier but that still keeps humans with jobs etc and thus doesn't revolutionize much.
by nkrisc | 2025-06-07 10:33:47+00:00
It’s sad how the existing corpus of artists’ works are being used against them.

The only reason this can generate images like these is because artists previously created artwork like this. And then in return they get:

> you don't need to high an artist or animator to develop you game.

Unless you created all the training material yourself, any model like this is highly unethical, in my opinion.
by krisoft | 2025-06-07 10:33:58+00:00
You can still provide support too if you want to. You just need to ask the user what their query was, what response they got, and what response they would be expecting. You can then as the expert either spot their problem immediately, or you can run the query and see for yourself what is going on.

Sure it is a possibility that the ticket will end up closed as “unable to reproduce”, but that is always a possibility. It is not like you have to shut off all support because that might happen.

Plus many support requests are not about the content of the api responses but meta info surrounding them. Support can tell you that you are over the api quota limit even if the content of your prompt was not logged. They can also tell you if your request is missing a required parameter or if they have had 500 errors because of a bad update on their part.
by viraptor | 2025-06-07 10:35:12+00:00
> Whereas my brain seems to be able to work at essentially the same intensity for as long as I want it to, up to 18 hours a day, and then I need a bit of sleep to recover.

Potential variable explaining this - what's your age? I could do this too in teens/twenties.

> How do other people here experience mental fatigue (or not)?

Just normal tiredness / distraction. Two days of actual full-on active pairing on something tricky and I just want to go to sleep after work.
by netcan | 2025-06-07 10:35:46+00:00
What are the implications of this.

As I understand accounting, this means that reported profits would be higher, and therefore incur more corporate income tax liability. Cash flow isn't effected besides tax.

A startup isn't likely to be making a profit yet, under either accounting rule. Is there a benefit to reporting a larger loss?

My first thought is that this effects Google and suchlike, not startups. But... assuming steady state "r&d" expenditure... it's not that much. Everything gets deducted within 5 years anyway.

So... maybe this hinders more modestly profitable, and fast growing companies most. Those that can't afford to carry 5 years worth of paper profits as easily.

Otoh... I am curious about how the difference between r&d expenses and operational ones are determined irl.

This should be quantifiable. How much extra assets are software companies actually booking?

It seems questionable that this "silent killer" had actually affected employment so much.
by stahtops | 2025-06-07 10:37:09+00:00
At first glance I support ... "social and economic equality" and "reforms to improve human conditions, combat corruption, and reduce inequality". Am I progressive?

If you ask me "should corporations pay more taxes?" I will say, yes. Famously so does Warren Buffet, is he also a progressive?

If you ask me, "hey should we gut tax incentives for R&D spending in the USA?" I will say, uhhh no? probably a bad choice?
by d4rkn0d3z | 2025-06-07 10:37:20+00:00
I wrote my first MLP 25 years ago. After repeating some early experiments in machine learning from 20 ywars before that. One of the experiments I repeated was in text to speach. It was amazing to set up training runs and return after seveal hours to listen to my supercomputer babble like a toddler. I literally recall listening and being unable to distinguish the output from my NN from that of a real toddler, I happened to be teaching my neice to read around that same time. And when the NN had gained a large vocabulary such that it could fairly proficiently read aloud, I was convinced that I had found my PHD project and a path to AGI.

Further examination and discussion with more experienced researchers gave me pause. They said that one must have a solution, or a significant new approach toward solving the hard problems associated with a research project for it to be viable, otherwise time (and money) is wasted finding new ways to solve the easy problems.

This is a more general principle that can be applied to most areas of endeavour. When you set about research and development that involves a mix of easy, medium, and hard problems, you must solve the hard problems first otherwise you blow your budget finding new ways to solve the easy problems, which nobody cares about in science.

But "AI" has left the realm of science behind and entered the realm of capitalism where several years of meaningless intellectual gyration without ever solving a hard problem may be quite profitable.
by baijum | 2025-06-07 10:40:44+00:00
That's a very fair and critical point. You're right that we can't change the fundamental, probabilistic nature of LLMs themselves.

But that makes me wonder if the goal should be reframed. Instead of trying to eliminate errors, what if we could change their nature?

The interesting hypothesis to explore, then, is whether a language's grammar can be designed to make an LLM's probabilistic errors fail loudly as obvious syntactic errors, rather than failing silently as subtle, hard-to-spot semantic bugs.

For instance, if a language demands extreme explicitness and has no default behaviors, an LLM's failure to generate the required explicit token becomes a simple compile-time error, not a runtime surprise.

So while we can't "fix" the LLM's core, maybe we can design a grammar that acts as a much safer "harness" for its output.
by xorcist | 2025-06-07 10:41:43+00:00
Relentlessly trying to lock up as much of the world's information as possible behind your login wall, I'd be struggling with procrastination as well.

Maybe the answer isn't so much finding new tricks to play on your mind, but finding something to do that doesn't involve codifying more power in the strong leader, to increase his masculinity in the worklace or whatever the political issue du jour is.
by UltraSane | 2025-06-07 10:42:28+00:00
I was thinking that if you had a good enough verified mathematical model of your code using TLA+ or similar you could then use an LLM to generate your code in any language and be confident it is correct. This would be Declarative Programming. Instead of putting in a lot of work writing code that MIGHT do what you intend you put more work into creating the verified model and then the LLM generates code that will do what the model intends.
by kqr | 2025-06-07 10:42:50+00:00
I find this list strange. I have only a passing interest in aviation and I would not believe very many of these.

What made the corresponding lists for names and time interesting were that it was genuinely surprising to realise that their statements were actually false. I don't get that feeling with these.

Like the top level comment about identifiers for airplanes -- why would they have them? That sounds baffling to me. With ownership changes, continuous upgrades, extending airframes, repurposing etc. I would be surprised if there was a stable identity.
by rxtexit | 2025-06-07 10:43:16+00:00
The copium is I think many people got comfortable post financial crisis with nothing much changing or happening. I think many people really liked a decade stretch with not much more than web framework updates and smart phone versioning.

We are just back on track.

I just read Oracular Programming: A Modular Foundation for Building LLM-Enabled Software the other day.

We don't even have a new paradigm yet. I would be shocked that in 10 years I don't look back at this time of writing a prompt into a chatbot and then pasting the code into an IDE as completely comical.

The most shocking thing to me is we are right back on track to what I would have expected in 2000 for 2025. In 2019 those expectations seemed like science fiction delusions after nothing happening for so long.
by vanderZwan | 2025-06-07 10:44:45+00:00
In general it's a bit of an apples to fruit salad comparison, albeit one that is appropriate to highlight the different use-cases of JS and Zig. The Zig example uses an array with a known type of fixed size, the JS code is "generic" at run time (x and y can be any object). Which, fair enough, is something you'd have to pay the cost for in JS. Ironically though in this particular example one actually would be able to do much better when it comes to communicating type information to the JIT: ensure that you always call this function with Float64Arrays of equal size, and the JIT will know this and produce a faster loop (not vectorized, but still a lot better).

Now, one rarely uses typed arrays in practice because they're pretty heavy to initialize so only worth it if one allocates a large typed array one once and reuses them a lot aster that, so again, fair enough! One other detail does annoy me a little bit: the article says the example JS code is pretty bloated, but I bet that a big part of that is that the JS JIT can't guarantee that 65536 equals the length of the two arrays so will likely insert a guard. But nobody would write a for loop that way anyway, they'd write it as i < x.length, for which the JIT does optimize at least one array check away. I admit that this is nitpicking though.