MurphyA/hacker-news huggingface dataset:
by tsvetkov | 2025-08-07 22:12:02+00:00
Sure, but monitoring, reviewing and steering does not really require modern IDEs in their current form. Also, I'm sure agents can benefit from parts of IDE functionality (navigation, static analysis, integration with build tools, codebase indexing, ...), but they sure don't need the UI. And without UI those parts can become simpler, more composable and more portable (being compatible with multiple agent tools). IMO another way to think about CLI agentic coding tools as of new form of IDEs.
by m4x | 2025-08-07 22:12:20+00:00
It's quite possible that the models from different companies are clustering together now because we're at a plateau point in model development, and won't see much in terms in further advances until we make the next significant breakthrough.

I don't think this has anything to do with AGI. We aren't at AGI yet. We may be close or we may be a very long way away from AGI. Either way, current models are at a plateau and all the big players have more or less caught up with each other.
by habryka | 2025-08-07 22:12:27+00:00
My guess is not much? Because we are doing rolling applications, so we are somehow trying to judge how many good applicants in total we are going to get (classical secretary problem). Applying early means we might let you in with a lower bar if we end up getting a lot of great applications later and raise our bar. Applying later might be better if we realize we were overly conservative in the beginning and are disappointed in the later applications.

Thinking about it, my guess is we will probably let promising people who applied early know that they are on some kind of waitlist and extend an invite to them if we end up disappointed with the later applications, so if you are flexible, I think that makes early strictly easier. I don't expect the effect to be that large though.
by mallets | 2025-08-07 22:13:01+00:00
Their US facilities are purposefully NOT bleeding-edge, and also cost more.

And this version of Intel can't, maybe a different one that fully separates design and foundry could. The best solution long-term would be to directly subsidize foundries, with the right incentives to acquire different customers (how TSMC or Samsung operate). They just might get lucky if everyone hits a hard scaling wall.

Whatever happens, tariffs and bans are the absolute worst incentives for innovation and growth.
by loudmax | 2025-08-07 22:13:45+00:00
The short answer is that the best value is a used RTX 3090 (the long answer being, naturally, it depends). Most of the time, the bottleneck for running LLMs on consumer grade equipment is memory and memory bandwidth. A 3090 has 24GB of VRAM, while a 5080 only has 16GB of VRAM. For models that can fit inside 16GB of VRAM, the 5080 will certainly be faster than the 3090, but the 3090 can run models that simply won't fit on a 5080. You can offload part of the model onto the CPU and system RAM, but running a model on a desktop CPU is an enormous drag, even when only partially offloaded.

Obviously an RTX 5090 with 32GB of VRAM is even better, but they cost around $2000, if you can find one.

What's interesting about this Strix Halo system is that it has 128GB of RAM that is accessible (or mostly accessible) to the CPU/GPU/APU. This means that you can run much larger models on this system than you possibly could on a 3090, or even a 5090. The performance tests tend to show that the Strix Halo's memory bandwidth is a significant bottleneck though. This system might be the most affordable way of running 100GB+ models, but it won't be fast.
by mindcrime | 2025-08-07 22:13:55+00:00
For anyone who's curious, the closest equivalent in the US is P25[1] or "Project 25". And if you're wondering, yes, P25 systems have been known to have their own share of vulnerabilities of various sorts. My favorite one[2] is the one that lets an attacker force a P25 radio to broadcast tokens "on demand" allow you to (theoretically, with the right receiving setup and software) track the location of P25 radios more or less in real-time.

And on a related note, for anyone who is interested in listening in on any local P25 transmissions, you can do so in a fairly inexpensive manner, using an RTL-SDR dongle and the Open Source op25[3] software package. No listening to encrypted traffic, but IME, many (maybe most) public safety agencies keep most of their traffic in the clear. More so for fire/ems traffic. Law enforcement is more likely to be encrypted, but even then, I find that many jurisdictions only encrypt a small number of channels, like maybe a dedicated vice/narc squad channel, SWAT team channel, etc. General LE dispatch and tac channels are still in the clear in many areas.

[1]: https://en.wikipedia.org/wiki/Project_25

[2]: https://www.reddit.com/r/tacticalgear/comments/1f4d5dr/psa_p...

