MurphyA/hacker-news huggingface dataset:
by kuerbel | 2026-02-09 06:45:57+00:00
And no-one ever stops and thinks about what it means to give up so much control.

Maybe one of those companies will come out on top. The others produce garbage in comparison. Capital loves a single throat to choke and doesn't gently pluralise. So of course you buy the best service. And it really can generate any code, get it working, bug free. People unlearn coding on this level. And some day, poof, Microsoft is coming around and having some tiny problem that it can generate a working Office clone. Or whatever, it's just an example.

This technology will never be used to set anyone free. Never.

The entity that owns the generator owns the effective means of production, even if everyone else can type prompts.

The same technology could, in a different political and economic universe, widen human autonomy. But that universe would need strong commons, enforced interoperability, and a cultural refusal to outsource understanding.

And why is this different from abstractions that came before? There are people out there understanding what compilers are doing. They understand the model from top to bottom. Tools like compilers extended human agency while preserving a path to mastery. AI code generation offers capability while dissolving the ladder behind you.

We are not merely abstracting labor. We are abstracting comprehension itself. And once comprehension becomes optional, it rapidly becomes rare. Once it becomes rare, it becomes political. And once it becomes political, it will not be distributed generously.
by s20n | 2026-02-09 06:46:09+00:00
> I’ve been using Copilot - and more recently Claude - as a sort of “spicy autocomplete” and occasional debugging assistant for some time, but any time I try to get it to do anything remotely clever, it completely shits the bed.

This seems like a really disingenuous statement. If claude can write an entire C compiler that is able to compile the linux kernel, I think it has already surpassed an unimaginable threshold for "cleverness"
by typ | 2026-02-09 06:46:15+00:00
The silicon shield became a slogan that has only been popularized in recent years. The potential crisis of war has been there for more than half a century (even before semiconductors became a thing). The real value proposition of the status quo is the freedom of navigation between the northeastern Asian countries and the SEA (the Strait of Malacca, aka the lifeline of energy imports), and the consequential domino effect of the entire western Pacific.

Also, not sure why everyone forgets about it. People should have learned from the experience of the pandemic that the cutting-edge foundry nodes are not really the crucial ones, as being the bottleneck of industrial infrastructure. A delay of the next-gen iPhone or RTX gaming card isn't that catastrophic. But a shortage of embedded MCUs, which are actually fabricated by mature nodes, could stall the entire industrial base of a country.
by robotpepi | 2026-02-09 06:46:15+00:00
> So there's nothing about this particular sequence?

Kolakoski's sequence is conjectured to be "uniformly recurrent", which means that every block of contiguous symbols appearing once in the sequence appears infinitely often and with bounded gaps. This is clearly not true for any sequence constructed like this. And the fact that we don't know how to prove uniform recurrence for Kolakoski's, which is arguably the simplest sequence defined by this method, is remarkable. There are other conjectures about frequencies of symbols, etc.
by acjohnson55 | 2026-02-09 06:46:21+00:00
I'm working in a very mature codebase on product features that are not technically unprecedented, which probably is determining a lot of my experience so far. Very possible that I'm experiencing a sweet spot.

I can totally imagine that in greenfield, the LLM is going to explore huge search spaces. I can see that when observing the reasoning of these same models in non-coding contexts.
by virgilp | 2026-02-09 06:49:15+00:00
That's not how things work in practice.

I think the concern is not that "people don't know how everything works" - people never needed to know how to "make their own food" by understanding all the cellular mechanisms and all the intricacies of the chemistry & physics involved in cooking. BUT, when you stop understanding the basics - when you no longer know how to fry an egg because you just get it already prepared from the shop/ from delivery - that's a whole different level of ignorance, that's much more dangerous.

