MurphyA/hacker-news huggingface dataset:
by 1zael | 2025-08-24 04:04:21+00:00
Congratulations, you replace my pile of "slop" (which really is functional, tight code written by AI in 1/1000th of the time it would take me to write it) with your "shorter" code that has the exact same functionality and performance. Congrats? The reality is no one (except in the case of like competitive programming) cares about the length of your code so long as it's maintainable.
by manquer | 2025-08-24 04:04:27+00:00
Not necessarily , for a given definition of AGI you could have mathematical proof that it is incomputable similar to how Gödel incompleteness theorems work .

It need not even be incomputable, it could be NP hard and practically be incomputable, or it could be undecidable I.e. a version of the halting problem.

There are any number of ways our current models of mathematics or computation can in theory could be shown as not capable of expressing AGI without needing a fundamental change in physics
by arp242 | 2025-08-24 04:04:36+00:00
The actual text is "Bans the worst beach‑litter plastics (straws, cutlery, sticks) and cuts pollution" and the tooltip says "Targets the most littered plastic items with bans, design and collection rules, and extended producer responsibility to clean up coasts and waterways."

I looked a bit further, it bans a long list of plastic single-use stuff: plates, cutlery, certain food containers, certain cups, and a bunch of other things. It also regulates some labelling for other single-use products.

It claims that "80 to 85% of marine litter, measured as beach litter counts, is plastic, with single-use plastic items representing 50% and fishing-related items representing 27%".

Saying it's just a "plastic straw ban is" ... eh, well, a straw man. And single-use plastics are a substantial source of litter/pollution (I didn't investigate the accuracy of this claim in-depth).

In conclusion, this seems about as accurate and good faith as the ol' "EU bendy banana myth".
by AndyKelley | 2025-08-24 04:04:59+00:00
It's unfortunate that the original authors of this interface didn't understand how important infallibility is to resource deallocation, and it's unfortunate that NFS authors didn't think carefully about this at all, but if you follow the advice of the text you pasted and read the section about how you can't retry close() after an error, it is clear that close is, in fact, a fundamentally infallible operation.
by wkat4242 | 2025-08-24 04:05:11+00:00
Well if they had followed the news they would have already thought twice about everything they said and did since Snowden showed the entire US population has been under constant surveillance for decades now. At the time most people didn't quite care because their country "did this in their best interest and it would never turn authoritarian". Yay land of the free.

How much of that surveillance was kept I wonder? I don't consider it unimaginable that someone would get expelled for saying something on WhatsApp 15 years ago.
by westurner | 2025-08-24 04:05:26+00:00
From the linked LKML post:

> To demonstrate this framework's viability, support for KFuzzTest has been prototyped in a development fork of syzkaller, enabling coverage-guided fuzzing. To validate its end-to-end effectiveness, we performed an experiment by manually introducing an off-by-one buffer over-read into [...]

"kfuzztest: a new kernel fuzzing framework" (2025-08) https://lwn.net/Articles/1033619/
by joe_the_user | 2025-08-24 04:05:55+00:00
So the "Bitter Lesson" paper actually came up recently and I was surprised to discover that what it claimed was sensible and not at "all you need is data" or "data is inherently better"

The first line and the conclusion is: "The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin." [1]

I don't necessary agree with it's examples or the direction it vaguely points at. But it's basic statement seems sound. And I would say that there's lot of opportunity for engineer, broadly speaking, in the process of creating "general methods that leverage computation" (IE, that scale). What the bitter lesson page was roughly/really about was earlier "AI" methods based on logic-programming and which including information on the problem domain in the code itself.

And finally, the "engineering" the paper talks about actually is pro-Bitter lesson as far as I can tell. It's taking data routing and architectural as "engineering" and here I agree this won't work - but for the opposite reason - specifically 'cause I don't just data routing/process will be enough.

