MurphyA/hacker-news huggingface dataset:
by NitpickLawyer | 2025-08-07 21:07:04+00:00
> Do the labs do not use source code?

The weights are part of the source code. When running inference on a model you use the architecture, config files and weights together. All of these are released. Weights are nothing but "hardcoded values". The way you reach those values is irrelevant in the license discussion.

Let's take a simple example: I write a chess program that is comprised of a source file with 10 "if" statements, a config file that matches between the variables used in the if statements and a "hardcoded values" file that stores the actual values. It would be a crappy chess program, but I hope you agree that I can release that as open source and no-one would bat an eye. You would also be granted the right to edit those hardcoded values, if you wish so. You'd perhaps make the chess bot better or worse. But you would be allowed to edit it, just like I would. That's the preferred way of modifying it. Me providing the methods that I used to reach those 10 hardcoded values has 0 bearing on my crappy chess bot being open source or not. Do we agree on that?

Now instead of 10 values, make it 100billion. Hey, that's an LLM!

> It is a bit like arguing that releasing a binary executable is releasing the source code.

That's the misconception. Weights are not a binary executable. In other words, there isn't another level above weights that the labs use to "compile" the weights. The weights exist from the beginning to the end, and the labs edit the weights if they want to modify the models. And so can you. There isn't a "compilation" step anywhere in the course of training a model.
by magicalhippo | 2025-08-07 21:07:07+00:00
I've found good use of Phi-4 at home, and after a few tests of the GPT-OSS 20B version I'm quite impressed so far.

Particularly one SQL question that has tripped every other model of similar or smaller size that I've tried, like Devstral 24B, Falcon 3 7B, Qwen2.5-coder 14B and Phi 4 14B.

The question contains an key point which is obvious for most humans, and which all of the models I tried previously have failed to pick up on. GPT-OSS picked up on it, and made a reasonable assumption.

It's also much more thorough at explaining code compared to the other models, again including details the others miss.

Now if only I had a GPU that could run the whole thing...
by olieidel | 2025-08-07 21:07:12+00:00
What about a software company founded in Germany by someone who grew up in another country, and accordingly got their education elsewhere?

What if that company is a remote company which hires people all over the world, and none of those people benefited from the {education|peace|law enforcement|trust} in Germany?

I do agree with you, in principle, that a company is somewhat coupled to the country it was founded in. The exact nature of that coupling, however, is not that simple, I would say.

Reality is complicated, I suppose :)
by derekstavis | 2025-08-07 21:07:44+00:00
Darklang is pretty fascinating, and was brought to our attention when we started demo-ing Capela to some folks in the industry. I think where Darklang (and others like Skip [1]) falls short is that it is a new language. Capela instead leverages typed Python, an existing and pretty familiar language to most programmers (and LLMs).

[1]: https://skiplabs.io
by ModernMech | 2025-08-07 21:08:20+00:00
It doesn't sustain, it's not supposed to. Techno feudalism is an indulgent fantasy and it's only becoming reality because a capitalist society aligns along the desires of capital owners. We are not doing it because it's a good idea or sustainable. This is their power fantasy we are living out, and its not sustainable, it'll never be achieved, but we're going to spend unlimited money trying.

Also I will note that this is happening along with a simultaneous push to bring back actual slavery and child labor. So a lot of the answers to "how will this work, the numbers don't add up" will be tried and true exploitation.
by noosphr | 2025-08-07 21:08:27+00:00
>This looks like they're not training the single big model but instead have gone off to develop special sub models and attempt to gloss over them with yet another model. That's what you resort to only when doing the end-to-end training has become too expensive for you.

The corollary to the bitter lesson strikes again: any hand crafted system will out perform any general system for the same budget by a wide margin.
by dev1ycan | 2025-08-07 21:09:27+00:00
We could expand but it boils down to bringing back aristocracy/feudalism, there was no inherent reason why aristocrats/feudal lords existed, they weren't smarter or deserved something over the average person, they just happened to be at the right place in the right time, these CEOs and people pushing for this believe they are in the right place and right time and once everyone's chance to climb the ladder is taken away then things will just remain in limbo, I will say, especially if you aren't already living in a rich country you should be careful of what you are supporting by enabling AI models, the first ladder to be taken away will be yours.
by rck | 2025-08-07 21:09:45+00:00
You are underestimating the hype around self-driving. A quick search gives this from 2018:

https://stanfordmag.org/contents/in-two-years-there-could-be...