Yes, it may be fine & completely non-concerning if agricultural corporations produce your wheat and your meat; but if the corporation starts producing standardized cooked food for everyone, is it really the same - is it a good evolution, or not? That's the debate here.
by MyHypatia | 2026-02-09 06:49:48+00:00
A treatment is available but it doesn't seem to work in some cases. The author of the post is motivated to improve that. Honestly, I feel happy when people choose to spend their time trying to solve these types of problems instead of working on things they may not be that interested in or on problems where success doesn't really yield a real benefit to humanity.

Maybe, he spends a lot of time on it and doesn't have success. That seems better to me than spending time on something he doesn't really care about.
by jurgenburgen | 2026-02-09 06:50:02+00:00
> Every company I have ever worked for has had a deep backlog of tasks and ideas we realistically were never going to get to. These tools put a lot of those tasks in play.

Some of that backlog was never meant to be implemented. “Put it in the backlog” is a common way to deflect conflict over technical design and the backlog often becomes a graveyard of ideas. If I unleashed a brainless agent on our backlog the system would become a Frankenstein of incompatible design choices.

An important part of management is to figure out what actually brings value instead of just letting teams build whatever they want.
by RupertSalt | 2026-02-09 06:50:09+00:00
I found it!

https://rscott.org/OldInternetFiles/network-contacts.1996061...

I had named it "HEARTLAND" rather than a Cthulhu-related name, which was hindering my searches. I had also asked Gemini and it hallucinated a historical record which it was unable/unwilling to link.

The network was: 192.160.182.0/24

ARIN still has the history: https://whois.arin.net/rest/ip/192.160.182.0?s=192.160.182.0

My original assigned user handle was: RE229 (a prime number, very on-brand)

My Netcom email address and a San Jose phone number are enshrined in the record. Don't bother contacting me through those! Interestingly, if you spell out the phone number, it ends in "NET", but does not spell anything compelling in its entirety.
by zjp | 2026-02-09 06:50:50+00:00
Since (IIRC) Claude Code's own author admits he built it entirely with Claude, I imagine the initial prompt was something like "I need a terminal based program that takes in user input, posts it to a webserver, and receives text responses from the webserver. On the backend, we're going to feed their input to a chatbot, which will determine what commands to run on that user's machine to get itself more context, and output code, so we need to take in strings (and they'll be pretty long ones), sanitize them, feed them to the chatbot, and send its response back over the wire."

Everything here except the LLM has been done a thousand times before. It can compose those building blocks in novel ways, that's what makes it so good. But it's not going to generate new building blocks.

I wanted CC to add Flying Edges to my codebase. It knew where to integrate its solution. It adapted it to my codebase beautifully. But it didn't write Flying Edges because it fundamentally doesn't know what Flying Edges is. It wrote an implementation of Marching Cubes that was only shaped like Flying Edges. Novel algorithms aren't ESPs.
by bee_rider | 2026-02-09 06:51:07+00:00
It seems pretty hard to say at this point—we have people who say they get good results and have high standards. They don’t owe us any proof of course. But we don’t really have any way to validate that. Everybody thinks their code is good, right?

Microsoft might just be having trouble selling copilot because Claude or whatever is better, right?

Moltbook is insecure, but the first couple iterations of any non-trivial web service ends up having some crazy security hole.

I think we’ll only know in retrospect, if there’s a great die-off of the companies that don’t adopt these tools.
by shayief | 2026-02-09 06:51:20+00:00
> If I could only maintain code I wrote, I'd have to work alone.

I don't think that "Code you did not write is code you do not understand" implies that the only way to understand code is to write it. I can read code, run it, debug it and figure out how it works.

It's true that all of that is possible with AI generated code. But the thing is, is it worth for me spending time understanding that PR, when I can make the change or launch my own agent to prompt it myself? At least with my own prompt I know exactly what I want it to be.
by vaxman | 2026-02-09 06:51:50+00:00
..was totally expecting this article to be written in Chinese, because it is an active attack vector being used to infiltrate western IT infrastructure by pre-loading spyware into the boot loaders, then clicked the article and the link opened to Cyrillic characters. Of course, APT29 is doing this too, but their agents aren't thought to be in the supply chain, so they have to infect the products after installation (using traditional hacking techniques). Look at how Intel ME and AMD PSP work and presume that something similar is in the latest Allwinner, Rockchip, Alibaba, and Nuclei, etc.. chips. To the Chinese, this seems fair because "the other Party" is doing it. Not arguing that it isn't, just that most customers have their head in the sand denying it is happening (even using them in IPKVMs!)