[1]https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...
by 1zael | 2025-08-24 04:07:15+00:00
Answered above, but to be concrete on features --> it helped me build an end-to-end multi-stage pipeline architecture for video and audio transcription, LLM analysis, content generation, and evals. It took care of stuff like Postgres storage and pgvector for RAG-powered semantic search, background job orchestration with intelligent retry logic, Celery workers for background jobs, and MCP connectors.
by tonetegeatinst | 2025-08-24 04:07:53+00:00
This is nothing new though.

Plenty pretend semiconductors or programming is a non political field where the science and creativity are rewarded, but the truth is both technology are "dual use".

I think programming or people who do a lot of work in cryptography and signals intelligence would know this all to well. What can be seen as scientific or academic curiosity can be seen as a threat to the establishment or a potential tool that the MIC can use.

Hell part of the reason semiconductors have come so far is due to how close they tie into defense and intelligence.

The government has long created rules it imposed on researchers such as the export restrictions on pgp, or on DVD keys, or prosecution of security researchers. This is nothing new for those who have been aware of the general trends.
by zonkerdonker | 2025-08-24 04:08:00+00:00
Agreed that more studies are required. https://pubmed.ncbi.nlm.nih.gov/25705824/ This and some others studies correlate a 40% reduction in all cause mortality (that is, 40% reduction in risk of dying from anything) with sauna use. Even if the correlation is proven to be weak, still seems worth it to get a shvitz whenever you can.
by giancarlostoro | 2025-08-24 04:08:14+00:00
> This entire experiment took place on a Windows 10 PC, driven by a specific question: what if you could have the best of both worlds? The idea of a full Linux environment running inside a Docker container, side-by-side with my standard Windows applications, was too intriguing to pass up. No reboots, no separate partitions—just a seamless, containerized Linux desktop.

> No reboots, no separate partitions—just a seamless, containerized Linux desktop.

> Windows 10 PC .... No reboots

You're kidding right? Linux is no reboots, Windows... Well let's just say yet another reason I no longer daily Windows.
by vivzkestrel | 2025-08-24 04:08:23+00:00
You won't have to sit and do any of this manually once my production grade python application generator goes live. At the moment it works with poetry and i am working on a uv version of it. Let me explain what the poetry one does

- Setup a docker container - Install python 3.x (specified by you) - Install poetry x.y (specified by you) - Setup README - Setup gitignore - Install black, isort, flake8, tox, pre-commit, mypy, commitizen, darglint, xdoctest, pytest with src and tests directory with a lot of inputs from you with pinned dependencies - Add nginx / caddy / traefik with Github Actions CI / CD and templates for pull requests, issues, feature requests, questions etc - Verify everything works by running all commands in docker - Give you a downloadable version of this production grade python application - As dependencies update, this whole pipeline updates immediately
by gus_massa | 2025-08-24 04:08:26+00:00
Previous discussions last month:

https://news.ycombinator.com/item?id=44658027 (47 points | 31 days ago | 43 comments)

https://news.ycombinator.com/item?id=44592303 (24 points | 37 days ago | 10 comments)

Is there any mayor update?

What is GIF_MODE_TURBO ? I don't remember that in the specification, but I read it a very long time ago. Someone commented that it didn't compile when GIF_MODE_TURBO was undefined. Is that fixed?
by BenFranklin100 | 2025-08-24 04:09:50+00:00
This guys gets it wrong too. It’s not even an engineering problem. It’s much worse: it’s a scientific problem. We don’t yet understand how the human brain operates or what human intelligence really is. There’s nothing to engineer as the basic specifications are not yet available on what needs to be built.

Will AGI require ‘consciousness’, another poorly understood concept? How are mammalian brain even wired up? The most advanced model is the Allen Institute’s Mesoscale Connectivity Atlas which is at best a low resolution static roadmap, not a dynamic description of how a brain operates in real time. And it describes a mouse brain, not a human brain which is far, far more complex, both in terms of number of parts, and architecture.

