MurphyA/hacker-news huggingface dataset:
by kentonv | 2025-09-22 18:16:18+00:00
You can define a stream like:

interface Stream extends RpcTarget { write(chunk): void; end(): void; [Symbol.dispose](): void; }

Note that the dispose method will be called automatically when the caller disposes the stub or when they disconnect the RPC session. The `end()` method is still useful as a way to distinguish a clean end vs. an abort.

In any case, you implement this interface, and pass it over the RPC connection. The other side can now call it back to write chunks. Voila, streaming.

That said, getting flow control right is a little tricky here: if you await every `write()`, you won't fully utilize the connection, but if you don't await, you might buffer excessively. You end up wanting to count the number of bytes that aren't acknowledged yet and hold off on further writes if it goes over some threshold. Cap'n Proto actually has built-in features for this, but Cap'n Web does not (yet).

Workers RPC actually supports sending `ReadableStream` and `WritableStream` (JavaScript types) over RPC. I'd like to support that in Cap'n Web, too, but haven't gotten around to it yet. It'd basically work exactly like above, but you get to use the standard types.

---------------------

Exceptions work exactly like you'd expect. If the callee throws an exception, it is serialized, passed back to the caller, and used to reject the promise. The error also propagates to all pipelined calls that derive from the call that threw.

---------------------

The mapper function receives, as its parameter, an `RpcPromise`. So you cannot actually inspect the value, you can only pipeline on it. `friend.isBestFriend ?` won't work, because `friend.isBestFriend` will resolve as another RpcPromise (for the future property). I suppose that'll be considered truthy by JavaScript, so the branch will always evaluate true. But if you're using TypeScript, note that the type system is fully aware that `friend` is type `RpcPromise<Friend>`, so hopefully that helps steer you away from doing any computation on it.
by bubblyworld | 2025-09-22 18:16:28+00:00
You probably know this already if you climb, but in east germany and the czech many areas mandate knots as protection, jamming them in cracks: https://www.climbing.com/travel/soft-stone-rigid-ethics-elbe...

Supposedly cams and nuts damage the rock. Pretty gnarly stuff. And it's often sandy off-widths as well...
by BoorishBears | 2025-09-22 18:16:30+00:00
I feel like public datasets are something we're holding onto with LLM benchmarks for historical reasons, but need to move on from.

Older, non-instruction tuned models needed post-training on public datasets to even reliably produce meaningful answers.

Now we're testing tasks that are so complex that the LLM should reasonably be expected to answer without additional post-training.

Once you have a public dataset, even feeding those examples to an LLM and producing synthetic variations is enough to let you game the benchmark. And the worst part is you don't need to be unethical to do this: some people would say it's just a good way to expand your training data even though it incidentally allows you to overfit on the task, without overfitting on the public dataset.

So everyone's doing stuff like that, and we're getting models that are increasing overfit to a few narrow tasks.

-

The alternative is just giving detailed plain english descriptions of the tasks in question. Those can be used to generate synthetic tasks, but won't result in matching the benchmark's "shape" perfectly (as long as the questions stay hidden), and that alone is enough to ensure some level of generalization takes place.
by markusw | 2025-09-22 18:16:32+00:00
Yeah, basically the same here, except it's Caddy in front instead of Traefik.

So you do periodic backups, not incremental on every write or something (read replica-like)?

It's important to me to not lose any data once committed if at all possible.

(For testing, I've sped everything up by running migrations on `template1` and every test gets a random database name. Works wonders.)
by djohnston | 2025-09-22 18:16:43+00:00
> and cancel left wing ones.

