MurphyA/hacker-news huggingface dataset:
by psychoslave | 2025-07-18 07:12:10+00:00
What a awful way to think about internship.

The goal is to help people grow, so they can achieve things they would not have been able to deal with before gaining that additional experience. This might include boring dirty work, yes. But that means they thus prove they can overcome such a struggle, and so more experienced people should be expected to also be able to go though it - if there is no obvious more pleasant way to go.

What you say of interns regarding checks is just as true for any human out there, and the more power they are given, the more relevant it is to be vigilent, no matter their level of experience. Not only humans will make errors, but power games generally are very permeable to corruptible souls.
by aredox | 2025-07-18 07:12:12+00:00
I know we are used to people online having the wildest disagreement because of contrarianism or something, but in this case, there is a perfectly reasonable explanation:

You know we have three different types (sometimes four) of color sensors in our eyes? And that people who miss one or several of them are "colorblind" and fail tests like the Ishihara Test?

Well, human noses have hundreds of possible olfactory receptors, and everyone of us only has a subset of these, which means we are all "smellblind" one way or another.

https://www.ncbi.nlm.nih.gov/books/NBK10824/

Each one of us smells perfumes differently. The only think that we can agree on is a shared association or experience like the smell of a standard object (e.g. a road, asphalt) or of a common plant (flower or food).
by veeti | 2025-07-18 07:13:27+00:00
According to "corporate responsibility" and "sustainability" at Microsoft they are committed to "swift, collective action and technological innovation" for "carbon negativity" and "zero waste" by 2030 [1]. Evidently the Windows team did not get the memo, as the e-waste producing minimum processor & TPM requirements in Windows 11 have been shown to be entirely arbitrary. One of the easiest things MSFT could do for the environment could start here.

[1] https://www.microsoft.com/en-us/corporate-responsibility/sus...
by yorwba | 2025-07-18 07:13:30+00:00
> I used it myself in talk almost 3 years ago! And it isn't a lie exactly, the linked paper is totally sound.

The paper was published in December last year and addresses your concerns head-on. For example, from the introduction:

"if the network can learn this ideal score function exactly, then they will implement a perfect reversal of the forward process. This, in turn, will only be able to turn Gaussian noise into memorized training examples. Thus, any originality in the outputs of diffusion models must lie in their failure to achieve the very objective they are trained on: learning the ideal score function. But how can they fail in intelligent ways that lead to many sensible new examples far from the training set?"

Their answers to these questions are very good and also cover things like correcting the output of previous steps. But the proof is in the pudding: the outputs of their alternative procedure match the models they're explaining very well.

I encourage you to read it; maybe you'll even find a new way to decompose images into surface material properties and lighting as a result.
by jll29 | 2025-07-18 07:14:08+00:00
My answer has to be differentiated: I use a MacBook Air with M1 at work_1, and Lenovo ThinkPads X1 at work_2 and at home, both running Ubuntu LTS.

The Mac wins regarding battery life (but deltas are shrinking) and - important when on the move - connecting with various WiFi SSDs (this can be quite critical).

The Linux ThinkPads win regarding keyboard quality, and hackability (as UNIX/Linux person, I prefer Linux' directory organization to MacOS', which is a mix of BSD and non-standard /proprietary stuff). I like than on my Linux boxes, any command is just there, whereas on the MacBook, 60% of the time I need a command not from the top-10, it's not there and I need to brew install it first, which sucks (this could be fixed by making a "distribution" of common commands for brew, I haven't even checked whether that exists).

Until recently, the Mac also won regarding weight, but now with the fantastic ThinkPad X1 Nano there's a high-quality high-mobility device with a great internal keyboard, good batteries and the weight of a feather that runs Ubuntu like a breeze.

So in the end, one ends up using the MacBook as an email/presentation machine and the Linux boxes (and, via ssh, servers of course) for technical work.