People are just finally starting to acknowledge LLMs are dead ends. The effort expended on them over the last five years could well prove a costly diversion along the road to AGI, which is likely still decades in the future.
by tomrod | 2025-08-24 04:10:17+00:00
Aye. Missing are self correction (world models/action and response observation), coherence over the long term, and self-scaling. The 3rd are what all the SV types are worried about, except maybe Yann LeCun who is worried about the first and second.

Hinton thinks the 3rd is inevitable/already here and humanity is doomed. It's an odd arena.
by crystal_revenge | 2025-08-24 04:10:35+00:00
The magical thinking around LLMs is getting bizarre now.

LLMs are not “intelligent” in any meaningful biological sense.

Watch a spider modify its web to adapt to changing conditions and you’ll realize just how far we have to go.

LLMs sometimes echo our own reasoning back at us in a way that sounds intelligent and is often useful, but don’t mistake this for “intelligence”
by shreyas056 | 2025-08-24 04:11:57+00:00
>Now, I'm not blaming developers. Modern software engineering is built on abstraction layers, and that's actually amazing! We've gone from assembly language to high-level frameworks, from bare metal to cloud-native platforms. A developer can slap a @RestController annotation on a Java class and magically have an HTTPS endpoint without knowing anything about TLS handshakes or certificate chains.

Well, you should blame the developer if they don't know the basics of computer science (TLS handshake being the basics)
by hoyo1s | 2025-08-24 04:12:25+00:00
Yes, and I think this is often a problem on the power generation side. The damn cost of energy storage and photovoltaics has been falling rapidly, hydropower and wind power costs are not high at all.

The key is to take advantage of economies of scale: The cost of renewable energy generation is mainly in the initial investment and equipment.

As long as you mass-produce enough equipment, the cost of each device will decrease due to economies of scale. However, thermal power generation is different. The cost of thermal power generation is mainly fuel, and the lower limit of the cost is much higher than photovoltaic power generation.

I don’t understand why so many people are obsessed with using fossil fuels for power generation, as if it is really more efficient... thermal power no longer has a price advantage a few years ago.
by cakealert | 2025-08-24 04:12:50+00:00
There is a reason why LLM's are architected the way they are and why thinking is bolted on.

The architecture has to allow for gradient descent to be a viable training strategy, this means no branching (routing is bolted on).

And the training data has to exist, you can't find millions of pages depicting every thought a person went through before writing something. And such data can't exist because most thoughts aren't even language.

Reinforcement learning may seem like the answer here: bruteforce thinking to happen. But it's grossly sample-inefficient with gradient descent and therefore only used for finetuning.

LLM's are regressive models and the configuration that was chosen where every token can only look back allows for very sample-efficient training (one sentence can be dozens of samples).
by rsync | 2025-08-24 04:13:09+00:00
magick convert IMG_1111.HEIC -strip -quality 87 -shave 10x10 -resize 91% -attenuate 1.1 +noise Uniform out.jpg

This will strip ALL exif metadata, change the quality, shave 10 pixels off each edge just because, resize to xx%, attenuate, and adds noise of type "Uniform".

Some additional notes:

- attenuate needs to come before the +noise switch in the command line

- the worse the jpeg quality figure, the harder it is to detect image modifications[1]

- resize percentage can be a real number - so 91.5% or 92.1% ...

[1] https://fotoforensics.com/tutorial.php?tt=estq
by tomrod | 2025-08-24 04:13:15+00:00
I mean, I'm conscious to a degree, and can alter that state through a number of activities. I can't speak for you or Metzinger ;).

But seriously, I get why free will is troubleaome, but the fact people can choose a thing, work at the thing, and effectuate the change against a set of options they had never considered before an initial moment of choice is strong and sufficient evidence against anti free will claims. It is literally what free will is.
by starchild3001 | 2025-08-24 04:14:13+00:00
I think this essay lands on a useful framing, even if you don’t buy its every prescription. If we zoom out, history shows two things happening in parallel: (1) brute-force scaling driving surprising leaps, and (2) system-level engineering figuring out how to harness those leaps reliably. GPUs themselves are a good analogy: Moore’s Law gave us the raw FLOPs, but CUDA, memory hierarchies, and driver stacks are what made them usable at scale.

