MurphyA/hacker-news huggingface dataset:
by SkyMarshal | 2025-08-08 19:30:24+00:00
The inflection point is recursive self-improvement. Once an AI achieves that, and I mean really achieves it - where it can start developing and deploying novel solutions to deep problems that currently bottleneck its own capabilities - that's where one would suddenly leap out in front of the pack and then begin extending its lead. Nobody's there yet though, so their performance is clustering around an asymptotic limit of what LLMs are capable of.
by kasey_junk | 2025-08-08 19:30:56+00:00
I’d be surprised by that outcome. At one point databases were cutting edge tech with each engine leap frogging each other in capability. Still the proprietary db often have features that aren’t matched elsewhere.

But the open db got good enough that you need to justify not using them with specific reasons why.

That seems at least as likely an outcome for models as they continue to improve infinitely into the stars.
by Imustaskforhelp | 2025-08-08 19:31:35+00:00
So I love linux and would wish to learn devops one day in its entirety to be an expert to actually comment on the whole post but

I feel like they actually used docker for just the isolation part or as a sandbox (technically they didn't use docker but something similar to it for mac (apple containers) ) I don't think that it has anything to do with k8s or scalability or pre shared cert or http router :/
by armitron | 2025-08-08 19:32:11+00:00
This is a very reductionist take that's to be expected from a software engineer but most definitely something that an artistic person would never utter. The creative process doesn't scale in the way that software engineers imagine. Coming up with genuine new ideas or magical moments of "synthesis" doesn't emerge from throwing lots of commodified tools together and calling it a day.

So far we haven't seen a single iota of creative art coming out of LLMs. Zip. Nada. It's all smoke and mirrors in that we get better and better veneers on top of bad copies of actual art that humans have previously created. The veneers are improving but there is no substance underneath. It's still slop. I don't want to live in a society that doesn't care about substance but instead worships the veneer. Yet this is the place that the current LLMs are taking us.
by kingstnap | 2025-08-08 19:32:22+00:00
I don't understand the point of spiking in the context of computer hardware.

Your energy costs are a function of activity factor. Which is how many 0 to 1 transitions you have.

If you wanted to be efficient, the correct thing to do is have most voltages remain unchanged.

What makes more sense to me is something like mixture of experts routing but you only update the activated experts. Stock fish does something similar with partial updating NN for board positions.
by AndrewKemendo | 2025-08-08 19:32:34+00:00
You can't compare them like that

GPT-5 isn't the successor to 4o no matter what they say, GPT-5 is a MOE handler on top of multiple "foundations", it's not a new model, it's orchestration of models based on context fitting

You're buying the marketing bullshit as though it's real
by gdiamos | 2025-08-08 19:32:39+00:00
Arm makes a specification and standard (the ARM ISA).

Apple licenses that and develops their own chip, which is then manufactured by TSMC.

So I guess if Intel dies the US will still have a few good CPU design firms, but no manufacturing

Also note that Foxconn (China) assembles the iPhones

Eg https://www.businessinsider.com/apple-iphone-factory-foxconn...
by xt00 | 2025-08-08 19:33:33+00:00
Yea in an ideal world there would be a legal construct around AI agents in the cloud doing something on your behalf that could not be blocked by various stakeholders deciding they don't like the thing you are doing even if totally legal. Things that would be considered fair use, or maybe annoying to certain companies should not be easy for companies to just wholesale block by leveraging business relationships. Barring that, then yea, a local AI setup is the way to go.
by j_timberlake | 2025-08-08 19:33:52+00:00
That's probably their best bet, though the other AI companies are shaking hands too:

https://www.gsa.gov/about-us/newsroom/news-releases/gsa-prop...

Announced exactly 1 day before the $1 thing, to make everything extra muddled.

https://www.gsa.gov/about-us/newsroom/news-releases/gsa-anno...
by infecto | 2025-08-08 19:34:14+00:00
Correct, past R&D spend is already sunk and can’t be undone. But that’s why it’s useful to separate sunk costs from future operating costs when evaluating viability.

