MurphyA/hacker-news huggingface dataset:
by bostik | 2025-10-14 08:40:25+00:00
I honestly think that Dockerfile was the biggest driver. Containers as a technology are useful, for the many reasons outlined in this thread. But what Dockerfiles achieved was to make the technology accessible to much wider and much less technically deep audience. The syntax is easy to follow, the vocabulary available for the DSL is limited, and the results are immediately usable.

Oh, and the layer caching made iterative development with _very_ rapid cycles possible. That lowered the bar for entry and raised the floor for everyone to get going easier.

But back to Dockerfiles. The configuration language used made it possible for anyone[tm] to build a container image, to ship a container image and to run the container. Fire-and-forget style. (Operating the things in practice and at any scale was left as an exercise for the reader.)

And because Anyone[tm] could do it, pretty much anyone did. For good and ill alike.
by Yizahi | 2025-10-14 08:40:48+00:00
I wonder what people will do, when they will realize that LLM lawyers produce insufficient results, but "suddenly" all cheap bottom rung lawyers are gone and switched professions.

As for the LLM "creative" content, have you seen it or read it? Well, same problem. After you will need a quality content, good luck finding some cheap creator. Pay full price for an experienced one and likely wait.

PS: I don't doubt that LLMs are here to stay. They will se a lot of usage and pervade all industries. It's just that future will be pretty shit. Talking on phone with LLMs, reading LLM slop, seeing LLM lop everywhere, receiving generated emails and using LLMs to reverse parse them to search for an actual content, major economy downturn, rapidly slowing salary growth (not that it was big before), etc.
by StackBPoppin | 2025-10-14 08:40:58+00:00
I haven't completely decided yet, but I'm thinking of having the next object emit a "scraping" sound as it moves/scales, that way the sound gives you a general idea of the vicinity of where you should be looking. It will take some experimentation to get the balance correct: too easy and the game isn't fun, too difficult and it's just frustrating and tedious.

Here is a tiny preview of the basic mechanics: https://youtu.be/cUU1HnT95RE
by rcxdude | 2025-10-14 08:41:16+00:00
I think that's the point. If you have an incompetent team or team member the number of checks around them can grow astronomically and still you will have problems. At a certain point the systemic problem can become "the system is unwilling to replace this person/team with a competent one".

(That said, this is only in the case of persistent problems. Everyone can be inattentive some of the time, and a sensible quality system can be very helpful here. It's when the system tries to be a replacement for actually knowing what you're doing that things go off the rails)
by kokada | 2025-10-14 08:41:33+00:00
Nix as used in NixOS is a declarative language and there is none of the issues I cited by being a "real" programming language (or as the article talks about, having "abstractions" like builtin.map). You can pretty easily setup a LSP to get code-completion (even between different projects, like NixOS vs Home-Manager). There is no proper type system in Nix but the module system does supplement it well.
by feraloink | 2025-10-14 08:41:57+00:00
The landing page has a Q&A. This is the relevant part of the response to the question, "Why aren't all GEO satellite links encrypted?"

>Encryption imposes additional overhead to an already limited bandwidth, decryption hardware may exceed the power budget of remote, off-grid receivers, and satellite terminal vendors can charge additional license fees for enabling link-layer encryption. In addition, encryption makes it harder to troubleshoot network issues and can degrade the reliability of emergency services.

So, the only suggestion that there would be greater heat/energy if they did encryption by default is the part about decryption (receiver) hardware having limited power budgets in some cases. There's more than what I copy-and-pasted above, but the overall message is that lots of organizations haven't wanted to pay the direct costs of enabling encryption... although they should.

EDIT: Link to Q&A https://satcom.sysnet.ucsd.edu/#qanda
Show HN: I built Bulk Resize Images Online in one day
by wainguo | 2025-10-14 08:42:28+00:00
The one-day journey

Morning: I wrote down the idea — “resize multiple images in bulk, no upload”

Evening: I deployed it, registered a domain, added PWA support so users can “install” it on desktop, and pushed it live: https://bulkresizeimages.online/

The entire flow — idea → refine → code → deploy → domain → launch — took about 12 hours.