[3]: https://github.com/boatbod/op25
by mikepurvis | 2025-08-07 22:14:20+00:00
I guess at a certain point you get into the philosophy of what it even means to be novel or test for novelty, but to give a concrete example, I'm in DevOps working on build pipelines for ROS containers using Docker Bake and GitHub Actions (including some reusable actions implemented in TypeScript). All of those are areas where ChatGPT has lots that it's learned from, so maybe me combining them isn't really novel at all, but like... I've given talks at the conference where people discuss how to best package and ship ROS workspaces, and I'm confident that no one out there has secretly already done what I'm doing and Chat is just using their prior work that it ingested at some point as a template for what it suggests I do.

I think rather it has a broad understanding of concepts like build systems and tools, DAGs, dependencies, lockfiles, caching, and so on, and so it can understand my system through the general lens of what makes sense when these concepts are applied to non-ROS systems or on non-GHA DevOps platforms, or with other packaging regimes.

I'd argue that that's novel, but as I said in the GP, the more important thing is that it's also how a human approaches things that to them are novel— by breaking them down, and identifying the mental shortcuts enabled by abstracting over familiar patterns.
by frank_nitti | 2025-08-07 22:15:04+00:00
I’ve mostly used them for laughs with my friends. Sometimes generating “custom” songs with funny lyrics, but most fun so far is editing lyrics of existing songs to say ridiculous things for fun.

No real clue how someone would use them for a more serious endeavor, only thing I could imagine would be to quickly iterate/prototype with song structures on a fixed seed to generate ideas for a real composition. Consider the case of an indie game developer or film maker getting some placeholder music to test the experience during early throwaway iterations.
by zamadatix | 2025-08-07 22:15:08+00:00
This reminds me how, a few years after the first fission power plant, Teller, Bhaba, and other nuclear physicists of the 1950s were convinced fusion power plants were about as far away as the physicists of today still predict they are.

I'm cautiously optimistic of each technology, but the point is it's easy to find bullshit predictions without actually gaining any insight into what will happen with a given technology.
by slowmovintarget | 2025-08-07 22:15:47+00:00
Yes, actually.

Fewer fingers on the scale means the LLM gets to actually do its thing. GPT-4 with zero filtering was scary smart according to the red teams that were testing it. The version the public got had a lobe tied behind it's back.

Having only Grok 3 to compare, and toying around with GPT-5... GPT-5 is pretty good.
by novok | 2025-08-07 22:15:48+00:00
I think that is because you do implicit plan tracking, creation and modification of the plan in your head in light of new information and then follow that plan. I'm not sure these tools do that very well.

The long running task, at it's core, is composed of many smaller tasks and you mostly focus on one task at a time per brain part. It's why you cannot read two streams of text simultaneously even if both are in your visual focus field.
by sneak | 2025-08-07 22:16:14+00:00
I think what you may be thinking of is the export from the US of strong encryption products under ITAR. It was challenged by djb (of qmail/djbdns fame, among many other things) and the result was roughly that publishing software is protected expression like any other publishing (prior to that it was classified as munitions and required an export license).

https://en.wikipedia.org/wiki/Bernstein_v._United_States
by godelski | 2025-08-07 22:16:28+00:00
There were also several hallucinations during the announcement. (I also see hallucinations every time I use Claude and GPT, which is several times a week. Paid and free tiers)

So not seeing them means either lying or incompetent. I always try to attribute to stupidity rather than malice (Hanlon's razor).

The big problem of LLMs is that they optimize human preference. This means they optimize for hidden errors.

Personally I'm really cautious about using tools that have stealthy failure modes. They just lead to many problems and lots of wasted hours debugging, even when failure rates are low. It just causes everything to slow down for me as I'm double checking everything and need to be much more meticulous if I know it's hard to see. It's like having a line of Python indented with an inconsistent white space character. Impossible to see. But what if you didn't have the interpreter telling you which line you failed on or being able to search or highlight these different characters. At least in this case you'd know there's an error. It's hard enough dealing with human generated invisible errors, but this just seems to perpetuate the LGTM crowd
by Buttons840 | 2025-08-07 22:16:34+00:00
The idea of singularity--that AI will improve itself--is that it assumes intelligence is an important part of improving AI.