"Left ones" being, celebrating murder? Assuming you weren't living under a rock for the past decade, do you think celebrating murder is less severe than right-wing views (critical of BLM, critical of feminism, critical of pro-choice) that were routinely cancelled by left-wing institutions (social media, university) over this time frame?
by zahlman | 2025-09-22 18:17:02+00:00
In case you weren't attempting to make a point through irony, GP appears to be using "F5" informally as shorthand for "instruct your IDE to attempt to build and run the code". Presumably, that kind of documentation wouldn't normally literally say "F5" there unless a specific IDE had already been prescribed. The point was simply that the user shouldn't be required to do anything manual to set up the code, when starting from scratch, except perhaps to authorize the automated setup procedure.
by SigmaEpsilonChi | 2025-09-22 18:17:11+00:00
Thank you!! I recommend taking a look at the later levels, it goes beyond a middle-school level of complexity! I have seen everyone from graduate students to tenured math professors to Grant Sanderson get stumped by late-game SineRider. It doesn't really fit neatly into any specific age group or "grade level"

The map should zoom with scroll, is that not working?
by ux266478 | 2025-09-22 18:17:21+00:00
> We have railways running from West -> East, why not include power lines that can take power from energy farms in the West -> East?

Firstly, there is no such thing as an infinitely scaling system.

Secondly, because power transmission isn't moving freight. The infrastructure to move electricity long distances is extremely complicated. Even moving past basic challenges like transmission line resistance and voltage drop, power grids have to be synchronized in both phase and frequency. Phase instability is a real problem for transmission within hundreds of miles, let alone thousands upon thousands.

Also that infrastructure is quite a bit more expensive to build than rail or even roads, and it's very maintenance hungry. An express built piece of power transmission that goes direct from a desert solar farm to one of the coasts is just fragile centralization. You have a long chain of high-maintenance infrastructure, a single point of failure makes the whole thing useless. So instead you go through the national grid, and end up with nothing, because all of that power is getting sucked up by everyone between you and the solar farm. It probably doesn't even make it out of the state it's being generated in.

BTW the vast majority of the cost of electricity is in the infrastructure, not its generation. Even a nuclear reactor is cheap compared to a large grid. New York city's collection of transmission lines, transformers, etc. (not even any energy generation infrastructure, just transmission) ballparks a couple hundred billion dollars. Maintenance is complex and extremely dangerous, which means the labor is $$$$. That's what you're paying for. That's why as we continue to move towards renewables price/watt will continue to go up, even though we're not paying for the expensive fuel anymore. The actual ~$60 million worth of fuel an average natural gas plant burns in a year pales in comparison to the billions a city spends making sure the electrons are happy.
by testing22321 | 2025-09-22 18:18:18+00:00
So a thing has to be among the “world’s biggest” to be of note?

Of course not.

They don’t make as many vehicles or have the revenue of other auto manufacturers, but who cares.

What they do, they do very, very well. They lead to mass market EV adoption. Even if they crumble tomorrow their contribution is immense. Who cares about market cap, it’s all just gambling.
by pavel_lishin | 2025-09-22 18:18:37+00:00
> You can enable virtually free test driven development. Write the test names down and let the LLM implement them for you. You save 50% of your time and you get to go to town on implementation and or optimizations.

That's assuming that it writes good tests, and that you don't care to take the time to verify the tests it wrote, no?
by david-gpu | 2025-09-22 18:18:39+00:00
I went through that exact rabbit hole a couple of years ago, and after much watching the HowNot2 channel, and much reading other sources, I came to the conclusion that you only need to learn very few knots to do nearly everything. The specific knots you learn matter little as long as you select knots with a long eatablished safety record.

My personal short list are the following:

1. Joining two ropes (i.e. bend): Zeppelin bend, or Figure-8 bend. If the ropes have a very different diameter you will need a different knot, such as a Double Sheet Bend.

2. Holding on to an object (i.e. hitch): Two Round Turns & Two Half Hitches. More turns and half hitches make it more secure.

3. Making a fixed-diameter loop at the end of a rope: Figure-8 loop.

4. Making a fixed diameter loop in the middle of a rope: Alpine Butterfly, or simply take another piece of rope and do a Prusik Loop.

5. Grabbing onto a rope, such as when you want a loop that can be cinched down (i.e. friction hitch): Icicle Hitch. I personally do Round Turns & Half Hitches instead, and will die on that hill.

Another useful trick that can be done with a combination of the above is called a Trucker's Hitch. It is not so much a unique knot, but a common combination of the principles above.

