MurphyA/hacker-news huggingface dataset:
by crq-yml | 2025-05-25 10:34:04+00:00
The pipeline bottlenecks all changed in favor of bruteforcing the things that BSP had been solving with an elegant precomputed data structure - what BSP was extremely good at was eliminating overdraw and getting to where the scene could render exactly the number of pixels that were needed and no more. It's optimized around small, low-detail scenes that carefully manage occlusion.

More memory, bandwidth and cache means that more of your solutions are per-pixel instead of per-vertex and you can tolerate overdraw if it means you get to have higher polycount models. Likewise, the environment collision that was leveraged by the BSP process reduced the number of tests against walls, but introduced edge cases and hindered general-purpose physics features. Scaling physics leads in the direction of keeping the detailed collision tests at their original, per-poly detail, but doing things with sorting or tree structures to get a broadphase that filters the majority of tests against AABB or sphere bounds.

On a Wii(original Wii) 3D action game I helped ship, we just rendered the whole level at once, using only the most basic frustum culling technique; the hardware did the lifting, mostly through the z-buffer.
by Joeboy | 2025-05-25 10:35:19+00:00
I would also like to work on more fun stuff! My uncertainty is whether enough fun paid programming work will appear to fill the gap left by the automation of the boring stuff.

I think a lot of people here are more interested in questions like "how am I going to pay my mortgage in future?" rather than specifically whether the job title of "software developer" will continue to exist (which I agree it probably will).
by selcuka | 2025-05-25 10:36:01+00:00
I was wondering if I’m the only one who thinks that way. He might be a better writer than some (including myself), but that’s a pretty low bar. The ideas are neither novel nor deep, and the writing is subpar.

I pasted the essay into ChatGPT, and this is what it said (just for fun; I’m not taking ChatGPT’s evaluation seriously.)

The essay is superficial, mildly derivative, and wrapped in a tone that suggests insight without delivering much. If it reads like someone thinking out loud and convincing themselves they’ve discovered something profound — that’s because it kind of is. It’s not wrong. It’s just lightweight. So why do people praise essays like this? Because they’re: - Readable: The ideas feel accessible and comforting. - Anecdotal: They use lived experience rather than abstract reasoning. - Flatter the reader: They make you feel smart for agreeing with something that feels intuitive. But that’s not the same as being deep or challenging.
by Barrin92 | 2025-05-25 10:36:30+00:00
>is simply the old games mentioned were the good ones of their generation.

I don't buy this. Yes obviously there's a survivorship bias but here's some of the most popular games of 1998 alone, from memory:

Ocarina of Time, Half Life, Xenogears, Metal Gear Solid, Thief, Starcraft, RE 2, Mario Party, Baldurs Gate

Almost 30 years later we still play franchise spin-offs and remakes off these games, half of them invented entire genres. The year before that, Golden Eye, FF 7, etc. It's not just that those are the good games people remember, they're so dominant in our culture when you ask someone what their favorite game right now is they're likely to say Baldurs Gate 3 or a remake of FF 7.

If you go forward ten, fifteen years with the exception of FromSoftware and the Souls games, I don't think anything has made remotely as much of an impact as even one or two games listed above.
by blixt | 2025-05-25 10:37:39+00:00
Let’s not forget that every round trip with the LLM costs latency (and extra input tokens). We now have parallel tool calls which sometimes works in some models[1]. But it’s great because now a model can say “write these 3 files then read these 2 files” before the time-to-first token latency is incurred once more (not to mention input token cost).

I think LLMs will indirectly move towards being fuzzy VMs that output tokens much like VM instructions so they can prepare multiple conditional branches of tool calling, load/unload useful subprograms, etc. It might not be expressed exactly like that, but I think given how LLMs today are very poor at reusing things in their context window, we will naturally add features that take us in this direction. Also see frameworks like CodeAct[2] etc.

[1] This can be converted to a single tool call with many arguments instead, which you’ll see providers do in their internal tools, but it’s just messier.

[2] https://machinelearning.apple.com/research/codeact
by epolanski | 2025-05-25 10:37:54+00:00
I don't know how much do I agree.

