MurphyA/hacker-news huggingface dataset:
by esseph | 2025-08-07 18:29:04+00:00
I don't know what the fuck "my kind" is, but you instantly threw away any validity you may have remotely had by jumping to some nonsense like that.

I'm not "missing the point" because I don't agree with economic principles that got us where we are today. Doubling down on those very same principles thinking we will have a different outcome? Nonsense.
by PaulHoule | 2025-08-07 18:29:18+00:00
... And be right 85% of the time.

Back in the 1990s there was that sordid episode when Microsoft Office used those other UUIDs which were based on time and a MAC address so any Office document could be tracked to what computer you used and when.

In my lost half-decade I was pursuing the dream of "Real Semantics", which, in retrospect, was a baby Cyc, not so much a master knowledge base but a system for maintaining knowledge bases for other systems that could hold several parallel knowledge bases at once. I read everything public about Cyc and also tried using DBpedia and Freebase as a prototype "master" database. Lenat strongly believed you'd get in trouble pretty quickly if you tried to make non-opaque identifiers but Wikipedia has done a pretty good job of it for 7M items, with the caveat that Wikipedia doesn't have a consistent level of modelling granularity (e.g. it is so much easier for a video game to be notable than a book, some artists have all their major songs in Wikipedia, others don't, there is no such thing as a "Ford Thunderbird" but there is a "7th generation Ford Thunderbird", etc.)
by teamweightloss | 2025-08-07 18:29:31+00:00
Trump 2016 was taken less seriously by multinationals, as the anti-globalization wave hasn't fully realized, and corporation were paying lip service to Trump, which the 2016 administration had no choice but to accept it due to only having power in the executive branch.

Trump 2024 is a completely different animal, with control in all 3 branches of government, plus overwhelming voter support in the election. As well as the collapse of globalization (baby boomer retiring reducing demand), and many countries moving to the right at the same time. Reshoring is the correct choice for the next 10 years and beyond, and many multinationals recognize this and have committed hundreds of billions accordingly.
by andai | 2025-08-07 18:29:37+00:00
> While GPT‑5 in ChatGPT is a system of reasoning, non-reasoning, and router models, GPT‑5 in the API platform is the reasoning model that powers maximum performance in ChatGPT. Notably, GPT‑5 with minimal reasoning is a different model than the non-reasoning model in ChatGPT, and is better tuned for developers. The non-reasoning model used in ChatGPT is available as gpt-5-chat-latest.

https://openai.com/index/introducing-gpt-5-for-developers/
by evnc | 2025-08-07 18:30:06+00:00
I assume they're doing "Structured Generation" or "Guided generation", which has been possible for a while if you control the LLM itself e.g. running an OSS model, e.g. [0][1]. It's cool to see a major API provider offer it, though.

The basic idea is: at each auto-regressive step (each token generation), instead of letting the model generate a probability distribution over "all tokens in the entire vocab it's ever seen" (the default), only allow the model to generate a probability distribution over "this specific set of tokens I provide". And that set can change from one sampling set to the next, according to a given grammar. E.g. if you're using a JSON grammar, and you've just generated a `{`, you can provide the model a choice of only which tokens are valid JSON immediately after a `{`, etc.

[0] https://github.com/dottxt-ai/outlines [1] https://github.com/guidance-ai/guidance
by mlinhares | 2025-08-07 18:30:35+00:00
Yup, then the technical people have to deal with the bullshit and nightmare that is implementing the shitty thing that was purchased. Hard to get around this in large enterprises, someone just decides they're going to use some shitty tool (like that cloud provider no one uses but has great sales folks) and you just get a notice you have to migrate all your apps to the new thing in 3 months cos they got a better contract there.

The school district my kids are changes the parent app almost every year, its always a nightmare for everyone involved, I can't imagine what it is like to work IT in such a place.
by NickM | 2025-08-07 18:30:38+00:00
The trouble is that making progress in leaps is often mutually exclusive with being productive in the short term. It’s hard to think big and plan long-term when you’re constantly overwhelmed with what’s in front of you.