Building it reminded me how fast you can move when you keep things simple and focus.

now its launched on https://www.producthunt.com/products/bulkresizeimage-online
by nebula8804 | 2025-10-14 08:42:46+00:00
Homer Simpson exemplifies this. Heres a guy that never went to college being the lead safety inspector for a nuclear power plant. He owns his home with three kids a stay at home wife and two cars and two pets(with the occasional elephant that comes and goes). A lot has changed since the writing of these characters and the world now.

[1]: https://www.youtube.com/watch?v=Fp2Ey0H7OUE
by gartdavis | 2025-10-14 08:43:43+00:00
Manufacturing was hard to do 2 decades ago, and is harder now.

I started, grew, and exited a modern manufacturing-based business, and I can confirm that almost everything about modern capitalism in this cycle is biased -against- any business that manufactures in 1st world economies. The business, Spoonflower, was and is an innovative marketplace of textile design, mated to on-demand manufacture, and had factories in Durham NC and Berlin Germany.

Three factors made this very difficult:

-- raising funding or debt to support old-fashioned capital equipment. Building factories was once the backbone of the US economy but is now pretty close to impossible for an entrepreneur. Raising money to write software is straightforward and well understood. Raising money to purchase industrial equipment the size of a city bus is not what our startup economy is optimized for, or even understands or has models for. Confusion about this is nearly universal.

-- operating a labor-intensive (anything where the largest component of cost is the labor component) manufactory. As others have noted, making stuff is physically demanding. Some people love hard work, but culturally this is rare. If you are crazy successful, the reward is another shift of harder, potentially more efficient work.

-- exiting investors / providing ROI. Our business fit in two categories: creative digital marketplaces (Ebay, Etsy...) valued at 4-6x revenue, or makers like Cimpress or Shutterfly at .5 to 1x revenue. Who buys factories.... even really interesting ones? The short answer: only those that already own factories. When you have a very short list of potential acquisitors, its hard to create an auction market for your equity.

In general, we did okay. But every step from launch to growth to exit felt very much like swimming into a strong current. The same very hard working and resourceful group of colleagues could have done anything. I'm proud of the work, but a lot of that pride is sheer contrariness at having executed on something so unlikely and having survived.

This would be much harder now.

Sourcing is harder. Friends working in the space now rely on a global sourcing network just as we did, that is in utter disarray. Operating on thin margins with a factory that must be fed raw materials to make money is terrifying on a normal day. These days the threat to supply chains is existential.

Launching consumer brands is harder. As has been widely noted, access to the top of the funnel has now been fully monetized (or fully enshittified) by Google, Facebook etc, and because of AI, that funnel now shrinks. Something will break loose here, but nothing has yet.

A post-pandemic employment environment is even more difficult for manufacturers. I think it is safe to say that demand for jobs that require 8-12 hours of physically demanding work surrounded by colleagues and industrial machinery is at an all time low.

I spent 15 years in service to a vision of domestic making, and while we were not defeated, I understand deeply the uphill battle any manufacturing entrepreneur faces.
by roenxi | 2025-10-14 08:44:11+00:00
> However, the hardware situation you described sounds very brittle to me. If the machine shop is so tightly constrained and error-phobic, that sounds like there's very little space of tinkering, exploration or innovation.

The technical term for that is "the real world". Moment of perspective on just how weird the software people are that they don't just accept mucking around as expensive and dangerous.
by sussmannbaka | 2025-10-14 08:44:44+00:00
What I want to see at this point are more screencasts, write-ups, anything really, that depict the entire process of how someone expertly wrangles these products to produce non-trivial features. There's AI influencers who make very impressive (and entertaining!) content about building uhhh more AI tooling, hello worlds and CRUD. There's experienced devs presenting code bases supposedly almost entirely generated by AI, who when pressed will admit they basically throw away all code the AI generates and are merely inspired by it. Single-shot prompt to full app (what's being advertised) rapidly turns to "well, it's useful to get motivated when starting from a blank slate" (ok, so is my oblique strategies deck but that one doesn't cost 200 quid a month).

This is just what I observe on HN, I don't doubt there's actual devs (rather than the larping evangelist AI maxis) out there who actually get use out of these things but they are pretty much invisible. If you are enthusiastic about your AI use, please share how the sausage gets made!
by ehnto | 2025-10-14 08:44:57+00:00
> at least for now.