For those who know about knots: please resist the temptation to nitpick and offer alternatives. Yes, there are many others. No, it doesn't matter. The knots above, or a combination thereof, covers 95% of everything you can do with rope, they are safe, and easy to verify.
by WorldMaker | 2025-09-22 18:19:12+00:00
Most of the debate here is that a lot of us don't find YAML anchors attractive. It can be one of the papercuts of using YAML.

I mostly agree with the article that with GitHub Actions specifically, I try to refactor things to the top-level "workflow" level first, and then yeah resort to copy and paste in most other cases.

I'm a little less adamant that GitHub should remove anchor support again than the original poster, but I do sympathize greatly, having had to debug some CircleCI YAML and Helm charts making heavy use of YAML anchors. CircleCI's YAML is so bad I have explored options to build it with a build process. Yeah, it does create new problems and none of those explorations got far enough to really improve the process, but one of the pushes to explore them was certainly that YAML anchors are a mess to debug, especially when you've got some other tool concatenating YAML files together and can result in anchor conflicts (and also other parts of the same YAML that depend on a particular form of how anchor conflicts overwrite each other, oof). I don't see GitHub Actions necessarily getting that bad just by enabling anchors, but I have seen enough of where anchors become a crutch and a problem.
by alwa | 2025-09-22 18:19:53+00:00
> Mostafavi told CalMatters he wrote the appeal and then used ChatGPT to try and improve it. He said that he didn’t know it would add case citations or make things up. He thinks it is unrealistic to expect lawyers to stop using AI. [...] “In the meantime we’re going to have some victims, we’re going to have some damages, we’re going to have some wreckages,” he said. “I hope this example will help others not fall into the hole. I’m paying the price.”

Wow. Seems like he really took the lesson to heart. We're so helpless in the face of LLM technology that "having some victims, having some damages" (rather than reading what you submit to the court) is the inevitable price of progress in the legal profession?

21 of 23 citations are fake, and so is whatever reasoning they purport to support, and that's casually "adding some citations"? I sometimes use tools that do things I don't expect, but usually I'd like to think I notice when I check their work... if there were 2 citations when I started, and 23 when I finished, I'd like to think I'd notice.
by hintymad | 2025-09-22 18:19:57+00:00
This reminded me of an interview I listened to by a startup founder talking about how his company integrates AI into all of its workflows. During the Q&A, he said that they could tackle any challenge simply by iteratively constructing better contexts for the AI. At first this sounded optimistic, but then it struck me that it was actually the ultimate pessimistic view of what current AI can do. His assumption seemed to be that software engineers have already implemented all the primitives humans will ever need. If that’s true, then the only task left is to phrase our instructions in the right way so the model can stitch those primitives together into a production system.
by nobodyandproud | 2025-09-22 18:20:02+00:00
> however people no longer necessarily invest where they live

This is a problem. The UK is experiencing what the US is experiencing so I hope you don't mind.

I'm all invested in the US. My citizenship, my family ties, my finances, and my language. Immigrating to a five-eyes nation would be far easier for me (racism aside).

There's no going back for me and my family, unless I want to immigrate out and start from the very bottom with absolutely nothing.

Because I'm a naturalized US citizen and I'm not white, I also harbor no illusions: There's every reason to believe that if this administration and its extremists continue down its trajectory, I and my family will eventually be subject to the worst.

I'm also not wealthy: So it's in my best interest to be a moderating voice, fight for the values that define my home, be seen giving back/paying it forward, and push for the best outcome for my home.