Slow Productivity by Cal Newport talks about this trade-off extensively and provides interesting points of reference where real famous historical figures achieved incredible things in ways that would seem slow and lazy by modern standards.
by famouswaffles | 2025-08-07 18:30:48+00:00
>Your half of the definition is implied, but uninteresting.

How is it uninteresting? Open AI had revenue of $12B last year without monetizing literally hundreds of millions of free users in any way whatsoever (not even ads).

Microsoft's cloud revenue has exploded in the last few years off the back of AI model services. Let's not even get into the other players.

100B in economic impact is more than achievable with the technology we have today right now. That half is the interesting part.
by avs733 | 2025-08-07 18:31:45+00:00
Its a particular type of mistake that is really interesting and telling. It is a misconception - and a common socially disseminated simplifcation. In students, these don't come from a lack of knowledge but rather from places where knowledge is structured incorrectly. Often because the phenomenon are difficult to observe or mislead when observed. Another example is heat and temperature. Heat is not temperature, but it is easy to observe them always being the same in your day to day life and so you bring that belief into a college thermodynamics course where you are learning that heat and temperature are different for the first time. It is a commonsense observation of the world that is only incorrect in technical circles

These are places where common lay discussions use language in ways that is wrong, or makes simplifcations that are reasonable but technically incorrect. They are especially common when something is so 'obvious' that experts don't explain it, the most frequent version of the concepts being explained

These, in my testing, show up a lot in LLMs - technical things are wrong when the most language of the most common explanations simplifies or obfuscates the precise truth. Often, it pretty much matches the level of knowledge of a college freshman/sophmore or slightly below, which is sort of the level of discussion of more technical topics on the internet.
by watusername | 2025-08-07 18:31:56+00:00
One issue that comes with leaving GitHub is a higher barrier to contributing. The author appears to see this as a nice filter, but it may not make sense for you. With a self-hosted forge, a new contributor will need to:

a) Sign up for an account in your forge: Do contributors really want another account? Does your captcha/email verification actually work (I've encountered ones that don't)? There are also forges that require you to ask for an account which is another hurdle.

b) Send an email: Configuring `git send-email` is alien to many contributors and may not even be doable in some corporate environments (OAuth2 with no app passwords allowed). Diverging from this is error-prone and against social norms which the contributor may not even be aware of (until they get flamed in the mailing list). You are also giving up automated CI which is a big part of the contributor feedback loop.

To be clear, going independent does indeed work for small personal projects (do not care much about contributions) as well as established ones (large incentive for new contributors to jump over hoops), and I'm fully aware that a lot of HNers do not see the need for those "niceties" provided by GitHub. But I feel that people often underestimate the barriers that they are putting up.
by BoorishBears | 2025-08-07 18:32:02+00:00
I'm extremely pro AI, it's what I work on all day for a living now, and I don't see how you can deny there is some justification for people being so cynical.

This is not the happy path for gpt-5.

The table in the model card where every model in the current drop down somehow maps to one of the 6 variants of gpt-5 is not where most people thought we would be today.

The expectation was consolidation on a highly performant model, more multimodal improvements, etc.

This is not terrible, but I don't think anyone who's an "accelerationist" is looking at this as a win.

Update after some testing: This feels like gpt-4.1o and gpt-o4-pro got released and wrapped up under a single model identifier.
by serial_dev | 2025-08-07 18:32:27+00:00
> your middle manager is responsible for enforcing the C-suite's directives on you

So true, our company monitors PR counts, it’s all pushed from higher level execs. So my manager, the middle manager, tells us, “make sure your PR counts do not stand out negatively” and pretty much independently of this, make sure you deliver value. We need to keep the PR count up, so that execs see charts going up, and need to work on things that matter so that they don’t realize those are BS metrics to enforce. The thing is measuring what matters is not trivial for software engineering, so they measure what’s easy to measure, and they can pat themselves on the back for being data driven.
by cootsnuck | 2025-08-07 18:32:49+00:00
Not true. I've been a paid user forever and on the Android app they have definitely obscured the model selector. It's readily visible to me on desktop / desktop browser. But on the Android app the only place I can find it is if I click on an existing response already sent by chatGPT and then it gives me the option to re-generate the message with a different model.