Ironically, the M1 in my MacBook doesn't get used for the machine learning research I do as that is all done on beefy (Linux) servers and/or GPU clusters. But it does improve the UI responsiveness.

PS: From my budget at work, I also got an iPad Pro (the lightest/smallest), and I was shocked how heavy it is. As a result, it hardly gets used apart from taking photos and scanning documents with its excellent camera. I was hoping to carry it to meetings, but I instead take the MacBook Air or X1 Nano along, both of which seem much lighter, esp. the latter (<970g). (I never use pens because I type faster than I hand-write and prefer my text to stay searchable; I understand results may look different for pen fans.)
by mapasj | 2025-07-18 07:14:11+00:00
With this discussion about perfumes, seems like an appropriate place to offer a PSA about phthalates. Perfumes often contain phthalates, which are endocrine disruptors. Exposure can be very harmful to a developing fetus and young children. It can create problems for adults too.

Phthalates are pervasive too in soaps and shampoos, where it's hidden within the ambiguous "fragrance" item.

There are phthalate-free options for soap and shampoo, and a quick google search indicates the perfume industry is starting to offer more products without phthalates. Phthalates aren't the only nasty chemical used for scents. Parabens are another, and maybe more I'm unaware of.
by pjmlp | 2025-07-18 07:14:27+00:00
Once upon a time, I installed a quite usable version of Ubuntu, nicely configured on my parents computer.

Exactly because "vast majority of people, even the simplest desktop Linux distributions are simply too high a learning curve to be useful outside the very basics.", the first time they got into issues instead of doing the "call the son IT support", they went to the nearest PC store and got Windows reinstalled.

I was suddenly surprised during the next parents visit to find Windows again on that computer.

Since then, I rather have the local PC store support them, than me, and then also don't really care about GNU/Linux based systems, only whatever runs OSes from Microsoft, Apple and Google.

It isn't as if I am flying back to Portugal every weekend to do support, or find out how to make GNU/Linux actually fit their computer needs, and software needed by Portuguese goverment, banks and co.
by imiric | 2025-07-18 07:14:28+00:00
I jumped the gun a bit in my comment, since you did mention you want to see this from both sides. So it was clear, and I apologize.

The thing is that I often read this kind of response only to comments with negative experiences, while positive ones are accepted as fact. You can see this reinforced in the comments here as well. A comment section is not the right place to expand on these details, but I agree that blog posts should have them, regardless of the experience type.
by spauldo | 2025-07-18 07:14:48+00:00
I suspect this is very subjective. I had a Radio Shack 200-in-1 kit when I was a kid and learned tons from it, working through the book and then designing my own projects.

My current project just ties in my doorbell to Home Assistant. I could have built it only using what came with my old Raspberry Pi kit. Once I finish that, I'll be tying in my garage door, another project that uses simple parts. These projects are way simpler than what I do for work (currently writing PLC code for the Navy), but they're still fun.
by oblio | 2025-07-18 07:15:38+00:00
> The ones that praised it tended to be people who were taught or otherwise figured out how to use metadata tags like date:/onsite: , whereas the ones that disparaged it tended to be the folks who would search for things like "who won the game" and then proceed to click every scam/porno link on this green Earth and then blame Google/gdg/lycos/whatever when they were exposed to whatever they clicked.

One big warning here: search engines only became really useful when you could search for "who won the game" and the search engine actually returned the correct thing as the top result.

We're more than a quarter of a century later and probably 99.99% of users don't know about Google's advanced search operators.

This should be a major warning for LLMs. People are people and will do people things.
by TFYS | 2025-07-18 07:18:39+00:00
Most of the money circulating in the economy has been taxed many many times. The money your employer pays you has been taxed, the money that employer got from its customers was taxed, the money those customers used was their salary, which was taxed. That is such a stupid argument to make. It would mean that we should only have one tax at the root of money itself.
by oblio | 2025-07-18 07:18:53+00:00
Speaking of that observation about "fringe": this will probably, increasingly, be a factor, let's call it LLMO (optimization), where "LLM friendly" content will be pushed. So I expect secondary or fringe programming languages to become even more pushed aside, since LLMs will not be as useful.