I'm not sure how to quantify the value add of this, and the wealthy transnationals who distinctly take without being a net positive--because they can so easily relocate--are a large part of the problem.
by mahirsaid | 2025-09-22 18:20:36+00:00
There is still so much to be done with LIdar. I myself am so intrigued of the output these devices present. The data and capabilities that are possible with this tech is amazing to me. Terrain scanning to autonomous vehicles the list goes on. Lasers are amazing breakthrough for mankind and breakthrough for the physics that involved, light that can be manipulated to seek alternative outcomes of data or real-world applications.
by cameldrv | 2025-09-22 18:21:09+00:00
It’s a lot easier to make an AI that highly reliably identifies dangerous road debris if it can see the appearance and the 3D shape of it. There’s a fair bit of debris out there that just looks really weird because it’s the mangled and broken version of something else. There are a lot of ways to mangle and break things, so the training data is sparser than you’d ideally like.
by milesvp | 2025-09-22 18:21:18+00:00
I know a lawyer who almost took a job in state government where one of the primary duties was to make sure that the punctuation in the bills going through the state legislature were correct and accurate. For her, part of the appeal of the job, was that it would allow her to subtly alter the meaning of a bill being presented. Apparently it is a non trivial skill to be able to determine how judges are likely to rule on cases due to say, the presence, or the absence of an oxford comma.

There was an entire team dedicated to this work, and the hours were insane when the legislature was in session. She ended up not taking the job because of the downsides associated with moving to the capital, so I don't know more about the job. I'd be curious how much AI has changed what that team does now. Certainly, they still would want to meticulously look at every character, but it is certainly possible that AI has gotten better at analyzing the "average" ruling, which might make the job a little easier. What I know about law though, is that it's often defined by the non average ruling, that there's sort of a fractal nature to it, and it's the unusual cases that often forever shape future interpretations of a given law. Unusual scenarios are something that LLMs generally struggle with, and add to that the need to creatively come up with scenarios that might further distort the bill, and I'd expect LLMs to be patently bad at creating laws. So while, I have no doubt that legislators (and lobbyists) are using AI to draft bills, I am positive that there is still a lot of work that goes into refining bills, and we're probably not seeing straight vibe drafting.
by BrandoElFollito | 2025-09-22 18:22:04+00:00
This would be interesting indeed, but I did not see such a discrepancy. I tried a few cities around Massif Central (starting with Clermont Ferrand) and the coverage is not bad.

By not bad I mean that there is a way to reach the places bu choosing cities around the region. I did not say that it is easy :)
by Analemma_ | 2025-09-22 18:22:17+00:00
There's something grimly hilarious about knee-jerk demands for jail time for [other profession] for using AI, when a bunch of us here are eagerly adopting it into our own workflows as fast as we can.

Why jail time for lawyers who use Chat-GPT, but not programmers? Are we that unimportant compared to the actual useful members of society, whose work actually has to be held to standards?

I don't think you meant it this way, but it feels like a frank admission that what we do has no value, and so compared to other people who have to be correct, it's fine for us to slather on the slop.
by quickthrowman | 2025-09-22 18:22:36+00:00
For someone who had to attend 6+ years of school and had to pass a professional licensing exam with ethics questions? Yes, I do. $10,000 is one week of billable hours at $250/hr.

Do you think a Civil Engineer (PE) should be held liable if they vibe engineered a bridge using an LLM without reviewing the output? For this hypothetical, let’s assume an inspector caught the issue before the bridge was in use, but it would’ve collapsed had the inspector not noticed.
by nyrikki | 2025-09-22 18:23:10+00:00
> Larger models (e.g., Opus 4.1) often fail on semantic or algorithmic correctness in large, multi-file edits, whereas smaller models (e.g., Qwen 3 32B) more frequently fail due to issues in syntax and formatting, tool use, or context management.

While I haven’t dug into the details of this benchmark, this absolutely matches my personal experience.

Assuming “semantic correctness” is in the sense of Rice and runtime behavior.

While syntactic correctness has dramatically improved, security and architectural erosion and other long term issues have not.

Unfortunately Rice’s theorem also applies to finite programs in finite time too.

Actually it can apply to total functions in the general case.

I am still optimistic that coding agents will provide value long term in some fashion.

But the open domain frame problem simply reduces to the halting problem, yes and humans struggle with it too.

But fundamentally, PAC learning has to be reduced to _trivial_ problems, with only T/F.

We have found clever ways to work within these s limitations, but they still exist.

Hopefully we find clever ways to keep humans engaged with the code, while gaining the potential force multiplier that ML may offer.