The open (about the bet) is actually pretty reasonable, but some of the predictions listed include: passenger vehicles on American roads will drop from 247 million in 2020 to 44 million in 2030. People really did believe that self-driving was "basically solved" and "about to be ubiquitous." The predictions were specific and falsifiable and in retrospect absurd.
by stego-tech | 2025-08-07 21:09:51+00:00
Oh, don’t get me wrong, I’ve worked in plenty of organizations where politics takes preference over outcomes, and every middle manager and senior leader wants their pound of flesh before allowing the objectively right thing to happen. These people then promote each other into higher tiers, because modern shareholders couldn’t care less about business effectiveness when share buybacks will juice their earnings - and the C-Suite’s.

It’s a perverted reward hack by leadership, and nobody - governments, shareholders, boardrooms, regulators, etc - seems to give a shit.
by allthatineed | 2025-08-07 21:09:54+00:00
Location: Seattle

Remote: Please.

Willing to relocate: maybe, but only to places super friendly to trans women. (safe cities inside unsafe states are not a good fit.)

Technologies: Php, Ansible, HaProxy, Python3 (PyTorch, OnnxRuntime), cloudflare, Route53/GeoDns, DM, C/C++, C#.

Résumé/CV: https://kass.software

Email: kass@kass.software

Hello! My name is Kassandra Riley Spier-Swenson. I sometimes go by Kass or Kassie.

Find me on ${firstName}.software (all 3 variations)

Email me at ${firstName}@${firstName}.software (all 9 variations)

I made a very popular open source multiplayer video game faster (ss13) and then got kicked out of the community because I spoke out against sexual harassment and transphobic gender essentialism.

Been doing code and system/server administration for 15 years.

Once handled a 80gbps DDoS against a game server by making a tcp cloudflare out of micro vm haproxy relays (with first packet inspection to detect spoofed connections) and a cron script to sync nftable blocklists. This system is still in use.

Made a coqui-ai/TTS->RVS Machine Learning pipeline run on $200/m entry level GPU instances that could handle 100k requests an hour with less than a 250ms RTT in 99% of calls. This provided text to speech for a chat heavy video game with 90 player servers. This was my first python project and first time working with machine learning/AI. (<https://github.com/tgstation-operations/tgtts>) It was abandoned for a bloated mess that costs 10 times as much to run around the time I came out as trans (I'm sure it has no relation :3).

Have 0 years of professional employment in tech (it was all video game or open source stuff as a hobby.)

Please help me break into tech. I'm a bitching trans woman who clearly has skill :3
by WorldPeas | 2025-08-07 21:10:22+00:00
The problem, perhaps illusory that it's easy to fix, is that the model will choose solutions that are a year old, e.g. thinking database/logger versions from December '24 are new and usable in a greenfield project despite newer quarterly LTS releases superseding them. I try to avoid humanizing these models, but could it be that in training/posttraining one could make it so the timestamp is fed in via the system prompt and actually respected? I've begged models to choose "new" dependencies after $DATE but they all still snap back to 2024
Flipper Zero dark web firmware bypasses rolling code security
by lq9AJ8yrfs | 2025-08-07 21:10:42+00:00
flipper zero implementation of a variant [1] of the rolljam [2] attack

[1] https://arxiv.org/abs/2210.11923

[2] https://news.ycombinator.com/item?id=10018934
by cameldrv | 2025-08-07 21:11:02+00:00
Yeah it may be that previous training data, the model was given a strong negative signal when the human trainer told it it was wrong. In more subjective domains this might lead to sycophancy. If the human is always right and the data is always right, but the data can be interpreted multiple ways, like say human psychology, the model just adjusts to the opinion of the human.