The AIs improve by gradient descent, still the same as ever. It's all basic math and a little calculus, and then making tiny tweaks to improve the model over and over and over.

There's not a lot of room for intelligence to improve upon this. Nobody sits down and thinks really hard, and the result of their intelligent thinking is a better model; no, the models improve because a computer continues doing basic loops over and over and over trillions of times.

That's my impression anyway. Would love to hear contrary views. In what ways can an AI actually improve itself?
by AlotOfReading | 2025-08-07 22:16:40+00:00
They tried to define what they mean by technology [1], but they seemingly gave up on it partway through. Had they followed it consistently, they would have excluded certain cultural-practice-based technologies like nixtamalization that made the list.

The inconsistent definition and the pretty large gaps leads to a lot of oddness. Just look at how sparse anything related to textiles is. "Clothing" just gets one "invention" in 168k B.P., even though a t-shirt and an arctic jacket are obviously very different technologies. New world agriculture is similarly strange. Nodes appear from nowhere and lead nowhere, presumably because there are implicit "nature" edges they didn't want to represent as technology.

[1] https://www.hopefulmons.com/p/what-counts-as-a-technology
by mikepurvis | 2025-08-07 22:17:12+00:00
But wouldn't that novel query optimization still be explained somewhere in a paper using concepts derived from an existing body of work? It's going to ultimately boil down to an explanation of the form "it's like how A and B work, but slightly differently and with this extra step C tucked in the middle, similar to how D does it."

And an LLM could very much ingest such a paper and then, I expect, also understand how the concepts mapped to the source code implementing them.
by belter | 2025-08-07 22:17:34+00:00
AGI needs to genuinely learn and build new knowledge from experience, not just generate creative outputs based on what it has already seen.

LLMs might look “creative” but they are just remixing patterns from their training data and what is in the prompt. They cant actually update themselves or remember new things after training as there is no ongoing feedback loop.

This is why you can’t send an LLM to medical school and expect it to truly “graduate”. It cannot acquire or integrate new knowledge from real-world experience the way a human can.

Without a learning feedback loop, these models are unable to interact meaningfully with a changing reality or fulfill the expectation from an AGI: Contribute to new science and technology.
by ZigFisher | 2025-08-07 22:18:21+00:00
Sorry, you are slightly mistaken and mislead the colleagues present here.

The OpenIPC project is completely open wherever possible, but it allows the use of various streamers Divinus/Majestic/Mini/Venc/other and various binary drivers and libraries if the chip manufacturer does not provide open source code.

The Divinus streamer is a great alternative to Majestic and it is open and also part of the OpenIPC ecosystem - https://github.com/openipc/divinus
by kazinator | 2025-08-07 22:18:30+00:00
The bouncing behavior itself adds delay. If it takes 30 ms for the switch to settle to a state where it is considered closed according to the debouncing algorithm's parameters, then that's what it is. The algorithm might be "sample every millisecond, until 9 out of 10 samples show "closed". That imposes a minimum delay of 10 ms, and the maximum is whatever it takes for that criterion to be reached.
by funnym0nk3y | 2025-08-07 22:18:54+00:00
There are so many misconceptions about lithium wrt to the human body.

I don't know why so many people differentiate between lithium orotate and the lithium carbonate in psychiatry. Although they differ in absorption the active component is the lithium ion in both cases. Dosage is done according to lithium content, there are tables for converting from orotate to carbonate and back.

Then the effects of lithium orotate and carbonate can't be that different. And thus, above a particular dose blood monitoring is mandatory.

There are benefits of low dose lithium for sure. And the dosages in psychiatry have been on a steady decline. With lower doses come less side effects. It is definitly not the hammer of psychiatry that turns people into zombies or messes. It feels quite natural.

In addition the reduction of Alzheimers cases is not unique to lithium. Many meds cause Alzheimers rates in mentally ill people to decline to general population levels.
by runarberg | 2025-08-07 22:20:30+00:00
I feel like technological singularity has been pretty solidly ruled as junk science, like cold fusion, Malthusian collapse, or Lynn’s IQ regression. Technologists have made numerous predictions and hypothetical scenarios, non of which have come to fruition, nor does it seem likely at any time in the future.