Which is, obviously, sad. Especially since the big winner is Javascript, a language that's still subpar as far as programming languages go.
by trilogic | 2025-07-18 07:20:34+00:00
I just hope Mistral find a really cold place (in the alps) for their servers so the GPU will not melt, cause I can foresee 500 million users in the next 2 years. Coding plans of 20/30 euro/month will attract users worldwide and raise the progress in competitivity/innovation of EU, as nr 1 in the world. Now time to invest not to be greedy. And step up the Dutch for the chip production.
by ElFitz | 2025-07-18 07:21:09+00:00
> Now it looks to me that the whole input must be encrypted with key k. But in the search example, the inputs include a query […] and a multi-terabyte database […]

That’s not the understanding I got from Apple’s CallerID example[0][1]. They don’t seem to be making an encrypted copy of their entire database for each user.

[0]: https://machinelearning.apple.com/research/homomorphic-encry...

[1]: https://machinelearning.apple.com/research/wally-search
by akorotkov | 2025-07-18 07:21:47+00:00
Hey HN, I'm the creator of OrioleDB, an extension for PostgreSQL that serves as a drop-in replacement for the default Heap storage engine. It is designed to address scalability bottlenecks in PostgreSQL's buffer manager and reduce the WAL, enabling better utilization of modern multi-core CPUs and high‑performance storage systems. We are getting closer to GA.

This blog post is dedicated to describing a new OrioleDB optimization: fastpath search, an inter-page search that bypasses page copying and tuple deformation for certain fixed-length datatypes.

We would love for more people to test and benchmark OrioleDB. You can check how fastpath search would help your workload. The fastest way to do that is to use the Docker image provided:

docker run -d --name orioledb -p 5432:5432 orioledb/orioledb
by rwmj | 2025-07-18 07:21:51+00:00
I spent a bit of time reviewing and cleaning up the mess of someone who had taken text that I'd carefully written, put it through an AI to make it more "impactful", and revising it to remove the confusions and things I hadn't said. The text the AI wrote was certainly impactful, but it was also exaggerated and wrong in several places.

So did AI add value here? It seems to me that it wasted a bunch of my time.
by hansvm | 2025-07-18 07:22:23+00:00
I'll pick a few concrete tasks: Building a substantially faster protobuf parser, building a differentiable database, and building a protobuf pre-compression library. So far, AI's abilities have been:

1. Piss-poor at the brainstorming and planning phase. For the compression thing I got one halfway decent idea, and it's one I already planned on using.

2. Even worse at generating a usable project structure or high-level API/skeleton. The code is unusable because it's not just subtly wrong; it doesn't match any cohesive mental model, meaning the first step is building that model and then figuring out how to ram-rod that solution into your model.

3. Really not great at generating APIs/skeletons matching your mental model. The context is too large, and performance drops.

4. Terrible at filling in the details for any particular method. It'll have subtle mistakes like handling carryover data at the end of a loop, but handling it always instead of just when it hasn't already been handled. Everything type checks, and if it doesn't then I can't rely on the AI to give a correct result instead of the easiest way to silence the compiler.

5. Very bad at incorporating invariants (lifetimes, allocation patterns, etc) into its code when I ask it to make even minor tweaks, even when explicitly promoted to consider such-and-such edge case.

6. Blatantly wrong when suggesting code improvements, usually breaking things, and in a way you can't easily paper over the issue to create something working "from" the AI code.

Etc. It just wasn't well suited to any of those tasks. On my end, the real work is deeply understanding the problem, deriving the only possible conclusions, banging that into code, and then doing a pass or three cleaning up the semicolon orgasm from the page. AI is sometimes helpful in that last phase, but I'm certain it's not useful for the rest yet.