Right now, LLMs feel like they’re at the same stage as raw FLOPs; impressive, but unwieldy. You can already see the beginnings of "systems thinking" in products like Claude Code, tool-augmented agents, and memory-augmented frameworks. They’re crude, but they point toward a future where orchestration matters as much as parameter count.

I don’t think the "bitter lesson" and the "engineering problem" thesis are mutually exclusive. The bitter lesson tells us that compute + general methods win out over handcrafted rules. The engineering thesis is about how to wrap those general methods in scaffolding that gives them persistence, reliability, and composability. Without that scaffolding, we’ll keep getting flashy demos that break when you push them past a few turns of reasoning.

So maybe the real path forward is not "bigger vs. smarter," but bigger + engineered smarter. Scaling gives you raw capability; engineering decides whether that capability can be used in a way that looks like general intelligence instead of memoryless autocomplete.
by ComplexSystems | 2025-08-24 04:15:10+00:00
I would like to see what happens if some company devoted your resources to just training a model that is a total beast at math. Feed it a ridiculous amount of functional analysis and machine learning papers, and just make the best model possible for this one task. Then instead of trying to make it cheap so everyone can use it, just set it on the task of figuring out something better than the current architecture and literally have it do nothing else but that and make something based on whatever it figures out. Will it come up with something better than AdamW for optimization? Than transformers for approximating a distribution from a random sample? I don't know, but: what is the point of training any other model?
by CountGeek | 2025-08-24 04:15:25+00:00
Pets remove plastic and instead poison ourselves.

A 2023 Belgian study[0] tested 39 brands of straws (paper, bamboo, glass, stainless steel, and plastic): Paper and bamboo straws most frequently contained PFAS, sometimes at high levels. Plastic straws also contained PFAS, but less consistently. Stainless steel straws were PFAS-free in that study.

[0]https://www.europarl.europa.eu/doceo/document/E-9-2023-00268...
by giancarlostoro | 2025-08-24 04:16:38+00:00
Him using different ones is why I use Perplexity, I get to try different models and honestly its pretty darn decent, gives me everything in an organized way, I can see all the different links, and all the files it outputs can be downloaded as a simple zip file. It has everything from GTP5 to Deepseek R1 and even Grok.

There's other sites similar to perplexity that host multiple models as well, I have not tried the plethora of others, I feel like Perplexity does the most to make sure whatever model you pick it works right for you and all its output is usefully catalogued.
by MisterMower | 2025-08-24 04:19:13+00:00
I'm sorry you aren't able to see the parallels. Let me explain them for you:

Dog owners are the motorists. Dedicated infrastructure was built specifically for them to use.

Parents with kids are the cyclists. They want to restrict how that infrastructure is used so that they can enjoy it in the way that is most convenient for them, at the expense of the dog owners.

Initially, dog owners used the park freely without any interference from parents with kids. But at some point, parents with kids felt they were entitled to use the dog park in ways it was never intended to be used, and changed regulations to restrict dog owners enjoyment of the park.

I intentionally left out any discussion of how these things are paid for to highlight the unfairness of this situation. It doesn't matter how it's paid for, since the infrastructure already exists for a specific purpose and is being used for that purpose.

To include the finance side of things in this analogy, it would be like funding the dog park with a special sales tax on dog food, which increases the unfairness of the whole situation when it's taken over by the parents with kids, who paid nothing to maintain or build it.

In case you don't understand how analogies work, they highlight critical similarities between two situations that are otherwise dissimilar to help understand the underlying concept. They aren't parallel in all respects, nor can they be.

If your best argument against my analogy is to introduce irrelevant dissimilarities to distract from the obvious point of the analogy, I'll take that as an endorsement it was effective.
by arp242 | 2025-08-24 04:20:37+00:00
> Here is the various USB e-waste that I have - usb micro C (2 separate types with same name), micro usb super speed (this one is particularly cursed), mini-usb types A and B, and normal USB type A and type B.