I know I could be eating my words, but there is basically no evidence to suggest it ever becomes as exceptional as the kingmakers are hoping.

Yes it advanced extremely quickly, but that is not a confirmation of anything. It could just be the technology quickly meeting us at either our limit of compute, or it's limit of capability.

My thinking here is that we already had the technologies of the LLMs and the compute, but we hadn't yet had the reason and capital to deploy it at this scale.

So the surprising innovation of transformers did not give us the boost in capability itself, it still needed scale. The marketing that enabled the capital, that enables that scale was what caused the insane growth, and capital can't grow forever, it needs returns.

Scale has been exponential, and we are hitting an insane amount of capital deployment for this one technology that, has yet to prove commercially viable at the scale of a paradigm shift.

Are businesses that are not AI based, actually seeing ROI on AI spend? That is really the only question that matters, because if that is false, the money and drive for the technology vanishes and the scale that enables it disappears too.
by p_v_doom | 2025-10-14 08:45:16+00:00
Well, that both sounds logical and matches my experience so far. IMO the whole "parallel career ladder" to me does sound like mostly a way to have something to motivate people who do not go for management, and also maybe to limit who goes into management for good or worse.

Also this is something that goes beyond what one person has as an architectural vision - at some point you will run into conflicts between the practical corporate reality and your vision and one will have to give way - most of the time it is the vision. Often that vision does not exist in isolation, but is built with at least the implicit understanding of the softer, people side.
by deliciousturkey | 2025-10-14 08:45:44+00:00
When GPUs started being used for deep learning (after AlexNet), GPUs were not at all matmul machines. They were machines that excel in most kinds of heavily parallel workloads. And this holds to this day, with the exception of the tensor core, which is an additional hardware block designed to accelerate this specific task.

Matrix multiplication didn't "win" because HW was designed for it. It won because matrix multiplication is a fundamental part of linear algebra and is very effective in deep learning (most kinds of functions you might want to write for deep learning can be expressed as a matmul). Acceleration of it became later. Additionally, matrix multiplication is a good fit for physics, as you can design the HW so that data movement is minimized, and most of the chip area and power are spent in actual computation, and not moving data around.

Fundamentally speaking, you also want to make your algorithm compatible with real-world physics. The need for heavy parallelism is required by the fact that you cannot physically make a fast chip that processes dependent operations. It's just not possible to propagate signals through transistors fast enough to make it possible. Even CPUs, even if they present a non-parallel programming environment, have to rely on expensive tricks like speculative out-of-order execution to make "sequential" code parallel to make it fast.

In general though, I personally would wish that chips would be made with taking programmability in mind. A fixed-function matrix multiplier might be slightly more efficient than a parallel computing chip with smaller matrix multipliers. But it would be significantly more programmable, and you can design much more interesting (and potentially more efficient) algorithms for it.
by tooltalk | 2025-10-14 08:46:04+00:00
> What % of "rare earths" are going into those military products?

This is somewhat dated (2012), but I suspect that it hasn't changed much: <5%[1].

1. Rare Earths Shortage Would Spur Pentagon to Action, Gopal Ratnam, Bloomberg April 9, 2012 https://archive.ph/1aEF8
by kymki | 2025-10-14 08:46:05+00:00
Strudel did something many other approaches to live coding have failed to do imho. No hosting needed. Just open the web based REPL and go. Great entry to live coding, but you quickly run into limitations.

Here is a piece inspired by Dawn of Midi and my attempt at taking a piano synth and trying to make it sound like a lof of different things.

