MurphyA/hacker-news huggingface dataset:
by genocidicbunny | 2025-08-03 06:13:06+00:00
> And are you just talking undeveloped woods or something? I'm talking parks, as in playgrounds, soccer fields, baseball fields, water fountains, stocked fishing ponds, etc.

All of the above. Well, maybe swap baseball fields to basketball courts.

> Neither are mine. I didn't arrange a play date. My kids just went outside and played with the kids out there. We just go down to the park and play on the playgrounds with the other kids. We just hop on the bus and head to the downtown and see what's happening. We just go to the library. We just stopped by the farmers market. We just go to the pool. Maybe shoot some messages to some friends we're heading that way, but not necessarily something planned well ahead of time.

If it's anything like my experience in the US, the other side -- hosting such events, is regimented and calendarized.

> I don't know where you came from. But where I'm from, the average household income isn't too far off from the current national average. This isn't some ultra wealthy place.

When I was a kid, $3000/annum would have put you in the upper 2-3%.

I've since lived in places with very nice public spaces, what most would consider to be enviable 3rd places. Yet it all still feels so artificial, so made up. It feels designed, not organic, and the behaviours that I observe follow that.
by bsder | 2025-08-03 06:13:22+00:00
> I have about two weeks of using Claude Code and to be honest, as a vibe coding skeptic, I was amazed.

And, yet, when I asked it to correct a CMake error in a fully open source codebase (broken dependency declaration), it couldn't work it out. It even started hallucinating version numbers and dependencies that were so obviously broken that at least it was obvious to me that it wasn't helping.

This has been, and continues to be, my experience with AI coding. Every time I hit something that I really, really want the AI to do and get right (like correcting my build system errors), it fails and fails miserably.

It seems like everybody who sings the praises of AI coding all have one thing in common--Javascript. Make of that what you will.
by teiferer | 2025-08-03 06:13:35+00:00
Please take a moment and consider what exactly you responded to and how. I'm pointing you out specifically since you seem to be familiar with the concepts of good faith vs bad faith arguments.

The post you responded to asked about a specific aspect, namely political power, of math PhDs.

Your response was to state that surely other PhDs have bias in their work.

Are you not realizing that that's quite obviously a bad faith argument? Somebody claims that A has B and your respond that C had D, insinuating GP made connections between A and D or C and B. They didn't.

It's quite common to argue like that, but you did throw around the "bad faith" term. You need to measure your own behavior by that standard when accusing others.
by franga2000 | 2025-08-03 06:13:51+00:00
Where did anyone say that???

As for trucks having the same speed limit as cars in general: 1) a lot of the time there is a lower limit, 2) the truck itself has a lower max highway speed, 3) there a far fewer trucks on the road so it doesn't matter a much, 4) they are driven by professional drivers with things like electronically enforced daily driving limits, so many of the common causes of accidents are less likely.
by bsmit272 | 2025-08-03 06:13:54+00:00
Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical.

[...]

In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data.

[...]

First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different “States”. We define the “State” of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors.

[...]

The evaluation results of the real dataset show that the predictive ability of Prophet is improved amazingly, realizing a high recall and low false-positive rates while providing sufficient response time for the processing of failed SSDs.
by samgutentag | 2025-08-03 06:14:14+00:00
I totally get this side of things. I see the benefits of Agentic coding for small tasks, minor fixes, or first drafts. That said, I don't understand the pseudo-tribalism around specific interfaces to what amounts to only a few models under the hood and worry about what its doing for (or not doing for) junior devs.

Also, if we could get AI tooling to do the reviews for us reliably, I'd be a much happier developer.
by landl0rd | 2025-08-03 06:15:38+00:00
I’m not especially in favor of all of Khan’s actions but this was an accretive acquisition prospect for Adobe in a way that makes it worth more to them vs as a standalone company. Think how Urchin Analytics was worth a lot to Google but less by itself.