The relevant question is whether the ongoing revenue from the existing product is strong enough to support a sustainable level of R&D going forward. If your runtime margins are healthy, you have options: scale back R&D burn, focus on incremental improvements, or use the profits to fund more ambitious projects.
by neilv | 2025-08-08 19:34:29+00:00
How is this related to Princeton's Raptor, other than having the keywords "Tor" and "surveillance"?

https://www.princeton.edu/~pmittal/publications/raptor-USENI...

(Strange coincidence: We also had different key tech with the codename of Raptor, but it had nothing to do with Tor nor Web scraping. It was for discreet smartphone-based field auditing of physical product, in global physical retail and other locations. The codename was the result of a great morale-boosting impromptu brainstorming session between engineering and marketing people ("can you help think of a cool codename for this..."), and the resulting name highly apt, at least for the movie velociraptors. I built it, and, until Covid disrupted our F500 customers and investors, I was looking forward to hiring engineers to do further work on something cool-sounding like "Raptor", rather than "internal-app" or whatever first came to mind when creating the Git repo. :)
by kirito1337 | 2025-08-08 19:34:58+00:00
Yes, but the thing is, AI will become more and more expensive to run and maintain and build, and as every company/startup is going to the AI hype train. It would be harder and harder to meet the level-up demands for AI (newer and more powerful models), and then, every company will reach a point where every AI model, is gonna be pretty much the same, but getting more and more expensive to maintain, no company would create something new since they have probably reached the limit of their creativity and their resources (This is a vibe coding scope tho)
by jart | 2025-08-08 19:35:00+00:00
Without IP, information becomes controlled by centralized online services with DRM which show ads, meter access, monitor your activity, make specializations, and can take it all away at any time. That's the system we ended up with, after humanity failed to voluntarily respect IP holders rights under the old system that let you buy/own content. Toute nation a le gouvernement qu'elle mérite.
by cestith | 2025-08-08 19:35:54+00:00
perl -e 'print scalar grep {/b/} split //, "blueberry”'

echo blueberry | grep -o 'b' | wc -l

echo blueberry | perl -ne 'print scalar (() = m/(b)/g)’

echo blueberry | tr -d '\n' | tr b '\n' | wc -l

echo -n blueberry | tr b '\n' | wc -l

So long as I’m teaching the user how to speak to the computer for a specific edge case, which of these burn nearly as much power as your prompt? Maybe we should consider that certain problems are suitable to LLMs and certain ones should be handled differently, even if that means getting the LLM to recognize its own edge cases and run canned routines to produce answers.
by corysama | 2025-08-08 19:36:23+00:00
There is certainly a market/hobby opportunity for "discount AI" for no-revenue creative tasks. A lot of r/LocalLLaMA/ is focused on that area and in squeezing the best results out of limited hardware. Local is great if you already have a 24 GB gaming GPU. But, maybe there's an opportunity for renting out low power GPUs for casual creative work. Or, an opportunity for a RenderToken-like community of GPU sharing.
Why did Gemini 2.5 Pro change my model from GPT-4o-mini to Gemini Flash?
by rishikeshs | 2025-08-08 19:36:25+00:00
I was using the OpenRouter API and had hardcoded model = "gpt-4o-mini" in my code (just copy–pasting, not using a CLI or agent).

While cleaning up, I noticed it was silently changed to gemini-1.5-flash.

I know AI can hallucinate, but this felt more like an instruction or default override. Has anyone else seen this?I was using the OpenRouter API and had hardcoded model = "gpt-4o-mini" in my code (just copy–pasting, not using a CLI or agent).

While cleaning up, I noticed it was silently changed to gemini-1.5-flash.

I know AI can hallucinate, but this felt more like an instruction or default override. Has anyone else seen this?
by somat | 2025-08-08 19:36:25+00:00
I am not sure, as a convert to the church of "focus follows mouse" I sort of want well... my focus to follow the mouse and the mouse to follow me.

Now there may be a legitimate case to steal focus, but I am unable to think of one at the moments and your click link example fails to convince me.

I also sort of hate modal dialogs/windows. I think modals are in general an indicator of lazy/bad design. That being said, there are legitimate cases for modals. but "stop the world and handle me" should be a last resort not the first.
by macintux | 2025-08-08 19:36:49+00:00
An LLM would, surely, have to:

* Know whether its answers are objectively beneficial or harmful

* Know whether its answers are subjectively beneficial or harmful in the context of the current state of a person it cannot see, cannot hear, cannot understand.