The long tailed problems are particularly important, and while human SREs make mistakes and organizations often have constraints that add to the problem, SREs do a lot more to avoid those long tailed problems than they are given credit for.

IMHO that has always been one of the hardest parts of the industry and a true measure for what makes great team members.

Unfortunately the metrics and incentives often don’t capture that value.
by angiolillo | 2025-09-22 18:23:12+00:00
Great question!

If you let someone flounder on one task indefinitely then you don't learn anything about subsequent tasks. But if you correct them too quickly you won't uncover the other approaches they would have tried to complete the task. Most research plans define cutoffs such as:

1. Participant expresses extreme frustration or gives up

2. A couple minutes have elapsed from the first failed attempt

3. Participant unsuccessfully attempts three distinct approaches

If the test reaches one of your cutoffs then the interface/docs have failed the task and the moderator can skip to the next task or question. Sometimes they'll also offer to show the participant the expected solution or explanation.
by dwaltrip | 2025-09-22 18:23:15+00:00
I remember like 10 years ago seeing some people getting really excited by CRDTs. I was deeply confused about how the software would magically know what the correct thing to do would be when 2 people made edits that directly conflicted with each other…

You can’t know what to do without talking to the people involved, as they have to decide what makes sense for end goal. It’s mostly a social / collective action problem, not a purely technical one.
by flkiwi | 2025-09-22 18:24:04+00:00
Morgan and Morgan is a plaintiffs firm specializing in personal injury (though they have other areas of practice). For those not familiar with the US system, if someone hits a victim with their car, a victim slips and falls in a store, etc. and the victim sues, Morgan and Morgan commonly handles that type of case for the victim. Basically the usual "Americans will sue each other for anything" type of law firm (though I don't strictly mean that to be criticism). Morgan & Morgan's business model places a heavy emphasis on convincing the defendant to settle before litigation. They also depend heavily on advertising. Regardless of what happens next, this story is a free "Morgan and Morgan stands up to the big guy for you" headline.

Apropos of nothing, the firm's founder, John Morgan, has been instrumental in attempting to legalize marijuana in Florida, which some have identified as a potential, but very funny, conflict of interest given the type of work Morgan and Morgan does.
by mihaic | 2025-09-22 18:24:05+00:00
The needed inovations for any potential new business model of the internet are not technical.

Even if some system of making crawlers pay for a content access, how could you be sure it's never used without your permission? It's not like DRM solved internet piracy.

And even if a technical solution were possible, it's natural to see this level of cynicism of how an internet giant would handle it.
by ben_w | 2025-09-22 18:24:25+00:00
Tokens that can be purchased for $10 may or may provide the purchaser with almost any dollar denominated result, from negative-billions* to postive-billions**.

Right now, I assume more the former than the latter. But if you're an optimistic investor, I can see why one might think a few hundred billion dollars more might get us an AI that's close enough to the latter to be worth it.

Me, I'm mostly hoping that the bubble pops soon in a way I can catch up with what the existing models can already provide real help with (which is well short of an entire project, but still cool and significant).

* e.g. the tokens are bad financial advice that might as well be a repeat of SBF

** how many tokens would get you the next Minecraft?
by cs702 | 2025-09-22 18:24:47+00:00
It's not exactly the same, but I'm reminded of Nortel Networks and Lucent Technologies, two makers of networking and telecom equipment that grew very rapidly during the dotcom bubble, in the mid to late 1990s, and subsequently got in financial trouble. Nortel went bankrupt. Lucent was acquired for peanuts.

Before their collapses, both Nortel and Lucent had been aggressively "investing" in and "lending" to a bunch of telecom/internet/dotcom startups that would turn around and use the funds to... buy equipment and services from Nortel and Lucent. In fact, Nortel and Lucent would require companies to turn around and use the funds to buy equiment and services!

For a while, this round-trip of accounting trickery looked like a win-win strategy for everyone involved: The startups got to show the world they were able to raise more funds. Nortel and Lucent got to show rapid revenue growth. The end, of course, wasn't pretty.