Also, Adobe was massively overpaying, arguably even if you consider that. Even if you assume it was due to seeing Figma as a huge competitive threat the stock nosedived due to the acquisition price.
by seuros | 2025-08-03 06:16:14+00:00
Not AI-generated. Not everyone is born writing flawless English.

If it sounds like an LLM, maybe it is because people like me had to learn how to write clearly from LLMs because English is not our first language.

I could’ve written in my native tongue, but then someone else will have complained that not how english is structured.

Also, the story is real. Just because it is well-structured doesn't mean it's fiction. Yes, i used AI to resort it, but i can assure you that no AI will generate the Piers Morgan reference.
by literalAardvark | 2025-08-03 06:16:24+00:00
You can greatly reduce pull out crashes by "presenting" when you see waiting cars, a defensive riding technique that more or less means driving in an S so that you can be noticed.

There's a surprising amount of agency involved in crashes. Learn blind spots and stay out of them, don't ride on the side of the road, only do things cars can't do when you're 200% sure the car you're passing won't swerve/turn/overtake. This includes accelerating like a hypercar, which many motorcycles can do.
by drewbeck | 2025-08-03 06:16:27+00:00
Per-usage pricing discourages use which limits how critical a service can be to your life or workflow. These companies want you to rely on the service such that you’ll pay the price. One customer might use it once a day and find the price reasonable; another may use it 10 times a day and still find the price reasonable. This kind of broad pricing allows for this variation.
by DocTomoe | 2025-08-03 06:16:39+00:00
> Mentally healthy people will never be less lonely as long as they know they are talking to a model.

This isn't an empirical claim; it’s a definitional loop. You've defined "mentally healthy" in a way that makes your conclusion true by default. It's like saying, "Only mentally ill people commit suicide, because a sane person wouldn't do that." You've smuggled your conclusion into your premise. It doesn't prove anything; it just circularly reinforces your bias.

If someone who passes every clinical benchmark for mental health reports feeling less lonely after talking to a model, your definition simply reclassifies them as "not mentally healthy" to preserve the thesis. That's unfalsifiable - Karl Popper would call it a pseudo-theory.

If you want to know whether talking to a model can reduce loneliness in mentally healthy people, you have to measure loneliness directly - not redefine "healthy" so your preferred answer is guaranteed.
by akoboldfrying | 2025-08-03 06:16:46+00:00
> In Chinese each word is a unique character. So there is no phonics system for Chinese.

We know. Their point is that the fact that Chinese children succeed in learning to read (non-phonetic) Chinese well contradicts the core argument of TFA, which is that phonics is necessary to learn to read well.

I'm very pro-phonics, but this is nevertheless a compelling argument against it being necessary. If you know of another explanation for why Chinese reading education seems to work well despite the lack of phonics, please give it. (Or is it that learning to read Chinese actually is a big problem in China?)
by bbminner | 2025-08-03 06:17:27+00:00
Looking at Molmo description:

"Our key innovation is a new collection of datasets called PixMo that includes a novel highly-detailed image caption dataset collected entirely from human annotators using speech-based descriptions, and a diverse mixture of fine-tuning datasets that enable new capabilities. Notably, PixMo includes innovative 2D pointing data that enables Molmo to answer questions not just using natural language but also using non verbal cues. We believe this opens up important future directions for VLMs enabling agents to interact in virtual and physical worlds. The success of our approach relies on careful choices for the model architecture details, a well-tuned training pipeline, and most critically the quality of our newly collected datasets, all of which we have released."

This is a solid engineering project with a research component - they collected some data that ended up being quite useful when combined with pre-existing tech. But this is not rocket science and not a unique insight. And I don't want to devalue the importance of solid engineering work, but you normally don't get paid as much for non-unique engineering expertise. This by no means sounds unique to me. This seem like a good senior-staff research eng project in a big tech company these days. You don't get paid 250M for that kind of work. I know very talented people who do this kind of work in big tech, and from what I can tell, many of them appear to have much more fundamental insight and experience, and led larger teams of engineers, and their comp does not surpass 1-2M tops (taking a very generous upper bound).
by SilverElfin | 2025-08-03 06:18:17+00:00
Aren’t they basically the same now, unchecked in power? They’re able to muscle into anything using their money and incumbent strength. They can use existing products to sell new ones. They can copy products and give them away. They can wage legal wars.