I mean, I'm a software developer and we have AI tools that we use, maybe not customer facing, but we have internal tools that provide lots of value.

Random example #1: we regularly scrape our competitors for their prices and offers, but it's insane to maintain all of that because our competitors update their pages/dom frequently so this doesn't scale over dozens of competitors and thousands of products. We replaced thousands of Playwright scraping code with few hundreds of lines of instructions for an LLM to find the information on the pages itself (it can figure out where/what is).

Random example #2: we sell some products that require lots of customer care and instructions as we're in the home maintenance business and sometimes there are questions about products and their installations that are extremely specific and we do have a chat and call center for our customers to assist them. What our customer care reps do is that they need to find and combine information often from multiple hundreds-page long PDFs. Thus we built an OCR+Rag pipeline to assist our customer care reps. This has provided tremendous value and cut our costs by a lot. It also has the potential to replace them completely and move them to other tasks, but it's gonna be a long time before we can really trust an LLM for that. The risks of giving incorrect or wrong information is too great right now. But internally customer care reps can double check the information as the LLM points out the original pdf sources (up to the specific line).

On top of that I'd say that there's also other tools that both make money and help us, such as AI in creative software like Adobe tools. Some of our graphic experts said that this speeds them up a lot, we need to produce or edit tons of images, illustrations, etc for our products.

And those are just few random examples of how AI definitely can speed up and improve margins of our company, while making cloud and saas vendors money too.
by diggan | 2025-05-25 10:38:19+00:00
Again, probably the optimist in my shining through, but hardship seems to (at least where I am) trigger people to react against it, and go out on the street to protests. Clearly, it isn't the case everywhere, but I do believe (or maybe hope) that if we ever get to that point, people won't give up until their life gets at least "Good Enough".
by dvh | 2025-05-25 10:40:53+00:00
1. Ok, the subtitles are in the lady's bust area, does that mean that glassholes 2.0 will look down there when they talk with someone?

2. The popup on the bottom right corner "Gemini start translate" is black on top of light background, does that mean that these new glasses can display dark on top of light background? I thought this was impossible with current technology. Or was it faked like magic leap faked the jumping whale demo?
by sebzim4500 | 2025-05-25 10:41:43+00:00
Having used claude 4 for a few hours (and claude 3.7 and gemini 2.5 pro for much more than that) I really think it's much better in ways that aren't being well captured by benchmarks. It does a much better job of debugging issues then either 3.7 or gemini and so far it doesn't seem to have the 'reward hacking' behavior of 3.7.

It's a small step for model intelligence but a huge leap for model usability.
by ben_w | 2025-05-25 10:41:49+00:00
Because of what else he writes besides the fanfic. (Better question is why anyone takes JKR herself seriously).

But if you insist on only listening to people with academic acolades or industrial output, there's this other guy who got the Rumelhart Prize (2001), Turing Award (2018), Dickson Prize (2021), Princess of Asturias Award (2022), Nobel Prize in Physics (2024), VinFuture Prize (2024), Queen Elizabeth Prize for Engineering (2025), Order of Canada, Fellow of the Royal Society, and Fellow of the Royal Society of Canada

That's one person with all that, and he says there's a "10 to 20 per cent chance" that AI would be the cause of human extinction within the following three decades, and "it is hard to see how you can prevent the bad actors from using [AI] for bad things.": https://en.wikipedia.org/wiki/Geoffrey_Hinton

Myself, I'm closer to Hinton's view than Yudkowsky's: path dependency, i.e. I expect that before we get existential threat from AI, we get catastrophic economic threat the precludes existential threat.
by frabcus | 2025-05-25 10:43:05+00:00
I'd like version numbers to indicate some element of backwards compatibility. So point releases (mostly) wouldn't need prompt changes, whereas a major version upgrade might require significant prompt changes in my application. This is from a developer API use point of view - but honestly it would apply to large personality changes in Claude's chat interface too. It's confusing if it changes a lot and I'd like to know!
by resiros | 2025-05-25 10:43:15+00:00
I think an approach like AlphaEvolve is very likely to work well for this space.

