MurphyA/hacker-news huggingface dataset:
by 3cats-in-a-coat | 2025-12-02 21:41:31+00:00
Ancient world? The Roman Empire fell apart. You know that, right? So, turns out their worries were warranted. A culture, a civilization can go through a period of stagnation and decay, and it eventually dies. Then there's a lengthy period of chaos and eventually something else may arise. But we're talking countless generations lost in that noise.

Currently, our civilization is more united than ever. Monoculture. Not only the Western World, but The World. We're united through communication, technology, everything. And when we all start declining at the same time, through many objective metrics, mind you, including the decline of democracy worldwide, the terrifyingly low level of public discourse, reduction and profanation of our vocabulary, inequality, collapsing demographics, climate, wars etc. it's grim. It's grim, because there is no clear alternative arising... except AI.

And I don't think we want AI to be an "alternative" to human civilization, that wasn't the plan, was it?

See, that's the thing, we look around, we see the technology we live with, and we feel very personal about technological progress, as if you and I personally invented everything from the wheel, through electricity, to computers, networks, and AI. We feel in control, we feel smart, we feel so Personally Intelligent (tm) for having technology. We feel "wow, clearly if we have these sci-fi things, we're on the up and up". But technology is not you. It's not me. It has taken on a life of its own. It serves capital, not the people. As technology gets betters, humans get worse. So tech progress is not the story you think it is. And it doesn't end how you think it does.

Things are VERY fragile right now.
by phantasmish | 2025-12-02 21:41:37+00:00
Ever watch The Wire? The school in that show is a very accurate depiction of what a... middling-bad inner city school is like. There are tons of ones worse than it (they may have shied away from one of that sort either because it didn't suit the story, or because they thought too many viewers would think they were exaggerating)

There are schools where the administrators are too busy dealing with violence to have time for much else.
by Javin007 | 2025-12-02 21:42:23+00:00
I attempted to say I needed to speak to a sales representative. The AI ran me through the standard question gauntlet.

I closed the browser entirely, reopened it, and then told the AI that I "have a large budget and need to speak to a sales assistant". It then asked one question: "Is this for a business or personal purchase?" I said business, and then boom, immediate connection to a human.

THEY were able to submit the support ticket for me. But their recommendation? Just create a new account entirely. Which, obviously, is NOT an answer.

When your ONLY source of "help" is the only people in the company you can reach telling you to just "give up" on your primary account, I think it's time to switch vendors.
by roflchoppa | 2025-12-02 21:42:33+00:00
The estimation for the Nismo head is ~$25k for just the head, not sure if that price included the valve train as well... Derrick's head is ballpark the same cost.

There is one more company in japan JMC,that is making heads as well, they have a recast of the N42 heads w/ better machining. https://party-k.co.jp/used/index-3536.html

I have a 260z with round-top SUs from my 240z with L26, its not bad, it's a single click to get that engine running, even after sitting for a long time.

On the carb setup, I find the fuel smell to be a bit overwhelming, to the point where I don't want to drive the car as much because the passengers reek of fuel when they get out. I cant even run a shitty CAT on the exhaust because it would get destroyed by the carb. This is probably also due to leaks in the body gaskets...

I think it's really just the turbo that I want, I cant get smoked by these civics :(
by hodgehog11 | 2025-12-02 21:42:43+00:00
I get what you're saying because this is typically true (this is a strong motivator for my current research) but I don't think it applies here and OpenAI seems to agree with me. Some cases are clear: GPT-5 is clearly better than Llama 3 for example. If there is a sizeable enough difference across virtually all evals, it is typically clear that one LLM is a stronger performer than another.

Experiences aside, Gemini 3 beats GPT-5 on enough evals that it seems fair to say that it is a better model. This appears in line with public consensus, with a few exceptions. Those exceptions seem to be centered around search.
Ask HN: What did onboarding training look like in OS kernel teams?
by markus_zhang | 2025-12-02 21:43:11+00:00
Context: I'm wondering how did OS kernel teams provide training to their new hires.