Personally, I think it’s unacceptable to have any business worth 4T as a single company. It’s not good for competition and for customers in the long term. But today’s anti competitive tactics are different from the simpler monopolistic tactics of the past. We need all new regulations to deal with it.
by praash | 2025-08-03 06:18:22+00:00
I really enjoyed spending a bit of my morning with these two implementations of the concept.

I prefer your idea of treating the question as a proper puzzle with a 1-submission limit. The "calculator" UI took a whole 2 minutes to understand initially, but I really liked seeing the chain instead of having to mush all the factors in my head.

It's really nice to see the correct answer broken down to get a feel about the real numbers!

The current question's answer seems to contain big errors in magnitude in its factors:

"How many kilograms of skin does a human shed in their lifetime?"

Skin cells shed per day: 5e8 / day Mass of one skin cell: 3e-6 g Years in a lifetime: 80 yr Grams to kilograms conversion: 1 kg / 1000 g

The final "correct" result is displayed as 44 kg, but these values result in 44,000 kg. It's also odd to show a conversion factor for kg/g, but not day/year.

The first two factors correspond to shedding 1.5 kg/day, which is definitely unrealistic!
by drusenko | 2025-08-03 06:20:30+00:00
Your idea of there being competition for human relationships is super fascinating. In my own life, there are fun/easy relationships, and there are those which push me to think deeply and differently, for any number of reasons.

In that vein, doesn’t “competition for relationships” necessarily breed egocentrism above all else? The winning relationship will give you what you want, but not what is necessarily true…

In that vein, you might also consider that the commenters you’re replying to may be worth engaging intellectually with more deeply purely based on the fact that they’re presenting divergent views that are uncomfortable.

Based on how we’ve designed AI to date and how you describe it in terms of optimizing for self enjoyment for each individual (and difficult to argue most will choose that for themselves), it’s hard to see a world where AI can push productive conflict the way humans can.

Then again, I might just be a flawed human who doesn’t fully understand the point you are trying to make and is extrapolating from my own biases, flaws, experiences, and the limited sample size I have of your point of view.
by ggm | 2025-08-03 06:20:43+00:00
Mate, if you think we're paying amortised costs you either work in American pharma or marketing. We're paying fat shareholder returns.

"A few dollars" forsooth. My 2TB SSD cost $150 AUD and was (I believe) immensely profitable to everyone down the supply chain. The same spend gets you 16GB of packaged DDR ram and I think we can both see there is no linear relationship between the DDR chip cost, in GB and the 100x denser storage needed for SLC flash. This is not about vlsi density or number of chips. I'm not paying $15,000 more for my SSD.

"The prices are the prices"
by disambiguation | 2025-08-03 06:21:04+00:00
Insufficient data for a meaningful answer. We don't really have a clear grasp of the relative benefits and harm yet.

Automobiles offer enormous gains in convenience and productivity, but at the cost of a non-negligible mortality rate as well as environmental impact. Society deems this a net-positive.

The two major unknowns with "AI companions" are: can people be trusted with this level of autonomy, and is the massive centralization of personal data going to result in abuse and exploitation.

I doubt any amount of discourse can answer questions of "how much", but perhaps the conversation can anticipate potential harm and "invent the traffic light" before the car crash equivalent becomes common place.

To do that, we have to first answer: what are the characteristics of the failure-mode of an AI companion?
by DocTomoe | 2025-08-03 06:22:03+00:00
Realistically, that’s not far from how a human brain works - we rely on a deep corpus of pre-learned patterns (largely set in early childhood) and continually refresh it with new inputs held in short-term memory, reinforced through repetition. If LLMs reach the point where they can integrate their "short-term" context (RAG, etc.) into updated long-term weights more regularly, they’d be functionally simulating that aspect of human cognition.