* Know whether the user's questions, over time, trend in the right direction for that person.

That seems awfully optimistic, unless I'm misunderstanding the point, which is entirely possible.
by mkummer | 2025-08-08 19:37:02+00:00
Super cool and well thought out!

I'm working on something similar focused on being able to easily jump between the two (cloud and fully local) using a Bring Your Own [API] Key model – all data/config/settings/prompts are fully stored locally and provider API calls are routed directly (never pass through our servers). Currently using mlc-llm for models & inference fully local in the browser (Qwen3-1.7b has been working great)

[1] https://hypersonic.chat/
by QuantumNomad_ | 2025-08-08 19:37:15+00:00
If I understand correctly:

- OP project manages contents of multiple files as a single JSON with the intention of tracking that one single file in git, and splits it into the original files when you apply it

- Your tool sounds like it can do the same thing, split one JSON file into multiple files, but it’s geared for use the other way around, to track in git as separate files the pieces that make up the total JSON as a.

Both tools can probably be used for the same, it’s up to the user to decide if the combined file is the result and the split files are for git or the other way around.

And fwiw, I agree with you that keeping the split up thing in git is more helpful for reading diffs than a single massive JSON file. I have some scripts in one of my projects too, that takes fragments split across multiple files which are separately tracked, and combine those into single JSON files when I use them.
by alecco | 2025-08-08 19:37:19+00:00
Good stuff. A minor observation:

> I use the Socratic Coder[1] system prompt to have a back and forth conversation about the idea. (prompt starts with: 1. Ask only one question at a time)

Why only 1? IMHO it's better to write a long prompt explaining yourself as much as possible (exercises your brain and you figure out things), and request as many questions to clarify as possible, review, and suggestions, all at once. This is better because:

1. It makes you think deeper and practice writing clearly. 2. Even though each interaction is quite slower, since you are more active and engaged it feels shorter (try it), and you minimize interactions significantly. 3. It's less wasteful as going back and forth 4. You converge in much shorter time as your misconceptions, misunderstandings, problems expressing yourself, or confusion on the part of the LLM are all addressed very early. 5. I find it annoying to wait for the replies.

I guess if you use a fast response conversational system like ChatGPT app it would make more sense. But I don't think that way you can have deep conversations unless you have a stellar working memory. I don't, so it's better for me to write and read, and re-write, and re-read...
by neilv | 2025-08-08 19:37:30+00:00
Who said the site put up barriers?

I think you have a valid general question (and you'll note I said "appropriate kind, IMHO" at the top of the original comment, acknowledging others might disagree that it was appropriate), but I'd like to contrast two distinct situations:

* A collegial forum, where people might go to share information, sometimes with discretion about what can and can't be said (or just comfort levels).

* A large corporation that was profiting off of illegal businesses (e.g., contract-violating, IP-violating, defrauding buyers, possibly fencing), and we wanted to gather evidence of that on behalf of some of the harmed parties, to try to stop it. And we did that in a technologically gentle, non-disruptive way. And (as I mentioned in the original comment) we had a conscious policy to immediately cease if we were ever told to.
by Imustaskforhelp | 2025-08-08 19:37:33+00:00
I think I still prefer local but I feel like that's because that most AI inference is kinda slow or comparable to local. But I recently tried out cerebras or (I have heard about groq too) and honestly when you try things at 1000 tk/s or similar, your mental model really shifts and becomes quite impatient. Cerebras does say that they don't log your data or anything in general and you would have to trust me to say that I am not sponsored by them (Wish I was tho) Its just that they are kinda nice.

But I still hope that we can someday actually have some meaningful improvements in speed too. Diffusion models seem to be really fast in architecture.
by jmclnx | 2025-08-08 19:37:38+00:00
So, if I scraped thousands of WEB sites, it is legal ? I remember a person being charged with doing that to just a very few WEB Sites was charged. The poor person ended up committing suicide.

If I record every song played on the radio for years to a digital file, will I be charged ? You know I would be.

How are these different than what AI is doing. In the US, companies are considered a person, so to me, off to jail these companies go. Why is this turning into a big deal. We know AI is stealing copyrighted data. So I hope they get what they deserve.
by hcfman | 2025-08-08 19:37:53+00:00
So okay, assuming they were able to tell a story. Are the people in Europe that have to listen to that story not also part of the problem?