You've got all the elements for a successful optimization algorithm: 1) A fast and good enough sampling function + 2) a fairly good energy function.

For 1) this post shows that LLMs (even unoptimized) are quite good at sampling candidate vulnerabilities in large code bases. A 1% accuracy rate isn't bad at all, and they can be made quite fast (at least very parallelizable).

For 2) theoretically you can test any exploit easily and programmatically determine if it works. The main challenge is getting the energy function to provide gradient—some signal when you're close to finding a vulnerability/exploit.

I expect we'll see such a system within the next 12 months (or maybe not, since it's the kind of system that many lettered agencies would be very interested in).
by sieve | 2025-05-25 10:44:03+00:00
> Can't say I ever ran across that in any serious context

It exists. Invaders do not necessarily invent things out of whole cloth. You widen existing fissures in society in order to control it. The gotra-jaati-varna system has a complex history but the caste system in place for the past century or so in Indian society and politics is a gift of the British. They were the ones who started recording it during the census. A 2011 census found that people claim to belong to 4,673,034 different castes/sub-castes/gotras/clans.[1] Which is absolute insanity.

Castes that are considered to be "upper" in one district/state move down the ladder in other districts/states. But all this complexity is thrown out of the window in ideological discussions.

[1] https://en.wikipedia.org/wiki/2011_Socio_Economic_and_Caste_...
by makeitdouble | 2025-05-25 10:44:36+00:00
I'd somewhat love to hear your thoughts on this crossing at one of the most busy crossing of Tokyo

https://youtube.com/shorts/TvL2SR33XEA?si=F_fnbcvRGrizZ0rQ

The shibuya crossing also has at least 2 giant screens, with sound, and have been there for decades now. All of them have camera, and they probably have been checking the effect of each of the ads since the screens have been setup.

There's a lot to discuss I think.
by sebzim4500 | 2025-05-25 10:45:00+00:00
>I had to stop the model going crazy with unnecessary tests several times, which isn't something I had to do previously

When I was playing with this last night, I found that it worked better to let it write all the tests it wanted and then get it to revert the least important ones once the feature is finished. It actually seems to know pretty well which tests are worth keeping and which aren't.

(This was all claude 4 sonnet, I've barely tried opus yet)
by TheOtherHobbes | 2025-05-25 10:45:21+00:00
Your second point explains the first. There is a one-party corporate-captured system with two wings. One pushes the country rightwards, the other pretends to oppose it, but never makes structural changes that would push the country back towards higher taxes on the rich and higher public spending, even when it has the means and the opportunity.

Behind this is the biggest propaganda and PR machine in history, with mass media, social media, lobbyists, think tanks and policy institutes, client journalists, astroturfing operations, and individual politicians all generating compliant prepackaged talking points that either support the corporate line or distract opposition with noise.
by JackSlateur | 2025-05-25 10:46:28+00:00
Let's encrypt came as a living bane for the CA business

There are now alternatives, like zerossl for instance

But most importantly: it pushed ACME and all the automation blocks, DNS-based DCV and stuff

So now, lots (all ?) providers also let's you generate certificate (cloud provider and cdn and whatever)

In the end, no, let's encrypt hardly controls "most of the domains on the internet"
by geraneum | 2025-05-25 10:48:33+00:00
This has become a common recurrence recently.

Have a problem with clear definition and evaluation function. Let LLM reduce the size of solution space. LLMs are very good at pattern reconstruction, and if the solution has a similar pattern to what was known before, it can work very well.

In this case the problem is a specific type of security vulnerability and the evaluator is the expert. This is similar in spirit to other recent endeavors where LLMs are used in genetic optimization; on a different scale.

Here’s an interesting read on “Mathematical discoveries from program search with large language models” which was I believe was also featured in HN the past:

https://www.nature.com/articles/s41586-023-06924-6

One small note, concluding that the LLM is “reasoning” about code just _based on this experiment_ is bit of a stretch IMHO.
by zozbot234 | 2025-05-25 10:49:52+00:00
> Estimation theory makes a lot of sense - to me a lot more than pulling priors out of thin air.