If the question is about harder facts which the human disagrees with, this may put it into an essentially self-contradictory state, where the locus of possibilitie gets squished from each direction, and so the model is forced to respond with crazy outliers which agree with both the human and the data. The probability of an invented reference being true may be very low, but from the model's perspective, it may still be one of the highest probability outputs among a set of bad choices.

What it sounds like they may have done is just have the humans tell it it's wrong when it isn't, and then award it credit for sticking to its guns.
by GoatInGrey | 2025-08-07 21:11:23+00:00
My suspicion is that there's a there there, but it doesn't align with the predictions. This is supported by the tension between AI doom articles and the leading models experiencing diminishing performance gains while remaining error-prone. This is to speak nothing of the apparent LLM convergence limit of a ketamine-addled junior developer. Which is a boundary the models seem destined to approach indefinitely without ever breaching.

The "bust" in this scenario would hit the valuations (P/E ratio) of both the labs and their enterprise customers, and AI businesses dependant on exponential cost/performance growth curves with the models. The correction would shake the dummies (poorly capitalized or scoped businesses) out of the tree, leaving only the viable business and pricing models still standing.

That's my personal prediction as of writing.
by croddin | 2025-08-07 21:11:32+00:00
I vibe coded with gpt-5 and the source json (https://www.historicaltechtree.com/api/inventions) to get this list:

Top 10 inventions by number of direct descendants

1: High-vacuum tube — 13

2: Automobile — 12

3: Stored-program computer — 12

4: Voltaic pile — 11

5: High-pressure steam engine — 11

6: Glass blowing — 10

7: Papermaking — 10

8: Bipolar junction transistor — 10

9: Writing (Mesopotamia) — 9

10: MOSFET — 8
by schwartzworld | 2025-08-07 21:12:30+00:00
Local storage is a poor example because it updates in the background and wouldn’t necessarily change your UI much. But if a design calls for a search to be made while a user types that would get janky fast.

React in particular is data driven so in the above example, if you make the api call on each keypress, and save it into state or whatever, the UI will update automatically. I can type 70 words per minute. Nobody wants the search results to update that fast. (Should we be building searches that work this way? Often you have no choice.) A slow network + a short search string + a not top of the line device like a cheap phone means a really janky experience. And even if it’s not janky, its a waste of your users bandwidth (not everybody has unlimited) and an unnecessary drain on your server resources.

Even though we say “update as the user types” people type in bursts. There’s no reason not to debounce it, and if you can make the debounce function composable, you can reuse it all over the place. It’s a courtesy to the users and a good practice.
by 9rx | 2025-08-07 21:12:43+00:00
That's the feeling I get when I try to use LLMs for coding today. Every once in a blue moon it will shock me at how great the result is, I get the "whoa! it is finally here" sensation, but then the next day it is back to square one and I may as well hire a toddler to do the job instead.

I often wonder if it is on purpose; like a slot machine — the thrill of the occasional win keeps you coming back to try again.
by cnst | 2025-08-07 21:12:44+00:00
> 79% of OpenBSD kernel source is AMD DRM

The sys.tar.gz from https://ftp.eu.openbsd.org/pub/OpenBSD/7.7/ (normally unpacked into /usr/src/sys on an OpenBSD machine) represents the entire kernel source code of OpenBSD 7.7. (The userland and compiler are in src.tar.gz, Xorg is in xenocara.tar.gz, and ports are in ports.tar.gz.)

It has grown to 634MB unpacked for the entire kernel source tree.

But the vast majority of this growth is attributable to the sys/dev/pci/drm/amd directory, which is AMD Direct Rendering Manager, standing at 499MB, with the include files at sys/dev/pci/drm/amd/include being 458MB, the biggest of which is the asic_reg directory.

499/634 is 79%.

It follows that 79% of OpenBSD kernel by source code is dedicated to AMD's DRM implementation. Note that we're talking about the source code, NOT compiled code.

It's a huge part of Linux, too, over at drivers/gpu/drm/amd/include/asic_reg:

https://github.com/torvalds/linux/tree/master/drivers/gpu/dr...