Catch just two more and you can challenge the USB trainer in Viridian City!
by Aaargh20318 | 2025-08-24 04:22:01+00:00
> Because you're potentially moving several thousand kilos at huge speed

No, I’m not. My current car weighs less than a thousand kilos (945 to be precise) and the speed limit in basically the entire city is 30km/h.

Newer cars are ‘several thousand kilos’ especially because of all the regulations. Just being an EV adds a significant amount of weight due to the battery.
by pjjpo | 2025-08-24 04:22:30+00:00
Do people actually get rich fast without loving their work, at least to a reasonable degree? Motivation is the most important requirement for productivity, not smarts (unnecessary for engineering where the main job is to understand tools to the fullest) nor even experience (though the ones that stay in the industry for a while generally also have more motivation). I've seen plenty of folks get managed out that fall in the unmotivated, unproductive pattern as they probably should be.

Retiring early to pursue something different is a great goal, but I think that means liking your work enough to get there. And some luck maybe that someone shared the importance of investment which is often the missing piece.
by rurban | 2025-08-24 04:24:15+00:00
I preferred Snow Leopard, 10.6 over all others. With Lion the downfall started. And it cracked with the doomed butterfly keyboards, that's when I gave up and switched back to ThinkPads with Fedora. Battery life was nice, but I am not wandering around that much anymore. Those days I did work in the pool with an air.
I hacked a way to use URL shortener to extract users' emails from GitHub/Docker
by lexokoh | 2025-08-24 04:24:27+00:00
I hacked together a way to use a URL shortener to extract users' emails when fetching GitHub or Docker content.

This was out of curiosity. Check out how it works and see how to protect yourself.

https://github.com/kagehq/email-extractor
by kragen | 2025-08-24 04:24:32+00:00
One of the most surprising applications for gas tubes in Miller's 01969 book, which I hadn't read before, is capacitive touch control of a 100mA solenoid. His Fig. 6–15 on p. 74 consists of four neon tubes (two T2–27–WR500, two 5AB-B), four resistors, two .001μF capacitors, a 4μF capacitor, a freewheeling diode for the solenoid, and the solenoid itself, and is powered by a 160VDC supply. When you touch the "on" or "off" touchplate, which is grounded through a 5.6MΩ resistor, your body capacitance momentarily provides a path for the 160V to ground before the touchplate capacitor charges up and blocks it. This kicks on the respective T2–27–WR500, which has a series 5AB-B to ground through a 6.8kΩ resistor shared between them. The two series pairs of tubes, connected on the high side through the other cap, form a flip-flop; the "off" pair has a 10kΩ resistor feeding it from the positive supply, while the "on" pair instead is supplied through the solenoid being controlled. When one pair turns on, the big cap couples a negative–going pulse to the other pair to turn it off.

12 low-precision components is pretty good for providing a flip-flop, high-voltage power switching†, capacitive touch sensing, and indicator lights. Miller seems to imply that such circuits were commonplace at the time.

______

† I think the circuit is switching 3mA with 50 volts across the solenoid, so a respectable 150mW, even if the 5AB-B is only rated for 0.3mA. The T2-27-1WR500 is rated for 3mA. The 5AB-B has a maintaining voltage of 50–60V, the T2-27-1WR500 of 60–70V, so the voltage left across the series combination of the 10k high-side resistor and the 6.8k low-side resistor is something like 50V when the "off" side of the flip-flop is conducting, and 50V/16.8kΩ is just under 3mA. I assume the solenoid must have comparable resistance.
by MadnessASAP | 2025-08-24 04:25:44+00:00
You didn't mention it, but LLMs and co don't have loops. Whereas a brain, even a simple one is nothing but loops. Brains don't halt, they keep spinning while new inputs come in and output whenever they feel like it. LLMs however do halt, you give them an input, it gets transformed across the layers, then gets output.

While you say reinforcement learning isn't a good answer, I think its the only answer.