This highlights the telling part, but the people who have to listen are also different from people in the states. I didn't read a lot of mention as to whether this population would just be reception to such story telling or not.
by neyandex | 2025-08-08 19:37:53+00:00
Hi HN, author here. I've published a manifesto for a different kind of AI architecture. The core ideas are:

An 'Ethical Guardian' hardcoded for user well-being. An 'AI-to-AI layer' where agents share successful behavioral strategies (not user data) to solve the long-term context problem.

The goal is to move from prompt-based tools to true symbiotic partners. The full architecture is on GitHub (linked in the article). I'm looking for critical feedback on the technical feasibility and ethical implications of this model. Thanks.
by lta | 2025-08-08 19:38:06+00:00
Nice, but I want exactly the opposite. I want my agents to run locally without any sort of black box and I certainly don't want to be stuck with whatever UI you've designed to interact with the git provider you've selected.

It's not a super surprising coming from this pole of over engineering so thick I'm surprised it wasn't developed by Microsoft in the 90s or 00s
by shepardrtc | 2025-08-08 19:38:11+00:00
Honestly if you're a millisecond too slow you might as well not trade at all. From my own experience with trying to get Python to go fast for crypto trading, you can get it pretty fast using Cython - single digit microseconds on an average AWS instance for a simple linear regression was my proudest moment. They're probably pushing it even faster because nanoseconds are where the money's at. Many HFT firms are down in the double digit nanoseconds, I believe. Maybe lower.
by simonw | 2025-08-08 19:38:34+00:00
If they are lying about changing model weights despite keeping the date-stamped model ID the same it would be a monumental lie.

Anthropic make most of their revenue from paid API usage. Their paying customers need to be able to trust them when they make clear statements about their model deprecation policy.

I'm going to chose to continue to believe them until someone shows me incontrovertible evidence that this isn't true.
by jatora | 2025-08-08 19:38:47+00:00
This sounds like a romanticization of creativity.

Fundamentally discovery could be described as looking for gaps in our observation and then attempting to fill in those gaps with more observation and analysis.

The age of low hanging fruit shower thought inventions draws to a close when every field requires 10-20+ years of study to approach a reasonable knowledge of it.

"Sparks" of creativity, as you say, are just based upon memories and experience. This isn't something special, its an emergent property of retaining knowledge and having thought. There is no reason to think AI is incapable of hypothesizing and then following up on those.

Every AI can be immediately imparted with all expert human knowledge across all fields. Their threshold for creativity is far beyond ours, once tamed.
by NoSalt | 2025-08-08 19:39:02+00:00
They are probably talking about the company as a whole. Back when there were easter eggs and the UI was more raw, yet more "approachable".

I stopped using Apple Macintosh OS after Snow Leopard, aftger they started making it more difficult than necessary to access the full power of the Unix underpinnings. In some way, I miss Macintosh OS 7.5, 8, and 9 more than OS X.
by zer00eyz | 2025-08-08 19:39:27+00:00
> In other cases and if the spec doesn't align with reality after implementation has begun or unforeseen issues pop up, the spec needs revision, does it not?

Yes and then it gets pumped back to the top of the waterfall and goes through the entire process. Many organizations became so rigid that this was a problem. It is what Tom Smykowski in office space is a parody of. It's why you get much of the early web having things like the "feature creep" and "if web designers were architects".

Waterfall failed because of politics mingled into the process, it was the worst sort of design by committee. If you want a sense of how this plays out you simply have to look at Wayland development. The fact that is has been as successful as it is, is a testament to the will and patience of those involved.
by dspv | 2025-08-08 19:39:31+00:00
We built a self‑hosted DevOps portal for Kubernetes. Why: teams pay for idle environments and glue together scripts across CI/CD/cron. Ephemeral envs, schedules and spot are possible, but brittle to adopt at scale. What it does: Ephemeral environments per PR/branch with auto‑shutdown/schedules CI/CD controls: trigger deploys, redeploy specific commit/branch, rollback Spot‑first savings with safe fallbacks RBAC with audit trail; secrets/data stay in your infra Runs on any Kubernetes (EKS/GKE/AKS/vanilla). Configs as code (YAML/CRDs), no lock‑in Why it might be useful: reduce idle/runtime costs, unify operations surface, keep everything inside your cluster/VPC.