My current view is that the difference in viewpoints stems from a combination of the tasks being completed (certain boilerplate automation crap I've definitely leaned into AI to handle, maybe that's all some devs work on?) and current skill progression (I've interviewed enough people to know that the work I'm describing as trivial doesn't come naturally to everyone yet, so it's tempting to say that it's you holding your compiler wrong rather than me holding the AI wrong).

Am I wrong? Should AI be able to help with those things? Is it more than a ~5% boost?
by paulv | 2025-07-18 07:22:37+00:00
My experience as a long time Linux user (since 1997, so admittedly stuck with some bad habits from when things were actually hard to get working) has been that things are kind of confusing if you deviate from the golden path, but if you are on the golden path you won't ever notice that it is turned on.

The laptops I have gotten from eg Dell with Linux pre installed have just worked. Machines I have upgraded through many versions of Ubuntu (lts versions of 16-24) were weirdly broken for a while when I first turned secure boot on while I figured it out, but that seemed reasonable for such a pathological case. Machines I have installed Debian on in the last few years have been fine, except for some problems when I was booting from a software raid array, but that is because I was using 2 identical drives and I kept getting them confused in the UEFI boot configuration.

I have not used them on machines with nvidia, vbox, or other out-of kernel-tree modules though.
by estsauver | 2025-07-18 07:22:47+00:00
I came from a Chemical Engineering and Math degree, but ended up teaching myself programming while I was in university. I saw my freshman year roommate getting offered more for an internship than chemical engineers were making after they graduated and put 2 and 2 together.

I think there is a huge benefit to having had some sort of extremely rigorous quantitative background. When I talk with people who are self taught who haven't come from a hard (where that is an extremely hand wavey concept) can get started in engineering and often can be productive, but really struggle when things break or get difficult. There's a sharpening of the mind that comes with extreme analytic rigour, and I think that's more important than whether you get it in a real analysis class or in a CS class.
by mkj | 2025-07-18 07:23:09+00:00
It's not just Linux - certificates to sign Windows are also affected in 2026.

https://support.microsoft.com/en-us/topic/windows-secure-boo...

https://techcommunity.microsoft.com/blog/windows-itpro-blog/...

Really it seems like having any expiry date for these certificates is a mistake. The one thing it might protect against is a compromised signing key, but if you have to wait 15 years for a compromised key to stop being valid, it's not very useful!

Don't worry, the replacement MS certs expire in 2038 (a couple of months after the 32-bit unix time rollover).
by cm2187 | 2025-07-18 07:24:58+00:00
The paradox is that the better insurance companies are at pricing risk, the more irrelevant their business becomes. If they can predict your disease with 100% reliability, and charge you based on that, then what is the point of buying insurance if you are going to be charged either nothing or the full cost of the treatment, just like if you were not insured.

Except of course in the US, with scammy hospitals charging over-inflated prices to uninsured patients, while only insurers can access realistic prices.
by ivraatiems | 2025-07-18 07:25:29+00:00
Sure, here are a list of problems that people have come to me to help them solve on Linux machines:

1) Speakers are inexplicably swapped between left and right and you have to debug alsa/pulseaudio/pipewire to find out why

2) Bluetooth and WiFi don't work without drivers that require downloading and installing packages manually from different versions of the OS (eg the Ubuntu 22.04 drivers don't work but 21.04 do and that's not documented anywhere, I figured it out by reading logs and googling)

3) some devices or drivers don't work without enabling new kernel modules

4) some devices or drivers don't work without building a driver from source, the official binaries are not usable on a given system

5) sleep doesn't work correctly without manual configuration on command line

6) /etc/default/keyboard must be manually modified with the correct keyboard configuration for the device; the UI can't do it

If you think the above are problems a layperson with no computing (and specifically Linux) expertise can solve, you may not be spending a lot of time around actual laypeople.
by stefanka | 2025-07-18 07:26:26+00:00
Depends entirely on the application I guess, but you'd get further for sure. Either way you are now confronted with more complex cable management and maintenance, and additional weight. You'd at least get around gravity compensation with that approach. Personally, I think you need a lot more sensors and complex sensor fusion to achieve acceptable dexterity to be useful outside of an industrial context and this comes at high costs
by jll29 | 2025-07-18 07:26:41+00:00
AI won't make you stupid per se, but if you become dependent on it, if it becomes your go-to place to delegate anythin to before you use your own brains, then this is such a comfortable position that you will likely stay there, and that may erode both your actual abilities as well as your own self-confidence in them.