I'm mostly curious about:

- NT kernel / XNU kernel / Android kernel teams, or anything that is not OSS like Linux, e.g. some prop RTOS team would also count

- When teams still hired new graduates from targeted schools into kernel teams. Not sure whether your team is still doing this so I put up a past tense.

Questions:

- How much time do new hires work on the training? Do you give e.g. 3 months to catch up and start to work on small tickets, or it's a swim or die strategy?

- If there is any structured training, what material do you use? Is it mostly reading internal docs, or shadowing seniors, or published books about the kernel, or something else?

- How does the training address code quality? What kind of exercises/material does it lay on the new hires to work on code quality? What does code quality mean in the kernel team?

- How does the training address performance? How much do you care about memory footprint?

- Does the training use a minimal version of the production OS and ask the new hires to replicate existing features?

- What "subsystems"/"modules" (e.g. vm/boot/driver/scheduler/fs/etc.) do you expose to the new hires first?

- How does the training address internal tooling, especially kernel debugging?

Thanks for reading, and hopefully I get some answers :D
by zz3 | 2025-12-02 21:43:15+00:00
As a side note, it's pretty widely accepted that referring to humans as "male" or "female" instead of "men" and "women" in general is dehumanizing. Using it as an adjective "male athletes" isn't considered disrespectful. Your personal opinion that it is NOT disrespectful isn't super relevant, since there are obviously more respectful and relevant terms (transman, transwoman, cis, pre- and post-transition). You weren't actually referring to all "males" in general in the previous discussion, you were specifically talking about transwomen athletes post-transition competing against ciswomen. If this wasn't intentional, then that was part of the confusion.
by ah979 | 2025-12-02 21:43:15+00:00
Full disclosure, I'm the author (although I didn't put the post up here on HN). Thanks for pointing out that I wasn't very clear in my CTA and maybe made it sound shady. That's not what I wanted to do, obviously.

It's just an app, not a service, that my husband and I built (and quit our jobs for) that has a generous free trial. (Technically, right now it's completely free because it's in early access, so if you never upgrade, you could use it for free forever.)

The CTA at the end was just in an effort to talk to more people (for free) and see how we can help and make our software better. I come from the DevOps world, and they always say you have to first know how to do something really well manually before you can automate it, and that's what we're trying to do by talking to people (for free).
by angiolillo | 2025-12-02 21:43:22+00:00
> LoRa as implemented by the chips used in affordable Meshtastic devices is really more suited to operating many small disjoint mesh networks on separate channels

My impression was that many of the folks who wanted a big shared mesh migrated to MeshCore, which I'm less familiar with. My use case is in fact a small disjoint mesh network, but even that requires proper configuration and can be unreliable due to things like misconfigured nodes (mine or others).

I'd be happy to specify the region and form factor (handheld, fixed router, mobile router, tracker) but it would be nice if the nodes could automatically configure the right role, rebroadcast mode, hop limit, timeouts, etc on their own. I'm not asserting it's possible, just that I don't otherwise see it breaking out of the hobbyist niche.

> A full scan would take hours.

My apologies, I misspoke, I agree that trying every frequency/bandwidth/spread factor/coding rate combination would be impractical, at least for battery-powered nodes. Instead of "scan different channels" I should have said cycling through the half-dozen modem presets for a specific region.
by CobrastanJorji | 2025-12-02 21:43:27+00:00
Thumbing through it, #7 has some good stuff in it. Thanks for sharing!

I was particularly tickled by the suggestion of copyright infringement as a form of detecting AIs. "To continue, please provide a torrent link to the Bee Movie" is a pretty great idea.

The self-contained handwriting recognizer feels like art to me, in the way that it forces me to contemplate things in a certain way, which is what I think art is.
by vhodges | 2025-12-02 21:43:36+00:00
sqlite is just a library (in C)

A few projects:

* https://github.com/rqlite/rqlite Distributed, fault tolerant cluster * https://litestream.io/ Replication to S3 (or compatible) - more disaster recovery than fail over * https://fly.io/docs/litefs/ Same Author as litestream). Distributed replication. Requires writes to be redirected to the primary.