Copy, paste, modify.

const bpm = 138; setcps(bpm/60/4);

// Pattern 1: lower melodic pulse

const bass_pulse = note("<d2 f2 a2 g2>") .s("piano") .slow(4) .gain(rand.range(0.45, 0.65)) .attack(0.005) .decay(0.8) .sustain(1.2) .release(1.2) .lpf(800) .room(0.3) .delay(0.15) .delaytime(0.375) .delayfeedback(0.25) .pan(0.5);

// Pattern 1.5 (?): layered base

const bass_pulse_2 = note("<d2 f2 a2 g2>") .s("piano") .slow(4) .gain(rand.range(0.45, 0.65)) .add(note(12)) .attack(0.005) .decay(0.8) .sustain(1.2) .release(1.2) .lpf(800) .room(0.3) .delay(0.15) .delaytime(0.375) .delayfeedback(0.25) .pan(0.5);

// Pattern 2: Mid-range polyrhythm

const mid_pattern = note("<a3 c4 d4 f4 a3>") .s("piano") .struct("x(5,8)") .gain(rand.range(0.25, 0.45)) .attack(0.008) .decay(0.4) .sustain(0.05) .release(0.6) .lpf(perlin.range(1200, 2200).slow(8)) .room(0.5) .pan(rand.range(0.3, 0.7));

// Pattern 3: repetitive pulse

const high_pulse = note("d5 [~ d5] d5 ~") .s("piano") .fast(2) .gain(rand.range(0.18, 0.35)) .attack(0.01) .decay(0.3) .sustain(0) .release(0.4) .lpf(2800) .room(0.6) .delay(0.25) .delaytime(0.1875) .delayfeedback(0.3) .pan(0.7);

// Pattern 4: Sparse accent notes (3 over 4 polyrhythm)

const accents = note("a4 ~ f4") .s("piano") .slow(2) .gain(rand.range(0.35, 0.55)) .attack(0.5) .decay(0.6) .sustain(0.9) .release(0.9) .lpf(1800) .room(0.45) .pan(0.2) .sometimes(x => x.delay(0.3).delayfeedback(0.4));

// Pattern 5: Extended mid-range polyrhythm (13 over 16 - cello thingie)

const mid_long = note("<a3 c4 d4 f4 a3 c4 e4 d4 f4 g3 a3 c4 d4>") .s("piano") .struct("x(13,16)") .gain(rand.range(0.32, 0.48)) .attack(0.06) .decay(0.9) .sustain(0.25) .release(1.1) .lpf(perlin.range(800, 1400).slow(12)) .lpq(4) .room(0.55) .delay(0.18) .delaytime(0.25) .delayfeedback(0.3) .pan(rand.range(0.35, 0.65));

const high_long = note("<d5 a4 f5 d5 c5 a4 g4>") .s("piano") .struct("x(7,8)") .gain(rand.range(0.28, 0.42)) .attack(0.05) .decay(0.8) .sustain(0.3) .release(1.0) .lpf(sine.range(1000, 1600).slow(8)) .lpq(3.5) .room(0.6) .delay(0.22) .delaytime(0.1875) .delayfeedback(0.35) .pan(0.7);

stack( bass_pulse, bass_pulse_2, mid_long, high_long, mid_pattern, high_pulse, accents );
by rafaelmn | 2025-10-14 08:47:03+00:00
So at what point are you doing more work on the agent than working on the code directly ? And what are you losing in the process of shifting from the code author to LLM manager ?

My experience is that once I switch to this mode when something blows up I'm basically stuck with a bunch of code that I sort of know, even tough I reviewed it. I just don't have the same insight as I would if I wrote the code, no ownership, even if it was committed in my name. Like any misconceptions I've had about how things work I will still have because I never had to work through the solution, even if I got the final working solution.
by kykat | 2025-10-14 08:47:11+00:00
If we assume that each action has 99% success rate, and when it fails, it has 20% chance of recovery, and if the math here by gemini 2.5 pro is correct, that means the system will tend towards 95% chance of success.

===

In equilibrium, the probability of leaving the Success state must equal the probability of entering it.

(Probability of being in S) * (Chance of leaving S) = (Probability of being in F) * (Chance of leaving F)

Let P(S) be the probability of being in Success and P(F) be the probability of being in Failure.