There was also a Western paper on embedding hidden circuits (and thus functionality/ISAs) into CPUs and activating them in the field (after deployment) by executing a sequence of opcodes resulting in something akin to blowing a fuse on the processor die, thereby making the circuits invisible (to even a microscope) until activated. Those secret opcodes could then disable the chip or jump into a block of secret microcode and alter the program counter to behave in a non-standard manner so we might not easily be able to see what's happening, even if we knew to look. One still would have to deliver that secret sequence of opcodes to trigger such a stealth mode...that could be done via microcode/firmware updates from the manufacturer's website or an OS update, or similar techniques. Interestingly, an unexpected iteration of Chinese SoCs began flooding the markets just as the rest of the World was sustaining a "chip shortage" in a timeframe perfectly aligned with what would have been required to implement such a system after that paper was published.

Air-gapping the customer's devices during installation (to prevent the activation opcode sequence) might not work either. Many Chinese SoCs are used as embedded peripherals simply to add WiFi, Bluetooth and Thread capabilities onto existing designs via a simple UART or doorbell interconnect. This makes it too easy for these Chinese SoCs to also be listening for a Morse-code-like activation signal on a low-frequency band, triggering the backdoor to open (enabling a more conventional remote code execution port or logic debugger exposed via WiFi or BLE for example, or causing malfunction or damage to what's on the GPIO or UART pins). Such low-frequencies could be disguised (think Blue Whale signals) so they might be sent slowly over great distances (or even from floating weather balloons or low earth orbit) without drawing too much suspicion. https://youtu.be/E-gbYjLd93g
by p1nkpineapple | 2026-02-09 06:52:03+00:00
Since we recently moved out of the city and into the mountains of Switzerland, I had a niche problem... agreeing with my buddies which is the best ski field to meet at when we all live in different towns. So I made a little web app to help:

https://skicompromise.ch

This was a fun little project I did over the Christmas holidays but only finished off recently. Basically I precalculated the public transport time between the most populated towns in Switzerland to every ski field (about 350 of them!) and then built a little web app around it using Django.

You can choose to prioritise shortest (lowest time overall) versus fairest (smallest variance in group members).

Totally free to use. Next steps are to integrate it with live snow conditions/open lifts...

Claude did help a lot with the FE part. The biggest part was actually finding the best public transport stop for each ski field - that was a very manual process trawling through skimap.org images and Anreise info on ski resort websites.
by locknitpicker | 2026-02-09 06:53:10+00:00
> As long as AI (...) is perceived not as a "bicycle of the mind" and a tool to utilize 'your' skills to a next phase but as a commodity (...), it is really hard to justify the current state of AI.

I don't agree at all. The "commodity" argument is actually a discussion on economic viability. This is the central discussion, and what will determine if tomorrow we will still have higher-quality and up-to-date LLMs available to us.

You need to understand that nowadays there is a clear race to the bottom in LLM-related services, at a time when the vast majority is not economically viable. The whole AI industry is unsustainable at this point. Thus it's rather obvious that making a business case and generating revenue is a central point of discussion.
by Farooq1 | 2026-02-09 06:53:14+00:00
Your reply hits the real tension: a flat model simplifies layout changes, but it shifts complexity into how you map edits and selections. That trade‑off feels worth it if the goal is “safe structure changes” and AI‑driven transforms.

On the split/merge issue: in a flat model, the split/merge doesn’t have to be a structural operation at all. It can live entirely inside the block’s text content. The block keeps the same ID, and only its content changes. That avoids the “delete/reinsert” problem and keeps a stable identity for AI or history.