And while I'm griping about their Android app, it's also very annoying to me that they got rid of the ability to do multiple, subsequent speech-to-text recordings within a single drafted message. You have to one-shot anything you want to say, which would be fine if their STT didn't sometimes failed after you've talked for two minutes. Awful UX. Most annoying is that it wasn't like that originally. They changed it to this antagonistic one-shot approach a several months ago, but then quickly switched back. But then they did it again a month or so ago and have been sticking with it. I just use the Android app less now.
by duxup | 2025-08-07 18:32:53+00:00
I don't disagree with the idea that publish is a better word.

I do worry that with all software "some new people are confused" is a very dangerous reason to make changes. It feels like it could be reasonable, or maybe a couple confused sub optimal users demanding change that impacts everyone ...

When I work with new clients I often get a laundry list of customization requests, some very well thought out and reasonable, some absurd. I often ask to "push it to phase 2", and once they get familiar with the software ... suddenly they don't want to make those changes because they've adapted and they see why things are the way they are.
by usaar333 | 2025-08-07 18:33:28+00:00
No, this is below expectations on both Manifold and lesswrong (https://www.lesswrong.com/posts/FG54euEAesRkSZuJN/ryan_green...). Median was ~2.75 hours on both (which already represented a bearish slowdown).

Not massively off -- manifold yesterday implied odds this low were ~35%. 30% before Claude Opus 4.1 came out which updated expected agentic coding abilities downward.
by anigbrowl | 2025-08-07 18:33:32+00:00
(whispers) they're bullshit artists

It's like those idiotic ads at the end of news articles. They're not going after you, the smart discerning logician, they're going after the kind of people that don't see a problem. There are a lot of not-smart people and their money is just as good as yours but easier to get.
by avs733 | 2025-08-07 18:34:11+00:00
the wrongness isn't germane to most people but it is a specific typology of how LLMs get technica lthings wrong that is critically important to progressing them. It gets subtle things wrongby being biased towards lay understandings that introduce vagueness because greater precision isn't useful.

That doesn't matter for lay audieces and doesn't really matter at all until we try and use them for technical things.
by TheOtherHobbes | 2025-08-07 18:34:11+00:00
The article actually says this.

I'm wondering how many people read it to the bottom? It makes the case that productivity analysis tools already exist, maximally productive systems have some slack in them and 100% efficiency isn't just impossible, it's counterproductive (for the reason you state).

But the problem with AI is that it adds a random element which makes any kind of modelling much harder. Sometimes you get good results fasts, sometimes it wastes a lot of time and holds up the project.

You never know what you're going to get. So any kind of project planning becomes even harder.

But that's not even the main point. The subtext - which isn't stated - is that the C-suite has persuaded itself that AI is a system that is more controllable and predictable than human employees.

When in fact - as anyone working at the coal face knows - it's the opposite.

And that's a problem, for all kinds of reasons. The obvious ones, like the loss of expertise through career progression, have already been talked about.

The less obvious one being discussed here is that the more AI is used, the less predictable all kinds of projects become - both in time and quality.

And if the economy is now being designed on the assumption the opposite is true, that's not going to end well.

In previous phases of industrial revolution consistency was the bedrock benefit.

Trying to create a revolution out of inconsistency is a very risky click.
by NickM | 2025-08-07 18:34:11+00:00
SWE salaries are a massive cost. Improving productivity is one way of offsetting that cost.

In a lot of businesses you get praise and look important if you’re responsible for leading a large group of highly paid employees, more so then if you have a smaller team.

Thus the motivation is frequently to spend as much money as possible, not to improve efficiency.

If you improve efficiency then maybe you just get your team size cut and people ask hard questions about why you needed all those resources in the first place.
by likium | 2025-08-07 18:34:16+00:00
My point is humans make mistakes too, and we trust them, not because we inspect everything they say or do, but from how society is set up.