(Disclaimer: I am not a neuroscientist. The model is massively simplified. But I believe the broad strokes are accurate.)
by teiferer | 2025-08-03 06:22:09+00:00
Excellent observation. Either side can (and does) easily accuse their opponents of this.

Some don't like anti-immigrant, anti-gay, anti-climate rethoric. Others don't like trans-rights, anti-hate-speech, anti-christianity rethoric. For either side, those are not real problems which their opponents are concerned about.

That's an underappreciated aspect of current public discourse.
by beng-nl | 2025-08-03 06:22:47+00:00
Yeah I dont think Linux will take over, nor do I want it to. With “win” I just meant for Linux desktop to gain a market share enough for vendors (software and hardware) to be hopefully obliged to support it.

That said, we’re already seeing problems caused by Linux “monoculture” (the very thing we didn’t like about windows in the 90s) - OSS supporting just Linux and no other open source operating systems.

Or as I gleaned from the jargon file “all the world’s a vax.”

http://catb.org/jargon/html/V/vaxocentrism.html
by hyperman1 | 2025-08-03 06:23:16+00:00
You're asking me to tell about a homework from 1988, in dutch, when I was 8 years old. I think the last sentence was 'honderdduizend apen hingen daar te gapen' being translated to 'titi titi ta ta. Titi titi ta ta'.

The weird thing is: I could do it, even if I had no idea what I was doing. There was some pronunciation that seemed natural. My answers were mostly right ( Or maybe I got a good grade just for turning something in?).

Also, the teacher was a really nice lady, she was good with the piano and knew music, and she did teach us what she was supposed to. I have fond memories for her lessons. She succeeded.

I just think, the first lesson being a bit if a sampler, she didn't want to scare kids away. Artsy people sometimes have learned that math must be hard. So she accidentally oversimplified for me. I have no idea if the other kids felt the same. She might even have self-corrected starting the third lesson.
by whoknowsidont | 2025-08-03 06:25:57+00:00
I might still just be totally misreading things but I don't see how the assignment above is a valid, let alone a normal application of the Kodály Method?

Seems like the teacher really misunderstood what it was/meant for. I could totally be wrong here.

Translating sentences on paper (and again, how? why? by what metrics?) seems like the exact opposite of what the Kodály Method utilizes and its underpinning principles?
by ggm | 2025-08-03 06:27:03+00:00
"Must" but .. "wont" which means wasn't "must"

I think the "wargames" quote, immortalised in Unix /usr/games applies here.

it should be clear: I think he's wrong. I see limited applicability in purpose specific AI in their product.

Look what happened to the smart image scanning. In fact, look at the critiques of AI assisted photography.

Notice that Apple hasn't died yet, despite not having significant presence in data centers.

Small model AI embedded in devices? Sure. It's glitter. Real AI consumes racks and racks and racks of power and cooling. Apple is nowhere near the top either as a tenant or a host and if Tim really meant this the Apple DC real estate would be through the roof.

Apple has shitloads of money. It needs to find ways to earn on it. When declared land holding worldwide ramps up and isn't hotel or city listed property investment, ring me back.
by matco11 | 2025-08-03 06:30:28+00:00
I love basketball, soccer, and tennis… but you guys have no idea how powerful I have found to share stories like this with my young kids.

Yes, I want them to excel in sports, but these articles provide a crucial counterweight to the all-too-common narrative that becoming a pro athlete is the ultimate dream. Instead, these stories show that being exceptional in STEM isn’t just something you do because you are curious, you find it interesting, you enjoy it (all great motivators), or to please parents and teachers (generally, probably, lesser quality motivators): these stories show that being exceptional in STEM can open doors to exciting, high-impact careers.