On selection: the cleanest route is to render a normal DOM tree for interaction and treat the flat IR as the truth. So the DOM is just a projection. That buys you native selection and IME behavior without building a custom cursor engine. The only hard part is deciding a consistent reading order (left‑to‑right, top‑to‑bottom, region order), so selection feels predictable even when layout is spatial.

On syncing/CRDT: a flat model can be simpler in a different way. You’re syncing text inside blocks plus lists of IDs in regions. That’s two clear problems instead of one giant nested tree. It doesn’t remove the complexity, but it makes it easier to reason about where conflicts live (content vs layout).

On lists: a flat list of items is closer to how people think. UL/OL is a browser artifact. Quill’s model already shows this is workable, and it makes the “content pool + layout map” idea more consistent.

Using TipTap/ProseMirror as the editing surface (selection, IME, rich text behavior) while keeping a separate IR is a reasonable split: the view stays tree‑shaped, the data stays flat.

So overall: this approach looks less like “throw away trees” and more like “trees become a rendering tool, not the canonical structure.” That’s a meaningful shift, especially if AI or layout transforms are first‑class.
by whaleidk | 2026-02-09 06:53:26+00:00
It may also have to do with the domain and language to some extent.

Yes, the code archaeology is the time consuming part. I could use an LLM to do that for me in my co-workers generated code, but I don’t want to because when I have worked with AI I have found it to typically create overly-complex and uncreative solutions. I think there may be some confirmation bias with LLM coders where they look at the code and think it’s pretty good, so they think it’s basically the same way they would have written it themselves. But you miss a lot of experiences compared to when you’re actually in the code trenches reading, designing, and tinkering with code on your own. Like moving around functions to different modules and it suddenly hits you that there’s actually a conceptual shift you can make that allows you to code it all much simpler, or recalling that shareholder feedback from last week that — if worked with —could allow you a solution pathway that wasn’t viable with the current API design. I have also found that LLMs make assumptions about what parts of the code base can and can’t be changed, and they’re often inaccurate.
by shevy-java | 2026-02-09 06:53:27+00:00
> CCC compiled every single C source file in the Linux 6.9 kernel without a single compiler error (0 errors, 96 warnings). This is genuinely impressive for a compiler built entirely by an AI.

It would be interesting to compare the source code used by CCC to other projects. I have a slight suspicion that CCC stole a lot of code from other projects.
by red75prime | 2026-02-09 06:54:07+00:00
> Pro-LLM coding agents: look! a working compiler built in a few hours by an agent! this is amazing!

> Anti-LLM coding agents: it's not a working compiler, though. And it doesn't matter how few hours it took, because it doesn't work. It's useless.

Pro-LLM: Read the freaking article, it's not that long. The compiler made a mistake in an area where only two compilers exists that are up to the task: Linux Kernel.
by OldSchool | 2026-02-09 06:54:11+00:00
I've been using two heat pumps near Austin, Texas since 2011, rated at a total of 84,000 BTU/hr (4 ton + 3 ton capacity) (25KW of heat) on a total of 5KW electrical input (COP ~= 5.0).

They are standard outdoor air heat exchangers so below about 35F efficiency drops significantly. That's pretty rare around here so it is almost always enough - we can still gain about 45F vs the outdoor temperature even below 20F.

We don't have natural gas available where I live, only propane. When I purchased the heat pumps, propane was $5/gallon for 91,500 BTU. That translates to about $4.60/hr to run 84,000 BTU/hr of furnace. With electric energy (cheap in Texas!) at about $0.11/KWh, the equivalent costs of my heat pumps was and remains close to about $0.55/hr to run.

In the summer, they cool with equal capacity and similar power consumption for a 15 SEER rating (waste heat from the system components works against cooling in the summer!)