In OpenBSD, the last release before the explosive growth, was OpenBSD 6.5 (Apr 2019) with sys.tar.gz at 20MB. With OpenBSD 6.6 (Oct 2019), it went to 30MB, now with 7.7 (Apr 2025) it's 64MB compressed.
by jcgrillo | 2025-08-07 21:13:05+00:00
> In ten years, I expect the primary interface of desktop workstations, mobile phones, etc will be voice prompts

I doubt it. The keyboard and mouse are fit predators, and so are programming, query, and markup languages. I wouldn't dismiss them so easily. This guy has a point: https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
by NitpickLawyer | 2025-08-07 21:13:41+00:00
> Consider the following: it is possible to release binaries under the Apache2 license. Microsoft has, at least at one point, released a binary under the BSD license. These binaries are not open source because they are not source.

Agreed. But weights are not binaries in the licensing context. For weights to be binaries it would imply another layer of abstraction, above weights, that the labs use as the preferred way of modifying the model, and then "compile" it into weights. That layer does not exist. When you train a model you start with the weights (randomly initialised, can be 0 can be 1, can be any value, whatever works best). But you start with the weights. And at every step of the training process you modify those weights. Not another layer, not another abstraction. The weights themselves.
by daneel_w | 2025-08-07 21:13:48+00:00
I don't understand your responses here. Clearly you are not familiar with what problem peppering solves, or why it's a recommended practice, no matter what self-contained password hashing you use. bcrypt, scrypt, Argon2; they are all subject to the same recommendation because they all store their salt together with the digest. You can always use a pepper, you should always use a pepper, and there's only one appropriate way to do it.
by jablongo | 2025-08-07 21:13:55+00:00
It's also worth considering that past some threshold, it may be very difficult for us as users to discern which model is better. I don't think thats what's going on here, but we should be ready for it. For example, if you are an ELO 1000 chess player would you yourself be able to tell if Magnus Carlson or another grandmaster were better by playing them individually? To the extent that our AGI/SI metrics are based on human judgement the cluster effect that they create may be an illusion.
by lyu07282 | 2025-08-07 21:14:10+00:00
It was never about money, it's about power. Money is just a mechanism, economics is a tool of justification and legitimization of power. In a monarchy it is god that ordained divine beings called kings to rule over us peasants, in liberalism it is hard working intelligent people who rise to the top of a free market. Through their merits alone are they ordained to rule over us peasants, power legitimized by meritocracy. The point is, god or theology isn't real and neither is money or economics.
by gwd | 2025-08-07 21:14:29+00:00
Cheaper is an understatement... it's less than 1/10 for input and nearly 1/8 for output. Part of me wonders if they're using their massive new investment to sell API below-cost and drive out the competitor. If they're really getting Opus 4.1 performance for half of Sonnet compute cost, they've done really well.
by lyrrad | 2025-08-07 21:15:03+00:00
I think 70 tests is overkill. As long as you are able to analyze and learn from your mistakes, you should be able to improve doing a fraction of the tests.

My experience was from almost two decades ago and is atypical. I had decided to write it only just a month before the LSAT, and so I started prior tests only four weeks before the test date. In that time, I did 27 official practice tests. I also read a couple books and tried two unofficial tests.

I tracked my results in a spreadsheet and had a marginal improvement from 173.4 to 176 for the first five and last five tests. But, essentially all improvement happened after just ten tests.

In all practice tests, I did best in logic games, answering 98.1% correct, compared with 93.5% for logical reasoning and 91.4% for reading comprehension.

I was somewhat lucky and got 97/99 on the actual test, enough for a 180 that time (2 incorrect in logical reasoning). I had only got that twice in the practice tests with an expectation between 174 and 180 with a median of 176.

After each test I analyzed every incorrect answer as well as the ones I was less sure about. There were online forums where I could ask about, or see other people's analysis of the problem questions.