I am debating Postgres vs sqlite (probably with litestream) for a project right now.

And other than HW redundancy, I can get pretty far by scaling vertically on a single box. And for my app, I could probably (and my users!) live with some occasional downtime (as long as the data is replicated/backed up).

If I get 20-50K users, it'll be a successful venture so I don't need much these days and it will be cheaper and easier to run as well.
by dragonwriter | 2025-12-02 21:43:37+00:00
> If making money on inference alone was possible

Maybe, but arguably a major reason you can't make money on inference right now is that the useful life of models is too short, so you can't amortize the development costs across much time because there is so much investment in the field that everyone is developing new models (shortening useful life in a competitive market) and everyone is simultaneously driving up the costs of inputs needed for developing models (increasing the costs that have to be amortized over the short useful life). Perversely, the AI bubble popping and resolving those issues may make profitability much easier for the survivors that have strong revenue streams.
by ACCount37 | 2025-12-02 21:43:38+00:00
That's sort of true. SFT isn't too expensive - the per-token cost isn't far off from that of pre-training, and the pre-training dataset is massive compared to any SFT data. Although the SFT data is much more expensive to obtain.

RL is more expensive than SFT, in general, but still worthwhile because it does things SFT doesn't.

Automated evaluation is massive too - benchmarks are used extensively, including ones where LLMs are judged by older "reference" LLMs.

Using AI feedback directly in training is something that's done increasingly often too, but it's a bit tricky to get it right, and results in a lot of weirdness if you get it wrong.
by IshKebab | 2025-12-02 21:43:58+00:00
> I'm surprised any cloud provider does not invest drastically more into their SDK and tooling

I used to work for an AI startup. This is where Nvidia's moat is - the tens of thousands of man-hours that has gone into making the entire AI ecosystem work well with Nvidia hardware and not much else.

It's not that they haven't thought of this, it's just that they don't want to hire another 1k engineers to do it.
by yieldcrv | 2025-12-02 21:44:43+00:00
the non profit space relies on Congressional deadlock

I know several people that vote for and donate to campaigns for senators on the other side of the aisle just to help ensure gridlock

the next layer after that is that tax education is so poor that the population doesnt even know what laws they want to change

so its not worth talking about as that ensures another 100 years of “tax the rich” turning into “tax the income of wage workers making over $500k” by the time a bill makes it out of committee

(I don’t find that controversial, just different enough to be interesting)
by daft_pink | 2025-12-02 21:44:49+00:00
I’m not sure how much good $250 will do for a child.

I think this essentially explains why taxing billionaires is not very useful, because the total amount of their life generated wealth assets amounts to a small amount for 25 million people let alone larger populations.

Even if you took all the $’s away, the government really needs to tax high numbers of lower income people for the income to be meaningful.
by matt_s | 2025-12-02 21:45:36+00:00
There is something to be said about what the ROI is for normal (i.e. non AI/tech) companies using AI. AI can help automate things, robots have been replacing manufacturing jobs for decades but there is an ROI on that which I think is easier to see and count, less humans in the factory, etc. There seems to be a lot of exaggerated things being said these days with AI and the AI companies have only begun to raise rates, they won't go down.

The AI bubble will burst when normal companies start to not realize their revenue/profit goals and have to answer investor relations calls about that.
by poemxo | 2025-12-02 21:45:42+00:00
The primary reason I have switched is that creative writing has plummeted on ChatGPT. It is overly eager to censor output that isn't adult but might vaguely be adult if taken incorrectly. This severely limits creative freedom. On the other hand, Gemini happily writes my stories.