I'm not sure how up to date you are but most AIs with tool calling can do math. Image generation hasn't been generating weird stuff since last year. Waymo sees >82% fewer injuries/crashes than human drivers[1].

RL _is_ modifying its behavior to increase its own profitability, and companies training these models will optimize for revenue when the wallet runs dry.

I do feel the bit about being economically replaced. As a frontend-focused dev, nowadays LLMs can run circles around me. I'm uncertain where we go, but I would hate for people to have to do menial jobs just to make a living.

[1]: https://www.theverge.com/news/658952/waymo-injury-prevention...
by camdenreslink | 2025-08-07 18:34:20+00:00
I find it struggles to even refactor codebases that aren't that large. If you have a somewhat complicated change that spans the full stack, and has some sort of wrinkle that makes it slightly more complicated than adding a data field, then even the most modern LLMs seem to trip on themselves. Even when I tell it to create a plan for implementation and write it to a markdown file and then step through those steps in a separate prompt.

Not that it makes it useless, just that we seem to not "be there" yet for the standard tasks software engineers do every day.
Show HN: Octofriend, a cute coding agent that can swap between GPT-5 and Claude
by reissbaker | 2025-08-07 18:34:21+00:00
Hey HN! We're shipping Octofriend today, a cute coding assistant that can swap between GPT-5, Claude, local or open-source LLMs, etc mid-conversation as needed. It handles reasoning tokens (including encrypted ones from OpenAI and Anthropic) really well, and includes a couple of custom-trained ML models to fix minor diff edit and JSON encoding errors that we've also open-sourced. Have fun!
by imadr | 2025-08-07 18:34:35+00:00
The model has multiple layers which are basically a giant non-linear equation to predict the final shaded pixel, I don't see how it's inherently difference from a shader outputing a pixel "at once".

Correct me if I'm wrong, but I don't see how you can simulate a PBR pixel without doing ANY pbr computation whatsoever.

For example one could imagine a very simple program computing sin(x), or a giant multi-layered model that does the same, wouldn't it just be a latent, more-or-less compressed version of sin(x)?
by allenbina | 2025-08-07 18:34:49+00:00
This was around 10 years ago, but at the time the suggestions on the forums were more towards fidelity and 5.1 surround sound. I can't recall exactly, but something of the sort. My guess is that these were people with audio backgrounds who wanted to commit to the project.

I was making suggestions that were more for DJs. My suggestions were (vaguely from memory) keyboard and midi shortcuts for going back 4 - 16- 32 beats. Looping the same number of beats. setting exact cue points (think off beat) and setting synchronized (auto move to the closest beat if bpm was set). Nothing crazy and things that I felt would be necessary for most DJs.

I must have set my alerts far to high because I was bombarded with emails for a week until I completely unsubscribed. At the time I was also getting interested in XBMC and felt their community was much more welcoming and the software was simpler to understand.
by getnormality | 2025-08-07 18:34:56+00:00
It's interesting to compare this to the new third generation benchmarks from ARC-AGI, which are essentially a big collection of seemingly original puzzle video games. Both Mechanize (OP) and ARC want AI to start solving more real-world, long-horizon tasks. Mechanize wants to get AI working directly on real software development, while ARC suggests a focus on much simpler IQ test-style tasks.
by demirbey05 | 2025-08-07 18:35:13+00:00
test time scaling is based on methods from pre-2020. If you look details of modern LLMs its pretty small prob to encounter method from 2020+(ROPE,GRPO). I am not saying IMO is not impressive, but it is not breakthrough, if they said they used different paradigm then test-time scaling I would say breakthrough.

> We are not close to solving IMO with publicly known methods. The point here is not method rather computation power. You can solve any verifiable task with high computation, absolutely there must be tweaks in methods but I don't think it is something very big and different. Just OAI asserted they solved with breakthrough.