I think a strategy aimed understanding wrong answers in your weakest area can be a more efficient use of preparation time.
Show HN: GPT5 generated kayak weather forecaster
by kylenessen | 2025-08-07 21:16:34+00:00
I wanted to share a project idea I've had bouncing around in my head for months now, but hadn't had the time to build out properly with other AI tools. Inspired by the demos from today's announcement, I decided to abandon any productive work and try out the new model. While it wasn't as easy as they showed on stage, it feels like a step-change improvement over any tools I've tried previously (Claude Code primarily, but models going back to GPT-3.5). The app itself has limited utility for HN I'm sure, but I thought I would share my real world example of how it has definitely leveled up my ability to build. This one feels different, FWIW.
by int_19h | 2025-08-07 21:16:40+00:00
It is, but traditionally progress bars were often paired with labels showing estimated remaining time.

That said, back in DOS era, this kind of thing was much more straightforward because most operations that would warrant a progress bar involved some kind of disk I/O, which - if you amortize it - is fairly linear, so one can estimate the completion time relatively well. In more complicated cases - e.g. Win95 installer doing things like hardware detection - those estimates were often wildly off.
by TheAlchemist | 2025-08-07 21:17:49+00:00
"I’ve been following Tesla for a very long time and I know that the company has a pretty solid track record of avoiding these types of judgments. Why do you think this was different?

Well, I mean, they did make an overture to settle the case, and for a very large sum of money. Now, it was a fraction of the verdict, but the condition of the settlement was that it would be secret. And my clients were not interested in a secret settlement. They knew that this was a case and a cause that was bigger than themselves. And it was important to them that we shine a light on what Tesla has done."

Hats off to those guys for not settling the case and going with it all the way. It should get unsealed in about 3 weeks according to the interview.
by zoeysmithe | 2025-08-07 21:19:08+00:00
It feels like we're slowly rebuilding the brain in pieces and connecting useful disparate systems like evolution did.

Maybe LLM's are the "language acquisition device" and language processing of the brain. Then we put survival logic around that with its own motivators. Then something else around that. Then again and again until we have this huge onion of competing interests and something brokering those interests. The same way our 'observer' and 'will' fights against emotion and instinct and picks which signals to listen to (eyes, ears, etc). Or how we can see thoughts and feelings rise up of their own accord and its up to us to believe them or act on them.

Then we'll wake up one day with something close enough to AGI that it won't matter much its just various forms of turtles all the way down and not at all simulating actual biological intelligence in a formal manner.
by j_timberlake | 2025-08-07 21:19:52+00:00
The correlation between "speculator is a billionaire" and "speculator is good at predicting things" is much higher than the correlation between "guy has a HN account" and "guy knows more about the future of the AI industry than the people directly investing in it".

And he doesn't just think he has an edge, he thinks he has superior rationality.
by palata | 2025-08-07 21:20:05+00:00
> The only way to make things unavailable is actually to file a dmca notice

Is it costly to do?

> In my experience, no they won’t help with gdpr takedowns

I would have expected that I could say "This is my code, hence this is my data, and I want you to remove my data from your website". I wonder how hard it is to file a complaint to the EU and see what happens.
by atleastoptimal | 2025-08-07 21:20:15+00:00
I think there are two competing factors. On one end, to get the same kind of "increase" in intelligence each generation requires an expontentially higher amount of compute, so while GPT-3 to GPT-4 was a sort of "pure" upgrade by just making it 10x bigger, gradually you lose the ability to just get 10x GPUs for a single model. The hill keeps getting steeper so progress is slower without exponential increases (which is what is happening).

However, I do believe that once the genuine AGI threshold is reached it may cause a change in that rate. My justification is that while current models have gone from a slightly good copywriter in GPT-4 to very good copywriter in GPT-5, they've gone from sub-exceptional in ML research to sub-exceptional in ML research.

The frontier in AI is driven by the top 0.1% of AI researchers. Since improvement in these models is driven partially by the very peaks of intelligence, it won't be until models reach that level where we start to see a new paradigm. Until then it's just scale and throwing whatever works at the GPU and seeing what comes out smarter.
by jdboyd | 2025-08-07 21:20:31+00:00
The case doesn't have an expansion slot, but if you order just the ITX motherboard there is an x4 slot. Since it isn't an open ended slot though, the question is where to find a PCIe 4.0 x4 card. You can certainly find 10gig cards that will fit. While the math works for performance, there don't seem to be anyone making 25gbit ethernet cards in x4 though.