I think we should be treating AGI like Cold Fusion, phrenology, or even alchemy. It is not science, but science fiction. It is not going to happen and no research into AGI will provide anything of value (except for the grifters pushing the pseudo-science).
by jhickok | 2025-08-07 22:20:52+00:00
It annoys me too because as someone that jumps around to the different models and the subscriptions, when I see that it says it's available to everyone I paid the money for the subscription only to find out that apparently it's rolling out in some manner of priority. I would very much have liked a quick bit of info that "hey, you wont be able to give this a try since we are prioritizing current customers".
by historyspwn | 2025-08-07 22:21:17+00:00
No one has so far suggested the obvious: it's a stunt to get people talking, checking the website, brushing up their Constitutional knowledge, foreshadowing an upcoming state of emergency. Possibly those who arranged such a stunt have advance warning and foresee a near-future need for a spot of civics education. Make a mental note of the specific rights allegedly "deleted" in case they turn out to be pertinent. Then, just a suggestion, give chapters 6, 8, and 13 of the book of Revelation a glance for examples of conditions where such rights might be suspended.
by CLPadvocate | 2025-08-07 22:22:13+00:00
I think your confusion results from the fact that "Palestinians" was the name used to call the Jews basically for nearly two thousand years, until the early 20th century. And, yes, they have been ethnically cleansed by arabs basically since the arab invasion in the 7th century.

The arabs themselves never wanted to be called Palestinians - they used the name "Syrian Arabs". Yassir Arafat, former leader of the PLO, was one of the most prominent opponents of the word "Palestinians" with regard to the arab population - he only changed his mind as Jordan and Egypt signed a peace treaty with Israel, because he wanted to distance himself from these "traitors".

One of the problems he (and also most arabs) had with the word "Palestina" - it has exactly ZERO relationship to arabs and to Arabic languages. It's a greek word with actually Hebrew roots. It means "land of the Philistines" - and the Philistines, according to many historians, are a mixed group of people, mostly from western Mediterranean (modern-day Italy, France, Spain, northern Africa), also called "the sea people", or, in other words - pirates. The Romans introduced this name to the region to wipe out the original names Judea and Samaria (which IS actually a sign of a genocide).
by BryantD | 2025-08-07 22:22:25+00:00
Very good article.

As someone who is currently on GLP-1 drugs, I will say that if I didn't need to be on them to control my diabetes, I would seriously consider stopping. I am undoubtedly healthier and have lost substantial weight, but the effects on my quality of life are substantial -- I simply don't enjoy food right now. This is in part psychological, because what I used to enjoy was the psychological effect of eating, but not completely. I hope this will ease out over time and if it doesn't, well, I'll cope.

I'm still very interested in the ongoing research.
by majewsky | 2025-08-07 22:22:55+00:00
To make the language easier to learn. Lots of languages go through orthographic reforms from time to time, English being one of the notable exceptions because there is no central authority that could impose rule changes in a way that would ensure that most language users eventually fall in line.

I entered school in Germany the very same year that the orthographic reform came into force, so I never learned the legacy spelling, but I certainly found it weird how much adult people at the time detested the rules that six-year-old me considered to be very reasonable (esp. the ss/ß reordering and the ban on fusing tripled consonants in compound words).

This is my very personal perspective. If you're interested in a more complete picture, https://de.wikipedia.org/wiki/Reform_der_deutschen_Rechtschr... looks like a good summary (through translation if necessary).
by rcarr | 2025-08-07 22:23:08+00:00
Seems like VSCode just added a lot of stuff for this in the latest update today, such as worktree support[1] and an agent session mode[2].

[1]: https://code.visualstudio.com/updates/v1_103#_git-worktree-s...

[2]: https://code.visualstudio.com/updates/v1_103#_chat-sessions-...
by timr | 2025-08-07 22:23:14+00:00
The fact that you have to invoke integrals and the Kutta condition to make your explanation is exactly what is wrong with it.

Is it correct? Yes. Is it intuitive to someone who doesn’t have a background in calculus, physics and fluid dynamics? No.

