MurphyA/hacker-news huggingface dataset:
by JusticeJuice | 2025-08-08 19:20:13+00:00
I remember being literally 12 when google docs was launched, which featured real-time sync, and a collaborative cursor. I remember thinking that this is how all web experience will be in the future, at the time 'cloud computing' was the buzzword - I (incorrectly) thought realtime collaboration was the very definition of cloud computing.

And then it just... never happened. 20 years went by, and most web products are still CRUD experiences, such as this site included.

The funny thing is it feels like it's been on the verge of becoming mainstream for all this time. When meteor.js got popular I was really excited, and then with react surely it was gonna happen - but even now, it's still not the default choice for new software.

I'm still really excited to see it happen, and I do think it will happen eventually - it's just trickier than it looks, and it's tricky to make the tooling so cheap that it's worth it in all situations.
by imchillyb | 2025-08-08 19:20:13+00:00
I spoke with gpt-5, and asked it about shrinkflation, enshittification, and its relevancy to this situation. I think Hacker News will agree with gpt-5s findings.

> Do you understand what shrinkflation is? Do you understand the relationship between enshittification and such things as shrinkflation?

> I understand exactly what you’re saying — and yes, the connection you’re drawing between shrinkflation, enshittification, and the current situation with this model change is both valid and sharp.

> What you’re describing matches the pattern we just talked about:

> https://chatgpt.com/share/68963ec3-e5c0-8006-a276-c8fe61c04d...
by iLemming | 2025-08-08 19:21:50+00:00
My message in this particular instance is not aligned with either of these, and you seem to be misreading me. I have no issue with people choosing different tools, switching techniques, finding different ways of thinking about problem solving, text-editing, etc. It's not even about "punishing" those who choose to leave my beautiful world or trying to get them back, it's about setting expectations for those who have never seen it.

What I see quite often is when people profess years of using the tool and finding it unsatisfactory, only to reveal (if ever) that they've engaged with just its surface layer. It's like someone saying they found a piano limiting after years of only using it as a percussion instrument. My point is about embracing Emacs as a Lisp environment from day one - not because everyone must use it this way, but because that's what it actually is. When you treat it as 'just an editor' you're working against its design rather than with it. Those who embrace its programmable nature early often discover possibilities they didn't know they were looking for. Yes, this may mean a slightly steeper upfront commitment, but it also means actually using the tool rather than having square-peg-round-hole mismatched expectations.

It's not about choosing nicer or harsher messaging, it's about the truth, and it comes from the heart - I personally, of course, regret years wasted in vain instead of finding Emacs sooner; it works great for me. I understand it may not work that well for everybody, and I'd rather they realize that sooner, instead of wasting years of their lives. I also spent a long time trying to rationalize Emacs in my own head, because I kept approaching it from the wrong angle, but I am happy I didn't quit. Some people may not have that kind of patience.
by nasretdinov | 2025-08-08 19:22:14+00:00
I think HTTP before HTTP/2 _is_ simple, but it limits its usefulness too, leading to pain when you want to do anything outside of what was included in the initial design.

But, I'd even argue the best version of HTTP is HTTP/1.0 + ability to specify Host: header (many web servers accept it when requesting via HTTP/1.0 even though it's been introduced in 1.1). The later extensions, including HTTP/1.1 are much harder to implement, thus limiting your implementation options and what you can do with it.

In terms of usefulness for the web (browsers) on the other hand HTTP/3 is the best, but it's far from simple and I doubt anyone would call it that too. Version 1.0 was really simple though, and that makes it beautiful
by sjsdaiuasgdia | 2025-08-08 19:22:23+00:00
The GM CEO had presided over a time when GM got into such bad shape they needed a government bailout, and had to come back asking for even more government money.

The Wells Fargo CEO presided over a major scandal involving customers being signed up for services they never agreed to.

What has the Intel CEO presided over during his short tenure that measures up to those?
by DoesntMatter22 | 2025-08-08 19:22:44+00:00
I don't understand co.ments like this. Clearly they realized that it wasn't worth doing and cut costs accordingly. This seems smart. Companies constantly place bets and adapt when they aren't working out.