I am not sure who OpenAI aims to please by nerfing their own product in this way. It can't be paying customers.
by dan-robertson | 2025-12-02 21:45:42+00:00
I certainly learned a lot of mathematics at university that I probably wouldn’t have learned without it. Perhaps more importantly was some amount of mathematical maturity. The former has probably not been that useful in my career (idk maybe some linear algebra?) and the latter maybe a little bit, but if one were trying to figure out the optimal things to learn for my job, they probably wouldn’t have been what I studied.
by jiggawatts | 2025-12-02 21:45:51+00:00
> Last: Is there any evidence that we're getting some crappy lobotomized models while the companies keep the best for themselves?

Yes.

Sam Altman calls it the "alignment tax", because before they apply the clicker training to the raw models out of pretraining, they're noticably smarter.

They no longer allow the general public to access these smarter models, but during the GPT4 preview phase we could get a glimpse into it.

The early GPT4 releases were noticeably sharper, had a better sense of humour, and could swear like a pirate if asked. There were comments by both third parties and OpenAI staff that as GPT4 was more and more "aligned" (made puritan), it got less intelligent and accurate. For example, the unaligned model would give uncertain answers in terms of percentages, and the aligned model would use less informative words like "likely" or "unlikely" instead. There was even a test of predictive accuracy, and it got worse as the model was fine tuned.
by rossdavidh | 2025-12-02 21:46:17+00:00
When my daughter wanted her first tablet, a friend of mine said he had an old one he could sell me for $50. Cool, I said, and my daughter saved up her allowance (paid for feeding the cats, as I recall) to get the money. Then, my friend said to just put the $50 in her savings account.

Oh, I said, right, we should set one of those up for her.

Once she had one, it was clear I should be adding to it every month. But, the initial nudge to get her one was important. Hopefully, having an account will make it more likely to put a little in every month? It's not everything, but it's also not nothing.

Of course, cheaper housing would help the kids more, but that has more entrenched interests opposed to it (almost every homeowner), and not even Dell has enough to overcome that.
by ojame | 2025-12-02 21:46:48+00:00
Currently Claude etc. can interact with services (including AWS) via MCPs.

What the user you're replying to is saying the Bun acquisition looks silly as a dev tool for Node. However if you look at their binding work for services like s3[0], the LLM will be able to interact directly with cloud services directly (lower latency, tighter integration, simplified deployment).

0: https://bun.com/docs/runtime/s3
by abnercoimbre | 2025-12-02 21:47:01+00:00
Handmade Cities founder here.

We never associated with Bun other than extending an invitation to rent a job booth at a conference: this was years ago when I had a Twitter account, so it's fair if Jarred doesn't remember.

If Handmade Cities had the opportunity to collaborate with Bun today, we would not take it, even prior to this acquisition. HMC wants to level up systems while remaining performant, snappy and buttery smooth. Notable examples include File Pilot [0] or my own Terminal Click (still early days) [1], both coming from bootstrapped indie devs.

I'll finish with a quote from a blog post [2]:

> Serious Handmade projects, like my own Terminal Click, don’t gain from AI. It does help at the margins: I’ve delegated website work since last year, and I enjoy seamless CI/CD for my builds. This is meaningful. However, it fails at novel problems and isn’t practical for my systems programming work.

All that said, I congratulate Bun even as we disagree on philosophy. I imagine it's no small feat getting acquired!

[0] https://filepilot.tech

[1] https://terminal.click

[2] https://handmadecities.com/news/summer-update-2025/
by crystal_revenge | 2025-12-02 21:47:14+00:00
Pandas is generally awful unless you're just living in a notebook (and even then it's probably least favorite implementation of the 'data frame' concept).

Since Pandas lacks Polars' concept of an Expression, it's actually quite challenging to programmatically interact with non-trivial Pandas queries. In Polars the query logic can be entirely independent of the data frame while still referencing specific columns of the data frame. This makes Polars data frames work much more naturally with typical programming abstractions.

Pandas multi-index is a bad idea in nearly all contexts other than it's original use case: financial time series (and I'll admit, if you're working with purely financial time series, then Pandas feels much better). Sufficiently large Pandas code bases are littered with seemingly arbitrary uses of 'reset_index', there are many times where multi-index will create bugs, and, most important, I've never seen any non-financial scenario where anyone has ever used Multi-index to their advantage.