You're "pulling priors out of thin air" whether you realize it or not; it's the only way that estimation makes sense mathematically. Frequentist statistics is broadly equivalent to Bayesian statistics with a flat prior distribution over the parameters, and what expectations correspond to a "flat" distribution ultimately depends on how the model is parameterized, which is in principle an arbitrary choice - something that's being "pulled out of thin air". Of course, Bayesian statistics also often involves assigning "uninformative" priors out of pure convenience, and frequentists can use "robust" statistical methods to exceptionally take prior information into account; so the difference is even lower than you might expect.

There's also a strong argument against NHST specifically that works from both a frequentist and a Bayesian perspective: NHST rejects the Likelihood principle https://en.wikipedia.org/wiki/Likelihood_principle hence one could even ask whether NHST is even "properly" frequentist.
by bgwalter | 2025-05-25 10:50:16+00:00
Well, Sun Microsystems launched a cloud shortly before being acquired by Oracle:

https://en.wikipedia.org/wiki/Sun_Cloud

Microsoft's Azure is reportedly a loss leader:

https://www.cnbc.com/2022/12/21/google-leaked-doc-microsoft-...

But don't let that stop you from going outside your core competency.
by boricj | 2025-05-25 10:50:24+00:00
The context of this is quite peculiar.

Given the amount of information loss that occurs at the linking stage, delinking is an extremely intricate puzzle to solve. It's also very hard to be sure that you got it right, because there are many, many ways it can go wrong in ways that are a nightmare to troubleshoot. The scale of the endeavor can be also staggering: one user has over 250 000 relocation spots in their artifact, pretty much all of them must be spot-on otherwise glitches and crashes will occur.

The only way I've managed to pull it off is with an analyzer design that makes no assumptions, double-checks everything, logs anything that couldn't be successfully processed and heavily investing into regression testing. The manual process then consists of fixing inaccuracies within the Ghidra database, or (rarely nowadays) fix an unhandled corner case within the analyzer. Also, no manual annotations of relocations means they cannot go stale as the Ghidra database is modified.

I also happened to attempt this first on possibly the worst architecture possible, MIPS. I won't go into the details, but split HI16/LO16 relocations, register dependency graphs and branch delay slots can produce some absolutely gnarly cases to untangle. Let's just say that my MIPS analyzer contains among other things one recursive function that takes 6 parameters, 4 of them shifts one place to the left at each iteration. I'm not exactly sure how it works.
by atombender | 2025-05-25 10:51:03+00:00
So how's the Verse language and ecosystem turning out?

There was a big splash about this when it came out in 2023, with big names like Simon Peyton-Jones, Lennart Augustsson, and Guy Steele involved. But since then I've heard nothing. Apparently still Fortnite-only, not built into the Unreal Engine. I'm not wired into the gamedev world, is anything happening here?
by panstromek | 2025-05-25 10:51:31+00:00
Yes, also a reason why I think the word is slowly disappearing. We used to call JS bundling tools transpilers, but I noticed more and more people call them just compilers. They do everything that compiler do, they only differ in output being a programming language.

I think what distinguishes the word is not necessarily what it does, but what the use case is. I usually say transpiler when I'm talking about source-to-source tools (like c2rust), where the product is more inteded to be consumed or modified by a human. I use compiler when I talk about source-to-artifact tools.
by mike_hearn | 2025-05-25 10:52:26+00:00
I don't quite understand one thing. They seem to think that keeping their past research papers out of the training set is too hard, so rely on post-training to try and undo the effects, or they want to include "canary strings" in future papers. But my experience has been that basically any naturally written English text will automatically be a canary string beyond about ten words or so. It's very easy to uniquely locate a document on the internet by just searching for a long enough sentence from it.

In this case, the opening sentence "People sometimes strategically modify their behavior to please evaluators" appears to be sufficient. I searched on Google for this and every result I got was a copy of the paper. Why do Anthropic think special canary strings are required? Is the training pile not indexed well enough to locate text within it?
by sylware | 2025-05-25 10:53:07+00:00
I am writing a wayland compositor which suits me.