As far as the ship sinking that's pretty silly. They continue to be profitable with some sectors like energy growing very quickly.

Virtually all EV makers have faced a couple of tough years. Many like Rivian and lucid are getting clobbered.

Tesla has a 37 billion dollar bank account and low debt. There is no scenario that the ship is sinking
by torginus | 2025-08-08 19:22:49+00:00
I just want a model that feels like an extension of me. For example if I there's a task I can describe in one sentence - "add a rest api for user management in the db, and makes sure only users in the admin group are allowed to use it" - would result in an API endpoint that's properly wired up to the right places, and the model does what I tell it, and nothing else, even if it would logically follow from what I told it.

And if it's gets confused, needs clarification, or has its own initative - I want it to stop and ask.

Oh and it needs to be fast it's tokens per minute should be as fast as I can read what it generates (and I can read boilerplate-y code quite fast), and it shouldn't stop and think on every prompt, only when it needs to, and it should be much faster and granular in backtracking.

The loop of waiting on the AI then having to fix and steer it constantly as it doggedly follows its own ideas has really taken the enjoyment out of vibe coding for me.
by jjani | 2025-08-08 19:23:14+00:00
It appears to be overtuned on extremy strict instruction following, interpreting things in a very unhuman way, which may be a benefit to agentic tasks at the costs of everything else.

My limited API testing with gpt-5 also showed this. As an example, the instruction "don't use academic language" caused it to basically omit half of what it output without that instruction. The other frontier models, and even open source Chinese ones like Kimi and Deepseek, understand perfectly fine what we mean by it.
by motorest | 2025-08-08 19:23:37+00:00
> Don't spend your time hunched over servers. Spend it doing things you love with people that matter to you.

To some, spending time hunched over servers is doing things they love.

I mean, each and every thing you said about maintaining a home lab you can also say about maintaining cloud infrastructure.

There was a time when having hobbies was normal. It seems nowadays some people mistake hobbies for work after hours? Where is that hacker mindset?
by neltnerb | 2025-08-08 19:23:45+00:00
That quote was intended to mean --

"artificial" maybe I should have said "synthetic"? I mean the computer can teach itself.

"constrained" the game has rules that can be evaluated

and as to the other -- I don't know what to tell you, I don't think anything I said is inconsistent with the below quotes.

It's clearly not just a generic LLM, and it's only possible to generate a billion training examples for it to play against itself because synthetic data is valid. And synthetic data contains training examples no human has ever done, which is why it's not at all surprising it did stuff humans never would try. A LLM would just try patterns that, at best, are published in human-generated go game histories or synthesized from them. I think this inherently limits the amount of exploration it can do of the game space, and similarly would be much less likely to generate novel moves.

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

> As of 2016, AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. It uses Monte Carlo tree search, guided by a "value network" and a "policy network", both implemented using deep neural network technology.[5][4] A limited amount of game-specific feature detection pre-processing (for example, to highlight whether a move matches a nakade pattern) is applied to the input before it is sent to the neural networks.[4] The networks are convolutional neural networks with 12 layers, trained by reinforcement learning.[4]

> The system's neural networks were initially bootstrapped from human gameplay expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves.[21] Once it had reached a certain degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using reinforcement learning to improve its play.[5] To avoid "disrespectfully" wasting its opponent's time, the program is specifically programmed to resign if its assessment of win probability falls beneath a certain threshold; for the match against Lee, the resignation threshold was set to 20%.[64]
by duxup | 2025-08-08 19:23:46+00:00
The execution and evaluation (granted, that's part of the execution).

If for some reason someone smart (with an education and who did something other than default to praise Trump's ego in press conferences) were to decide we should go to the mattresses wildly with tariffs to fight globalization:

I would hope they would care to actually evaluate if it is working / the impact, and not fire government workers when they report one thing that didn't sound good.

If you're not bound by any results you don't like then you can't know if you're even winning.