It’s been amazing to watch my kids begin to reframe STEM not as the “sensible” thing to do, but as something genuinely cool, aspirational, and full of opportunity.
by nobodywillobsrv | 2025-08-03 06:30:30+00:00
What are the actual data on this now? A lot of people I know could be doing AI and I don't think there is that much special in the experience but yes a few years of correct experience matters I suppose. But it does seem bizarre ... Is buying a chance at improving by months of time currently worth billions? Maybe.

It just seems very short cited right now.

Or should I and my friends all be targeting 7-8 figure jobs?
by brunooliv | 2025-08-03 06:31:21+00:00
Am I the only one who uses CC in a one-shot fashion? I rely on what has been generated by the init slash command at the root of my monorepo and then when I have a complex task I manually create a md file called CONTEXT.md inside the package/area where I want to implement the new task. I try to be as detailed as possible, think like writing a ticket for a junior dev. Then my only command for CC is something like: “Focus on the project structure described in CLAUDE.md and work through the task planned in CONTEXT.md. Follow current codebase standards, keep code reusable and easy to follow and modify.”

This is like 99% of my CC interactions working on top of a well structured codebase and it just works perfectly for almost any task I throw at it.
by whoknowsidont | 2025-08-03 06:31:22+00:00
>You're asking me to tell about a homework from 1988, in dutch, when I was 8 years old

Well asking certainly, but I'm not demanding? I don't know, seems like a very weird application. I certainly don't know ANY dutch, which doesn't help.

Is it just a "rhythm" mapping exercise based on the syllables? I probably read the first post a little bit to literally.
by khuey | 2025-08-03 06:32:02+00:00
> Or is it that learning to read Chinese actually is a big problem in China?

Historically it was. Reforming the writing system (potentially even ditching it entirely in favor of a Latin/etc derived script) to improve literacy rates was a major topic among Chinese intellectuals during the 20th century.

Some combination of character simplification, reading and writing the vernacular instead of "Classical Chinese", brute force, and modern technology has made this less acute. But it still is not unusual for even educated native Chinese speakers to simply not remember how to write some uncommon character. (You will see this in English occasionally too, of course. I have to think twice when I write rendezvous.)
Show HN: Arch-Router – Aligning LLM Routing with Human Preferences
by honorable_coder | 2025-08-03 06:32:04+00:00
Hi HN — we're the team behind ArchGW [1], an edge and service proxy for agents written in Rust, and we just recently published our research on LLM Routing: https://arxiv.org/abs/2506.16655

As teams integrate multiple LLMs - each with different strengths, styles, or cost/latency profiles — routing the right prompt to the right model becomes a critical part of the application design. But it's still an open problem. Most routing systems fall into two camps:

- Embedding-based routers use intent classifiers — label a prompt as “support,” “SQL,” or “math,” then route to a matching model. This works for simple tasks but breaks down in real conversations. Users shift topics mid-conversation, task boundaries blur, and product changes require retraining classifiers.

- Performance-based routers pick models based on benchmarks like MMLU or MT-Bench, or based on latency or cost curves. But benchmarks often miss what matters in production: domain-specific quality or subjective preferences like “Will legal accept this clause?”

Arch-Router takes a different approach: it decouples route selection from model assignment. Developers can write route policies using a domain-action taxonomy (like "engineering" or "image editing" respectively), and the router maps the prompt (and conversation context) to those policies using a lightweight 1.5B autoregressive model. No retraining, no fragile if/else chains. We built this with input from teams at Twilio and Atlassian. Arch-Router handles intent drift, supports multi-turn conversations, and lets you swap in or out models with a one-line change to the routing policy. Full details are in our paper, but here's a snapshot:

Specs:

- 1.5B params — runs on a single GPU (or CPU for testing)

- No retraining needed — point it at any mix of LLMs

- Routing can be cost, latency or quality aware based on your preferences

- Outperforms larger closed models on our conversational routing benchmarks (benchmarks in the paper)

Links:

- ArchGW (open source edge and service proxy for agents ): https://github.com/katanemo/archgw

- Model + code: https://huggingface.co/katanemo/Arch-Router-1.5B

- Paper: https://arxiv.org/abs/2506.16655