Keeping your own brain trained (just by using it) is one thing, but there's another angle to it: because LLMs sound so self-confident, people tend not to question the responses' correctness, which are often random garbage (there's an actual random number generator involved in creating the answer, e.g. check the parameter "temperature" in ChatGPT's docs).

We need to stay in control, and that requires being able to check if a system's answer can even be true.

Example: I know people who type 34+23 into ChatGPT out of laziness. Keep doing that for two years, and you won't even try anything else, it will have become a habit. I'm bad at mental arithmetic, but even I can add this up to 57 faster than anyone can type it into ChatGPT, which costs enormous energy to respond to that query which is more suitable to a pocket calculator (Linux/UNIX: bc -l is your friend).
by ToJans | 2025-07-18 07:28:53+00:00
It depends... I've worked with hundreds of juniors & seniors during my consulting days.

I've had ups and downs in this situation, but on most cases it's about showing the light to a path forward.

In most cases, the software development was straightforward, and most of the coaching was about a how to behave in the organisation they were functioning in.

One can only have so many architecture/code quality reviews, typically we evacuated the seniority of the devs on their ability to cope with people (colleagues, bosses, clients, ...)

We did have a few very bright technical people as well, but those were about 10 on a 2000-person company.

The reason I explicitly mentioned the slightly autistic junior person, is because I've worked with one, who was about to be fired, because other people had issues dealing with him.

So I moved desks, sat next to him for over a month, and he ended up becoming the champion for one of the projects we were doing, because he was very bright, precise and had a huge memory, which mattered a lot in that context.

Other stories are similar, once they were about to throw out a colleague because he was taking days to do something that should have taken a few hours max. So I say next to him, to see what he was doing.

Turned out he was refactoring all the code his feature touched because he couldn't stand bad code. So we moved him to quality control, and last time I checked he was thriving...

I guess what I'm saying is that -just like with people -, you need to find a good modus operandi, and have matching expectations, but if you can figure it out, it will pay off dividends.
by rcarmo | 2025-07-18 07:28:56+00:00
I have been using both for a number of years. n8n has had a resurgence recently because of its (quite clever) AI integrations (and it has higher level abstractions for doing workflows), but since I want more control I actually do my stuff (including an AÍ news summarizer and a few “agents”) in Node-RED because once you’ve sorted out your own library for things like API calls, memory, storage, etc. (which I did using just sub flows and custom JS inside them) it can be a lot simpler and more straightforward.

I see n8n as more of “citizen developer” tool (less flexibility, more built-in integrations) and Node-RED as an engineering scratchpad where you can literally build anything you want (for instance, it’s been part of my home automation setup for ages, way before Home Assistant became a thing).

I wouldn’t run _any_ of them as part of an enterprise solution, though. Security, IAM/RBAC, auditability and even general reliability would keep me up at night (even considering that Node-RED runs a surprising amount of small scale on-premises industrial automation, that’s typically in isolated networks, so I’m not factoring those in).
by fauigerzigerk | 2025-07-18 07:29:37+00:00
I used to donate to Wikipedia, but it has been completely overrun by activists pushing their preferred narrative. I don't trust it any more.

I guess it had to happen at some point. If a site is used as ground truth by everyone while being open to contributions, it has to become a magnet and a battleground for groups trying to influence other people.

LLMs don't fix that of course. But at least they are not as much a single point of failure as a specific site can be.