This is, indeed, a strong signal of an inflexion point.

Simple interface designs which do a good enough job are so much important. But it is so hard to keep them stable in time. There are always lunatics and toxics (whatever their reasons) which will want to extend/break/etc: they abuse the fact that, sometimes, critical mistakes (in the end) will have to be corrected... and to make it short, they see mistakes all-the-time/everywhere for their own agenda.
by SV_BubbleTime | 2025-05-25 10:53:27+00:00
I am aware in relative terms you are correct about Anthropic.

But I’m having a hard time describing and AI company “serious” when they’re shipping a product that can email real people on its own, and perform other real actions - while they are aware it’s still vulnerable to the most obvious and silly form of attack - the “pre-fill” where you just change the AI’s response and send it back in to pretend it had already agreed with your unethical or prohibited request and now to keep going.
by defrost | 2025-05-25 10:53:34+00:00
> It exists.

Sure, no disagreement from me on that.

Flat Earth, Young Earth, Lysenkoism, Vaccines causing Autism, Alien Lizard People wearing Human Skin, et al are all examples of other beliefs that exist, have had books devoted to them, have true believers, etc.

These are also things that I don't come across in serious contexts.

> Invaders do not necessarily invent things out of whole cloth. You widen existing fissures in society in order to control it.

Sure, something seen in history often enough. This supports the notion of a caste system existing prior to the British.

> They were the ones who started recording it during the census.

If it didn't exist prior to the British "recording it during census" then what was it they were recording?

Something they made up whole that never existed prior?

> A 2011 census found that people claim to belong to 4,673,034 different castes/sub-castes/gotras/clans.

.. and this was the gift of the British? Sounds more like a home grown Indian expansion.
by edanm | 2025-05-25 10:54:31+00:00
> If we state the full nature of our experiment, what we controlled and what we didn't... how can it be a "degree of belief"?

Here's a question for you: what's the millionth digit of Pi? (In the standard decimal expansion, of course.)

This is a question (kind of an "experiment") which literally has only one, constant answer, that is theoretically knowable. But unless you search online, or happen to know the answer, you actually don't know which of the digits 0-9 it is. And I can just as easily ask about the 10^100th digit of Pi, which is, again, a constant - and yet no one knows what it is.

So using the Frequentist approach to statistics doesn't make much sense - there's no repeated experiment with possible different outcomes.

But there is a real sense in which your answer should be "it's one of the digits 0-9 with 1/10 probability each". That answer makes sense, because the answer isn't unknowable, just unknown, and probability reflects your lack of knowledge and degree of belief.
by gmerc | 2025-05-25 10:55:36+00:00
Old games being available has stopped enshittification and value extraction from VC and PE.

The cost of new games has reached a point where that kind of capital is needed but the value extraction mechanisms these funding methods require don’t work when there’s competition from old games that allocate more value to the user.

The hardware upgrade train stopped about 2019 too because more expensive hardware scarcely makes a difference for modern games anymore.

Steam, as a privately owned company ensuring old games stay available is single handedly responsible for all kinds of attackers, legacy publishers and Big Tech from gaining control over ecosystem or backlog.

MS is trying to fuck around with game pass but it’s doomed with Steam.

The moment of truth will come when Gabe Newell gives up control
by itchyjunk | 2025-05-25 10:55:46+00:00
I have the same experience. I was pretty happy with gemini 2.5 pro and was barely using claude 3.7. Now I am strictly using claude 4 (sonnet mostly). Especially with tasks that require multi tool use, it nicely self corrects which I never noticed in 3.7 when I used it.

But it's different in conversational sense as well. Might be the novelty, but I really enjoy it. I have had 2 instances where it had very different take and kind of stuck with me.
by rvz | 2025-05-25 10:55:52+00:00
Exactly. It's getting to the point where the quality of the top AI labs are either not ground-breaking (except Google Gemini Diffusion) and labs are rushing to announce their underwhelming models. Llama as an example.

Now in the next 6 months, you'll see all the AI labs moving to diffusion models and keep boasting around their speed.

People seem to forget that Google Deepmind can do more than just "LLMs".