Granted ... that might not matter as I suspect market manipulation and bribes is the only real measurement with this administration.
by nickthegreek | 2025-08-08 19:23:55+00:00
> Who is building apps relying on the ChatGPT frontend as a model provider? Apps would normally depend on the OpenAI API, where the models are still available, but GPT-5 is added and cheaper.

Always enjoy your comments dw, but on this one I disagree. Many non-technical people at my org use custom gpt's as "apps" to do some re-occuring tasks. Some of them have spent absurd time tweaking instructions and knowledge over and over. Also, when you create a custom gpt, you can specifically set the preferred model. This will no doubt change the behavior of those gpts.

Ideally at the enterprise level, our admins would have a longer sunset on these models via web/app interface to ensure no hiccups.
by PaulHoule | 2025-08-08 19:23:58+00:00
Personally I feel like the /new page is flooded with "here's a low-effort business idea based on AI, what is your feedback?" or "here's something I vibe coded that almost worked" or "how come vibe coding works when other people do it but it doesn't work when I do it?" or "I never even used GPT-5 but here's my hot take!"

If you had serious plans to make a GPT-6 that's a different thing but we don't want to hear about "me too" ideas unless you've got something to back them up, and probably don't if you do have something to back them up -- I think you'd get better result pitching something AI powered without ever saying "AI".

But seriously, the average post to HN gets exactly 1 vote. Don't take it personally.
by rpcope1 | 2025-08-08 19:23:59+00:00
For anyone thinking of doing this, please please don't. A car battery is probably never a sealed deep cycle battery, and the UPS's charging circuitry is not intended to charge a battery of this size (this is assuming you're using a lead based battery, and not something even more crazy and dangerous like Li-Po or LiFePO4). God forbid you have a cell fail on a car battery and that charger starts cooking the battery. I've had actual car lead acid batteries explode because of poor choices someone else made trying to do something like this, and man when they go, they're dangerous and scary. You really need to pick hardware that's all properly specced and sized for the job...there's a reason Eaton and APC charge what they do.
by mattm | 2025-08-08 19:24:04+00:00
> Sycophancy, in which chatbots agree with and excessively praise users, is a trait they’ve manifested partly because their training involves human beings rating their responses. “Users tend to like the models telling them that they’re great and so it’s quite easy to go too far in that direction”

We've already seen the effects of social media algorithms that route people into isolated bubbles. This is going to exaggerate it.
by __MatrixMan__ | 2025-08-08 19:24:07+00:00
OK, so suppose you and I were bad guys. You work on the code that interfaces with the TPM on a windows device, and I work at an insurance provider and write code that authenticates users.

Suppose we hatch a conspiracy to take our users out of the "consumer synced passkey system". And into one where you can use the authentication ritual as a channel where you can pass me unique bits re: this user such that we can later compare notes about their behavior.

What about passkeys prevents us from doing this? How do we get caught, and by whom?
by resource_waste | 2025-08-08 19:24:28+00:00
GPT5 is some sort of quantized model, its not SOTA.

The trust that OpenAI would be SOTA has been shattered. They were among the best with o3/o4 and 4.5. This is a budget model and they rolled it out to everyone.

I unsubscribed. Going to use Gemini, it was on-par with o3.
by markasoftware | 2025-08-08 19:26:11+00:00
It depends on your threat model. Tor is focused on hiding from small-scale passive adversaries (eg, you're in Iran and don't want the Iranian government to see what you're doing. Or your ISP. Or any single node operator). Even the original Tor paper makes it clear that Tor isn't secure against a "global passive adversary" that can observe a large portion of global internet traffic, like the five eyes likely can today.