P(S) * 0.01 = P(F) * 0.20

Since P(S) + P(F) = 1, we can say P(F) = 1 - P(S). Substituting that in:

P(S) * 0.01 = (1 - P(S)) * 0.20 0.01 * P(S) = 0.20 - 0.20 * P(S) 0.21 * P(S) = 0.20 P(S) = 0.20 / 0.21 ≈ 0.95238
by jrflowers | 2025-10-14 08:48:07+00:00
I personally always love the “I wrote an entire codebase with claud” posts where the response to “Can we see it?” is either the original poster disappearing into the mist until the next AI thread or “no I am under an NDA. My AI-generated code is so incredible and precious that my high-paying job would be at risk for disclosing it”
by 0xAFFFF | 2025-10-14 08:48:08+00:00
> Current state-of-the-art models are, in my experience, very good at writing boilerplate code or very simple architecture especially in projects or frameworks where there are extremely well-known opinionated patterns (MVC especially).

Which makes sense, considering the absolutely massive amount of tutorials and basic HOWTOs that were present in the training data, as they are the easiest kind of programming content to produce.
by legacynl | 2025-10-14 08:49:00+00:00
Alright "touche" my guy, so original as well.

> if you dont understand, let alone have mastered, git, then you dont get to have an opinion

Eh Yeah kind of? If you use the wooden end of a hammer to try to hammer a nail, you don't get to complain that a hammer is a shitty tool for hammering nails without looking profoundly stupid. And complaining about people complaining about this is just ignorant.

Of course people are allowed to have opinions on git (or anything), but if your 2nd sentence is 'i have a command that deletes the .git folder and replaces it with a fresh copy', it shows that maybe the author is still in the hammering nails with the wooden part phase.
by aw1621107 | 2025-10-14 08:49:39+00:00
IIRC Cranelift doesn't use copy-and-patch. It uses e-graphs [0] as part of its optimization pipeline, though.

Closest thing in (relatively) recent news that uses copy-and-patch I can think of is CPython's new JIT.

[0]: https://github.com/bytecodealliance/rfcs/pull/27
by citrin_ru | 2025-10-14 08:50:00+00:00
> They also don't complain nearly as much about things which are 'established and ugly' like powerlines or coal power plants

I like industrial architecture and some plants inspire awe but post-war coal plants are as ugly and boring as it gets. Older ones look much better in my eye and I’m glad that some buildings are preserved after the stations are shut down.
by jonathanstrange | 2025-10-14 08:50:11+00:00
I was never more productive than with REALBasic, it was a true RAD tool, and I'd happily use Xojo nowadays if it wasn't so expensive. That's a "modern" structured BASIC so I guess not what you had in mind.

One thing I've learned over the years is that the language is almost irrelevant, the tooling and 3rd-party library support are much more important.
by imtringued | 2025-10-14 08:50:16+00:00
The most obvious application for eigenvalues is that if you find the eigendecomposition A=QλQ^{-1}, where λ=diag(eigenvalues) you can cheaply obtain the results of A^n = QλQ^{-1} ... QλQ^{-1} ... QλQ^{-1} = Qλ^nQ^{-1}. A less likely use is for inverting A or solving a linear system of equations with it.
by carlnewton | 2025-10-14 08:50:24+00:00
Woah! So soon after the September one?

I'm working on Habitat. It's a free and open source, self-hosted platform for communities to discover and discuss their local area. The plan is for it to be federated.

Stuck on a frustrating little bug at the moment, once I get through that I feel like I'll probably work on some CI utils to ensure that the code stays up-to-scratch and tag the first release.

- The idea: https://carlnewton.github.io/posts/location-based-social-net...

- A build update and plan: https://carlnewton.github.io/posts/building-habitat/

- The repository: https://github.com/carlnewton/habitat

- The project board: https://github.com/users/carlnewton/projects/2
by StopDisinfo910 | 2025-10-14 08:50:45+00:00
The best tool to hunt a submarine is an anti-sub helicopter.

Submarines are basically as good as dead if an anti-sub helicopter is nearby. They can't really retaliate, an active sonar will most likely expose them and they are not fast enough to escape a torpedo.

> But claiming this magically makes aircraft carriers obsolete is largely internet fanboy noise.

All surface ships are useless in a symetric warfare. Just look at what Ukraine did to the Russian navy in the Black sea.

Ships are slow and exposed. Even if their defence allows them to survive a direct attack (dubious), they are necessarily prone to saturation attack.

Very useful when you need to bomb a poor country to make them remember that you are a liberal country in name only and their tribute is overdue however.