Factor in your acquisition costs (mine, just after the housing bust and with a little legwork, were about 20% of retail at the time, so a no-brainer) and you can get a lot more objective idea what you're really accomplishing.
by anupsing_ai | 2026-02-09 06:54:43+00:00
Been building multi-agent systems for the past few months and keep running into the same problem: agent regressions that no traditional test catches.
Wrote up the failure modes I've actually seen. The short version:
Skill bloat is the sneaky one. You add tools over time, each makes sense individually, then at 30+ tools the agent starts picking wrong ones. Anthropic's team wrote about this too. A single Playwright MCP server burns 11.7K tokens on tool definitions alone, included in every request whether you use them or not.
Context rot caught me off guard. Chroma measured 18 LLMs and found 11/12 drop below 50% performance at just 32K tokens. We had a bug where adding a new capability pushed an older instruction into the "lost in the middle" zone. Took two days to figure out why an unrelated feature degraded.
The scariest one is when a prompt tweak makes your agent stop saying "I don't know" and start confidently making things up. Surface metrics go up. Accuracy goes down. Google's AI Overviews had this problem at scale.
Also covers cost creep (same traffic, 40% higher bill, nobody has a cost gate in CI/CD), multi-agent cascade failures (Berkeley's MAST paper found 14 failure modes across 7 frameworks, ChatDev hit 33% correctness), and model updates breaking things you didn't touch.
We have 20 years of great tooling for deterministic regression. For probabilistic regression we have basically nothing.
by thih9 | 2026-02-09 06:56:54+00:00
> Sometimes (not always), this makes addition visual

Fora taste of this in Arabic numerals consider a 7 segment font, with 1 aligned to the left; we’d have “5+1=6”.

And of course this would always work with tally marks.

Is there a more complex number system where visual feedback like this always works?
by beklein | 2026-02-09 06:56:55+00:00
Honest question: would a normal CS student, junior, senior, or expert software developer be able to build this kind of project, and in what amount of time?

I am pretty sure everybody agrees that this result is somewhere between slop code that barely works and the pinnacle of AI-assisted compiler technology. But discussions should not be held from the extreme points. Instead, I am looking for a realistic estimation from the HN community about where to place these results in a human context. Since I have no experience with compilers, I would welcome any of your opinions.
by tovej | 2026-02-09 06:57:07+00:00
They do not model the world.

They present a statistical model of an existing corpus of text.

If this existing corpus includes useful information it can regurgitate that.

It cannot, however, synthesize new facts by combining information from this corpus.

The strongest thing you could feasibly claim is that the corpus itself models the world, and that the LLM is a surrogate for that model. But this is not true either. The corpus of human produced text is messy, containing mistakes, contradictions, and propaganda; it has to be interpreted by someone with an actual world model (a human) in order for it to be applied to any scrnario; your typical corpus is also biased towards internet discussions, the english language, and western prejudices.
by jeeeb | 2026-02-09 06:57:17+00:00
If this was just about semiconductors then this would be a reasonable take but I doubt semi-conductors are anything more than a minor footnote in China’s strategic calculus vis-a-vis Taiwan.

Reunification with Taiwan has been a major policy goal of the CCP since the civil war and is one of Xi’s explicit policy goals. He just reaffirmed this commitment as part of his New Year’s speech.

Historically China has lacked force projection capability. However it has had a multi-decade modernisation and military build-up which has drastically changed this situation.

Further we’ve seen significant tightening of CCP control over society and in particular the military in Xi’s term.

A straight forward analysis of these events, in line with Xi’s public statements and past Chinese actions,
is that the ground work is being laid for encirclement of Taiwan followed by China taking over, by force if necessary.
by oblio | 2026-02-09 06:58:21+00:00
> What you're describing sounds closer to studying the Talmud than to reading and reviewing most code.

https://www.joelonsoftware.com/2000/05/26/reading-code-is-li...

Most human written code has 0 (ZERO!) docs. And if it has them, they're inaccurate or out of date or both.

Lots of code is simple and boring but a fair amount isn't and reading it is non trivial, you basically need to run it in your head or do step by step debugging in multiple scenarios.