People here are arguing about a subpoint on a subpoint that would maybe get you a deduction on a first-year physics exam, and acting as if this completely invalidates the response.
by rsoto2 | 2025-08-07 22:23:36+00:00
Fear the imbeciles that capitalism empowers. The same ones that are going to implode the market on this nonsense while they push native people out to build private islands in Hawaii.

Thiel is a literal vampire(disambiguation: infuses young blood) and has already built drones in which bad AI targeting is a feature. They will kill us all and the planet.
by lamontcg | 2025-08-07 22:23:37+00:00
It still can't actually reason, LLMs are still fundamentally madlib generators that produce output that statistically looks like reasoning.

And if it is trained on both sides of the airfoil fallacy it doesn't "know" that it is a fallacy or not, it'll just regurgitate one or the other side of the argument based on if the output better fits your prompt in its training set.
by echoangle | 2025-08-07 22:23:55+00:00
I agree that this is kind of true with a plain chat interface, but I don’t think that’s an inherent limit of an LLM. I think OpenAI actually has a memory feature where the LLM can specify data it wants to save and can then access later. I don’t see why this in principle wouldn’t be enough for the LLM to learn new data as time goes on. All possible counter arguments seem related to scale (of memory and context size), not the principle itself.

Basically, I wouldn’t say that an LLM can never become AGI due to its architecture. I also am not saying that LLM will become AGI (I have no clue), but I don’t think the architecture itself makes it impossible.
by Buttons840 | 2025-08-07 22:24:19+00:00
Polymarket betters are not impressed. Based upon the market odds, OpenAI had a 35% chance to have the best model (at year end), but those odds have dropped to 18% today.

(I'm mostly making this comment to document what happened for the history books.)

https://polymarket.com/event/which-company-has-best-ai-model...
by ummonk | 2025-08-07 22:24:54+00:00
The Kutta condition is insufficient to describe lift in all regimes (e.g. when the trailing edge of the wing isn't that sharp), but fundamentally you do need to fall back to certain 2nd law / boundary condition rules to describe why an airfoil generates lift, as well as when it doesn't (e.g. stall).

There's nothing in the Navier-Stokes equations that forces an airfoil to generate lift - without boundary conditions the flowing air could theoretically wrap back around at the trailing edge, thus resulting in zero lift.
by bcherry | 2025-08-07 22:25:09+00:00
yeah i think they shot themselves in the foot a bit here by creating the o series. the truth is that GPT-5 _is_ a huge step forward, for the "GPT-x" models. The current GPT-x model was basically still 4o, with 4.1 available in some capacity. GPT-5 vs GPT-4o looks like a massive upgrade.

But it's only an incremental improvement over the existing o line. So people feel like the improvement from the current OpenAI SoTA isn't there to justify a whole bump. They probably should have just called o1 GPT-5 last year.
by funnym0nk3y | 2025-08-07 22:25:16+00:00
Lithium is far from the drug with the highest amount of side effects among psych meds. Take the first generation antipsychotics for example. They are nasty.

As a matter of fact, lithium patients aren't much worse off when it comes to kidney function. Especially with modern levels of around 0.6 to 0.8 mmols.

Lithium has many modes of action, ion channels like you said, but also GSK3 function, BDNF changes and many more. It even changes the DNA methylation.
by csours | 2025-08-07 22:25:26+00:00
> Nobody can say for sure whether the AI boom is evidence of the next Industrial Revolution or the next big bubble. All we know is that it’s happening.

In hindsight, it will be clear, and future generations (if any exist) will ask: "Why didn't you understand what was happening at the time"

My answer: Noise. Just because you can find someone who wrote down the answer at the time, doesn't mean that they really understood the answer, at least not to the extent that we will understand with hindsight.

Future history is contingent.
by cheema33 | 2025-08-07 22:25:30+00:00
My first thought was, "meh, I already have Claude Code". But then I remembered my primary frustration with Claude Code. I need other LLMs to be able to validate Claude Code's assumptions and work. I need to do this in an automated way. Before Cursor CLI, I did not have a way to programmatically ask Cursor do this. It was very manual, very painful. But, now I can create a Claude Code agent that is a "cursor-specialist" that uses cursor cli to do all of that in an automated way.