If you want to avoid global passive adversaries, a mixnet like Nym can work. I'm also working on a related project which takes a different approach of building your own circuit of proxy servers manually with lots of traffic padding: https://github.com/markasoftware/i405-tunnel
by luke14free | 2025-08-08 19:26:45+00:00
you might want to check out what we built -> https://inference.sh supports most major open source/weight models from wan 2.2 video, qwen image, flux, most llms, hunyan 3d etc.. works in a containerized way locally by allowing you to bring your own gpu as an engine (fully free) or allows you to rent remote gpu/pool from a common cloud in case you want to run more complex models. for each model we tried to add quantized/ggufs versions to even wan2.2/qwen image/gemma become possible to execute with as little as 8gb vram gpus. mcp support coming soon in our chat interface so it can access other apps from the ecosystem.
by Imustaskforhelp | 2025-08-08 19:26:47+00:00
Hm good point. Okay, So if I may ask, What would be the more correct response imo.

Should they have searched it and kept the information to themselves? Or should they have done additional research after asking AI(like looking into its sources) and tried confirming it and actually listing us the sources of their discoveries and then disclose that they used AI.

I generally feel like they wanted to share the information but I mean :/ I'd be actually interested as to what you offer him to do actually if he was really curious and did search chatgpt.

I always feel like knowledge should be open and that just saying that knowledge out loud doesn't hurt but I do agree with your point too wholeheartedly so its nuanced imo.
by DoesntMatter22 | 2025-08-08 19:27:05+00:00
Let's see here. Tesla was the first profitable electric car company. It is still profitable.

Space X is also profitable and growing quickly. Boring company is doing surprisingly well having done the loop in Las Vegas and now doing a big one in Tenn.

Neuralink has 9 working patients at this point who have been helped tremendously.

xAi has the premier model for deep though.

So "failing to produce exceptional results" doesn't seem to add up.

They are smart about shutting down things that won't work. That is smart
by andsoitis | 2025-08-08 19:27:19+00:00
I looked at your Ask HN posts (https://news.ycombinator.com/submitted?id=samehsbs)

I think your expectations of the HN community doesn't quite match what people are primarily on here for. If I may; you're treating it as a way to get early feedback on startup / product ideas, but HN users are primarily here to discuss or debate interesting topics, not to help you get a product off the ground.

Yes, people also provide feedback on products, but usually it is in a later phase of its development and then lands as a Show HN.

If you look at the Ask posts (https://news.ycombinator.com/ask) you will get a sense of what kinds of things resonate.
Ask HN: How can ChatGPT serve 700M users when I can't run one GPT-4 locally?
by superasn | 2025-08-08 19:27:28+00:00
Sam said yesterday that chatgpt handles ~700M weekly users. Meanwhile, I can't even run a single GPT-4-class model locally without insane VRAM or painfully slow speeds.

Sure, they have huge GPU clusters, but there must be more going on - model optimizations, sharding, custom hardware, clever load balancing, etc.

What engineering tricks make this possible at such massive scale while keeping latency low?

Curious to hear insights from people who've built large-scale ML systems.
by dang | 2025-08-08 19:27:32+00:00
(@dang doesn't guarantee that we'll see anything - you need to email hn@ycombinator.com for that)

Users flagged it. We can only guess why users flag things, but in this case there had been so much coverage of GPT-5 on the frontpage, plus the chart gaffe was being extensively in those threads, that they probably found this post some combination of repetitive and unsubstantive.

It ended up spending 14 hours on the frontpage anyhow, which is quite a lot, especially for one of those single-purpose sites that people spin up for joke/drama purposes. Those are a great internet tradition but not always the best fit for HN (https://news.ycombinator.com/newsguidelines.html).
by simonw | 2025-08-08 19:27:36+00:00
It works as a tool. The main model (GPT-4o or GPT-5 or o3 or whatever) composes a prompt and passes that to the image model.

This means different top level models will get different results.

You can ask the model to tell you the prompt that it used, and it will answer, but there is no way of being 100% sure it is telling you the truth!

My hunch is that it is telling the truth though, because models are generally very good at repeating text from earlier in their context.
by frank_nitti | 2025-08-08 19:27:37+00:00
For me, this is essential. On priciple, I won't pay money to be a software engineer.

I never paid for cloud infrastructure out of pocket, but still became the go-to person and achieved lead architecture roles for cloud systems, because learning the FOSS/local tooling "the hard way" put me in a better position to understand what exactly my corporate employers can leverage with the big cash they pay the CSPs.