Wait for self-adapting LLMs. We will see at most in 2 years, now all big tech are focusing on that I think.
by stouset | 2025-08-07 18:35:13+00:00
It can't go on indefinitely, but thanks to exponential growth it also can't go on for much longer. 275 years is—to me at least—a shockingly short time-frame. Obviously it won't be something I live to see, but humanity being faced with insurmountable physical limits to growth in a handful of generations was eye-opening for me.
by nobodywillobsrv | 2025-08-07 18:35:36+00:00
This is one of these "best efforts" but also "lying a bit in marketing" is ok I guess.

On bad days this really bothers me. It's probably not the biggest deal I guess but somehow really feels like it pushes us all over the edge a bit. Is there a post about this phenomena? It feels like some combination of bullying, gaslighting and just being left out.
by logifail | 2025-08-07 18:35:48+00:00
I've travelled quite a lot in the top half of Italy over the decades for both work and pleasure - Milan, Venice, Florence, Siena, Verona, Bologna, Pisa, Parma.

I would suggest that from the outside it could be easy to underestimate just how seriously (many) Italians take their food.

Last year - as a family, on the way somewhere else - we visited a "factory" that makes Parmesan cheese. It's astounding how much work and time goes into making a product that, although it's of course produced "on mass", feels anything other than mass produced.
by kybernetikos | 2025-08-07 18:36:15+00:00
So I worry that you think that the equal transit time thing is true, but is just one effect among others. This is not the case. There are a number of different effects, including bernoulli and coanda and newtons third law that all contribute to lift, but none of the things that actually happen have anything to do with equal transit time.

The equal transit time is not a partially correct explanation, it's something that doesn't happen. It's not a superficial explanation, it's a wrong explanation. It's not even a good lie-to-children, as it doesn't help predict or understand any part of the system at any level. It instead teaches magical thinking.

As to whether it matters? If I am told that I can ask my question to a system and it will respond like a team of PhDs, that it is useful to help someone with their homework and physical understanding, but it gives me instead information that is incorrect and misleading, I would say the system is not working as it is intended to.

Even if I accept that "audience matters" as you say, the suggested audience is helping someone with their physics homework. This would not be a suitable explanation for someone doing physics homework.
by toast0 | 2025-08-07 18:37:01+00:00
The Constitution was ratified by state's conventions in 1787, 1788, 1789, and 1790. The convention of the ninth state (New Hampshire) ratified on June 21, 1788.

However, the operation of the Constitution didn't commence until March 1789 [1] [2]. Prior to that commencement, government still operated under the Articles of Confederation. The question is poorly worded, if they're asking for when the federal government began operating under the Constitution; but it's also poorly worded if they're asking for when the ninth state ratified it.

[1] https://supreme.justia.com/cases/federal/us/18/420/ [2] https://tile.loc.gov/storage-services/service/ll/usrep/usrep...
by oceanplexian | 2025-08-07 18:37:11+00:00
AGI isn't all that impactful. Millions of them already walk the Earth.

Most human beings out there with general intelligence are pumping gas or digging ditches. Seems to me there is a big delusion among the tech elites that AGI would bring about a superhuman god rather than a ethically dubious, marginally less useful computer that can't properly follow instructions.
by ricardobayes | 2025-08-07 18:37:24+00:00
To me, it's weird to call it "PhD-level". That, to me, means to be able to take in existing information on a certain very niche area and able to "push the boundary". I might be wrong but to date I've never seen any LLM invent "new science", that makes PhD, really PhD. It also seems very confusing to me that many sources mention "stone age" and "PhD-level" in the same article. Which one is it?

People seem to overcomplicate what LLM's are capable of, but at their core they are just really good word parsers.
by Retric | 2025-08-07 18:37:27+00:00
Assuming steady exponential curves over hundreds of years always results in nonsense. Going backwards you find out energy consumption doesn’t fit that model. For example the US total electricity consumption over the last 20 years should have gone up by 1-1.023^20 = 57.6% when it’s almost flat over that timeframe. Efficiency gains matter, as we don’t want to heat our homes to unlimited temperatures jump a comfortable one.

Globally rather than an exponential curve instead the global quality of life keeps rising as more people enjoy the benefits of modern technology like AC and tablets, but the number of people isn’t continually increasing. Birth rates keep declining so in the short term its populations catching up to increased lifespans.