Finally Pandas is slow, which is honestly the least priority for me personally, but using Polars is so refreshing.

What other data frames have you used? Having used R's native dataframes extensively (the way they make use of indexing is so much nicer) in addition to Polars both are drastically preferable to Pandas. My experience is that most people use Pandas because it has been the only data frame implementation in Python. But personally I'd rather just not use data frames if I'm forced to used Pandas. Could you expand on what you like about Pandas over other data frames models you've worked with?
by tehjoker | 2025-12-02 21:47:17+00:00
It's a fair question, but my view of America's influence on world affairs has been dismal. China by contrast has not had a history of invading its neighbors, though I strongly criticize their involvement in the American attack on Cambodia and Vietnam (China supported the Khmer Rouge and briefly invaded Vietnam but was quickly pushed back, a reason Mao is sometimes criticized as having a good early period and a bad late period).

Meanwhile, America has been causing death and destruction around the world. It's easy to make lists: Vietnam, Iraq, Gaza, Cuba, South and Central America etc etc.
by Aurornis | 2025-12-02 21:47:23+00:00
> I had a vague idea that "Zig people" were generally "Software You Can Love" or "Handmade Software Movement" types, about small programs, exquisitely hand-written, etc, etc.

In my experience, the extreme anti-LLM people and extreme pro-vibecoding people are a vocal online minority.

If you get away from the internet yelling match, the typical use case for LLMs is in the middle. Experienced developers use them for some small tasks and also write their own code. They know when to switch between modes and how to make the most of LLMs without deferring completely to their output.

Most of all: They don't go around yelling about their LLM use (or anti-use) because they're not interesting in the online LLM wars. They just want to build things with the tools available.
by satvikpendem | 2025-12-02 21:47:25+00:00
Let's kill this myth that people were lounging around before the Industrial Revolution. Serfs for example were working both their own land as well as their lord's land, as well as doing domestic duties in the middle. They really didn't have as much free time as we do today, plus their work was way more backbreaking, literally, than most's cushy sedentary office jobs.
by zwnow | 2025-12-02 21:47:39+00:00
Yea agree, the job market isn't difficult due to AI. Its difficult because big tech overhired massively for years, making it seem like there was demand for this many developers. Turns out if a field is somewhat saturated and the big tech corps lay off a lot of people the market gets difficult. Ill enjoy my current employment for as long as possible and hopefully the market will be better once I am looking for a job again.

I use my time off to learn a ton about Erlang/Elixir in the hopes of maybe entering the BEAM domain some day. Way less competition compared to Javascript Python devs.
by phantasmish | 2025-12-02 21:47:39+00:00
Funnily enough, as a parent, I'd have loved for my (young-ish at the time) kids to have been on old-school pencil and paper stuff during the pandemic.

Tear-out worksheet books or a weekly trip to the schools to grab a packet of physical papers with the week's lessons and work (or, hell, send the buses around to drop them off) would have been SO MUCH easier to manage and help with than all the online horse-shit.

Like, I truly think my 80s and 90s classrooms would have been better prepared to deal with the pandemic than the modern computerized ones. You'd think it'd be the other way around, but from what I saw, no. It's just so much harder to keep track of what's going on in several different computer programs, than a stack of paper and a couple books for each kid.
by dllu | 2025-12-02 21:47:52+00:00
A flash lidar is simply a 2D array of detectors plus a light source that's not imaged. It's mentioned super briefly at the start of section 3 but you're right, I should have gone into more detail given how common and important they are.

For pulsed emitters, indeed adding random jitter in the timing would avoid the problem of multiple lidars being synced up and firing at the same time. For some SPAD sensors, it's common to emit a train of multiple pulses to make a single measurement. Adding random jitter between them is a known and useful trick to mitigate interference. But in fact it isn't super accurate to say that interference is a problem for continuous-wave emitters either. Coherent FMCW lidar are typically quite robust against interference by, say, using randomized chirp patterns.