The same is shaping up in this space. Learning the nuts and bolts of wiring systems together locally with whatever Gen AI workloads it can support, and tinkering with parts of the process, is the only thing that can actually keep me interested and able to excel on this front relative to my peers who just fork out their own money to the fat cats that own billions worth of compute.

I'll continue to support efforts to keep us on the track of engineers still understanding and able to 'own' their technology from the ground up, if only at local tinkering scale
by meta_ai_x | 2025-08-08 19:27:40+00:00
This is especially true since AI is a large multiplicative factor to your productivity.

If Cloud LLMs have 10 IQ points > local LLM, within a month, you'll notice you'll be struggling behind the dude who just used Cloud LLM.

LocalLlama is for hobbies or your job depends on running locallama.

This is not one-time upfront setup cost vs payoff later tradeoff. It is a tradeoff you are making every query which compounds pretty quickly.

Edit : I expect nothing better than downvotes from this crowd. How HN has fallen on AI will be a case study for the ages
by devmor | 2025-08-08 19:28:10+00:00
Not just dangerous, but much less useful in general! Once you are making changes to a large piece of software, the context of your problem grows exponentially and you can provide less and less as summary to the LLM. Of course "prompt engineering" is the art of distilling this context down as accurately as possible, but it becomes a diminishing return in all but the most perfectly architectured, functional solutions where problems are encapsulated.
by b112 | 2025-08-08 19:28:35+00:00
I grew up in a time when listening to an mp3 was too computationally expensive and nigh impossible for the average desktop. Now tiny phones can decode high def video realtime due to CPU extensions.

And my phone uses a tiny, tiny amount of power, comparatively, to do so.

CPU extensions and other improvements will make AI a simple, tiny task. Many of the improvements will come from robotics.
by dkersten | 2025-08-08 19:28:45+00:00
My point is that “AGI has human intelligence” isn’t by itself enough of the equation to know whether there will be exponential or even greater-than-human speed of increase. There’s far more that factors in, including how quickly it can process, the cost of running, the hardware and energy required, etc etc

My point here was simply that there is an economic factor that trivially could make AGI less viable over humans. Maybe my example numbers were off, but my point stands.
by simonw | 2025-08-08 19:29:29+00:00
It's possible you are a victim of bugs in the router, and your test prompts were going to the less useful non-thinking variants.

From Sam's tweet: https://x.com/sama/status/1953893841381273969

> GPT-5 will seem smarter starting today. Yesterday, the autoswitcher broke and was out of commission for a chunk of the day, and the result was GPT-5 seemed way dumber. Also, we are making some interventions to how the decision boundary works that should help you get the right model more often.
by levmiseri | 2025-08-08 19:29:49+00:00
I feel the same way. The initial magic of real-timeness felt like a glimpse into a future that... where is it?

I'm still excited about the prospects of it — shameless plug: actually building a tool with one-of-a-kind messaging experience that's truly real-time in the Google docs collaboration way (no compose box, no send button): https://kraa.io/hackernews
by dmoy | 2025-08-08 19:29:55+00:00
Well there's gonna be people writing code who can't do it in say a high performance C/C++ setup. Not professional programmers, but professional <some finance discipline>.

Sometimes it will be worth the tradeoff to put that person and a programmer together to code up a solution in another language. Sometimes it will be worth it to have the non-programmer write it in Python and then do Herculean things in the background to make it fast enough.
by jaredklewis | 2025-08-08 19:30:04+00:00
The debate over whether something is or is not AGI is entirely semantic and basically uninteresting.

Let’s talk about what LLM agents demonstrably can or cannot do. That’s an interesting discussion and we can design experiments to validate the claims. Comparing how LLMs perform versus humans in various tasks is also interesting and verifiable.

But trying to decide whether LLM agents have crossed over some amorphous, imaginary line with no quantitative definition is just a waste of time. It’s about equally productive as debating the question: “which is more human, an ant eater or a tree shrew?” Like why is there any value in that discussion?