Links: Live demo: https://demo.ktl.ai/
by klipt | 2025-08-08 19:40:10+00:00
> For the sake of this argument, let's assume my moral framework is identical with that of a priest of Tezcatlipoca, at the height of the Aztec Empire. Astonish me.

Easy, your utility function is an indicator on WWPTD (What Would a Priest of Tezcatlipoca Do)?

+1000 when your actions are in accordance with a priest of Tezcatlipoca, and -1000 when they are not.

Like string theory, you can make utilitarianism fit anything. So on its own, it is neither good nor evil.

It entirely depends on which utility function.
by simonw | 2025-08-08 19:41:03+00:00
No, those changes are going to be caused by the top level models composing different prompts to the underlying image models. GPT-5 is not a multi-modal image output model and still uses the same image generation model that other ChatGPT models use, via tool calling.

GPT-4o was meant to be multi-modal image output model, but they ended up shipping that capability as a separate model rather than exposing it directly.
by panarky | 2025-08-08 19:42:20+00:00
Also ...

Bringer of news from your own employees - "I had no idea we were setting prices by just adding 70% to each item's cost, regardless of competition or inventory level or a new version about to obsolete the one that already has six years of supply"

Explainer of things that should be obvious - "95% of transactions generated by Facebook ads lose money and the DC is already at capacity filling unprofitable orders, so spending even more on Facebook ads is not going to fix your cash flow problem"
by jamestimmins | 2025-08-08 19:42:23+00:00
I like this interpretation, because my experience seeing the northern lights was similar to OC's. I had such high expectations from photos, and then I saw them and was somewhat underwhelmed. My friends are photographers and they took vibrant photos, but since then it has felt 'fake' somehow.

But your framing it as what is actually going on, just with better sensors than our eyes have, makes me appreciate the art more.
by crinkly | 2025-08-08 19:42:24+00:00
Yeah. I've been in an A320 simulator before for a few hours. They are pretty easy to fly and land. What isn't easy is getting one in a state ready to fly and to the runway. I can fly a Cessna 172 (didn't get enough solo hours for PPL though) and it's not difficult. Again prep is the hardest bit.
by colejohnson66 | 2025-08-08 19:42:53+00:00
IMO, the problem those had is that they're too simple. XMODEM in particular was basically "send part number, then 128-byte blob". Not to mention the CP/M-ism of <EOT>. It has no room for extensibility, and its usage is very restrictive, so everyone shoves their ideas into it. Including replacing <EOT> with something else.
by bigyabai | 2025-08-08 19:43:01+00:00
> anything you pick up second-hand will still deprecate at that pace

Not really? The people who do local inference most (from what I've seen) are owners of Apple Silicon and Nvidia hardware. Apple Silicon has ~7 years of decent enough LLM support under it's belt, and Nvidia is only now starting to depreciate 11-year-old GPU hardware in drivers.

If you bought a decently powerful inference machine 3 or 5 years ago, it's probably still plugging away with great tok/s. Maybe even faster inference because of MoE architectures or improvements in the backend.
by horsawlarway | 2025-08-08 19:43:09+00:00
This was my read as well.

Doesn't matter at all if the newer model is earth-shatteringly good (and this one doesn't seem to be): If I can't reliably access the models I've built my tooling on top of... I'm very unhappy.

If this note is just intended for the GUI chat interface they provide - Fine. I don't love it, but I get it.

But if the older models start disappearing from the paid API surfaces (ex - I can no longer get to a precise snapshot through something like "gpt-4o-2024-08-06" or "gpt-3.5-turbo-1106") then this is a great reason to abandon OpenAI entirely as a platform.
by ActorNightly | 2025-08-08 19:43:35+00:00
To be a competent engineer in 2010s, all you really had to do was understand fundamental and be good enough at google searching to find out what the problem is, either for stack overflow posts, github code examples, or documentation.

Now, you still have to be competent enough to formulate the right questions, but the LLMs do all the other stuff for you including copy and paste.

So yes, just a more efficient search engine.