MurphyA/hacker-news huggingface dataset:
by freedomben | 2026-01-22 16:28:21+00:00
I was originally thinking you were being way too harsh with your "punish criminally" take, but I must admit, you're winning me over. I think we would need to be careful to ensure we never (or realistically, very rarely) convict an innocent person, but this is in many cases outright theft/fraud when someone is making money or being "compensated" for producing work that is fraudulent.

For people who think this is too harsh, just remember we aren't talking about undergrads who cheat on a course paper here. We're talking about people who were given money (often from taxpayers) that committed fraud. This is textbook white collar crime, not some kid being lazy. At a minimum we should be taking all that money back from them and barring them from ever receiving grant money again. In some cases I think fines exceeding the money they received would be appropriate.
by StableAlkyne | 2026-01-22 16:28:31+00:00
Those undergrads still need to be advised and they use lab resources.

If you're a tenure-track academic, your livelihood is much safer from having them try new ideas (that you will be the corresponding author on, increasing your prestige and ability to procure funding) instead of incrementing.

And if you already have tenure, maybe you have the undergrad do just that. But the tenure process heavily filters for ambitious researchers, so it's unlikely this would be a priority.

If instead you did it as coursework, you could get them to maybe reproduce the work, but if you only have the students for a semester, that's not enough time to write up the paper and make it through peer review (which can take months between iterations)
by tialaramex | 2026-01-22 16:29:42+00:00
Unlike the UK (which mothballed and eventually tore down its coal power stations) there is still a whole bunch of coal power online in Canada.

Lingan Generating Station would be a typical example. Big thermal power station, built to burn local coal, realistically the transition for them is to non-coal thermal power, burning LNG or Oil, or trees or whatever else can be set on fire. If they burned trash (which isn't really a practical conversion, but it's a hypothetical) we could argue that's renewable because it's not like there won't be trash, but otherwise this is just never going to be a renewable power source.

Canada is a huge place, so I don't doubt that none those coal stations are near you (unless, I suppose, you literally live next to Lingan or a similar plant but just aren't very observant) but most of us aren't self-sufficient and so we do need to pay attention to the consequences far from us.
by genewitch | 2026-01-22 16:29:44+00:00
it isn't often that tehcnology gives me chills, but this did it. I've used "AI" TTS tools since 2018 or so, and i thought the stuff from two years ago was about the best we were going to get. I don't know the size of these, i scrolled to the samples. I am going to get the models set up somewhere and test them out.

Now, maybe the results were cherrypicked. i know everyone else who has released one of these cherrypicks which to publish. However, this is the first time i've considered it plausible to use AI TTS to remaster old radioplays and the like, where a section of audio is unintelligible but can be deduced from context, like a tape glitch where someone says "HEY [...]LAR!" and it's an episode of Yours Truly, Johnny Dollar...

I have dozens of hours of audio of like Bob Bailey and people of that era.
by oliverjanssen | 2026-01-22 16:30:21+00:00
Congrats on the new dad status! Enjoy the early years – the Toniebox phase is actually pretty great. Muky will be waiting when they start asking for "that song from the movie" on repeat. ;)

And yes – big tech seems to avoid this space. Maybe too niche, maybe too much liability with kids content. Works for me!
by infinet | 2026-01-22 16:30:47+00:00
Very interesting!

For completeness, the job in the article is not strictly parallel, instead, they are separable tasks, which fall under high-throughput computing. HTCondor is a popular choice and is widely used in physics. If the cluster has a shared filesystem such as NFS, it does not even need ssh to each node. Moreover, nodes can share the same R installation on NFS, remove the complexity of installing same set of R packages on all nodes. HTCondor is available on Ubuntu.
by FeistySkink | 2026-01-22 16:31:07+00:00
It would help if PRs from newly-created or private accounts could stand out. And perhaps PRs from accounts that spam multiple PRs with dozes or hundreds of commits, would have some kind of a warning that only people with write access to repos can see. In fact perhaps GitHub could throttle those accounts from creating that many PRs in the first place.

Another suggestion would be trying to figure out if a PR was vibe-coded and marking them as such. Sames as image-based social media tries to do.
by pixl97 | 2026-01-22 16:31:15+00:00
ESR is also from a time where spamming countless reports/junk code wasn't really a concept.

They did have things like trolls and zealots that thought "Their one idea" was a gift from god and the maintainers were idiots for not adding it to the application. And eventually those people may have been banned from mailing lists. But in general the people posting code were typically well known and had some interest in fixing the application for some useful purpose.

Simply put, no idealism stands the test of time without change. Nature shows us that everything must evolve or it goes extinct. How 'free software' evolves is now up for debate.
by toomuchtodo | 2026-01-22 16:31:16+00:00
Latest Data Shows the Rapid Growth of Heat Pumps in Canada - https://www.theenergymix.com/latest-data-shows-the-rapid-gro... - November 5th, 2025

(still good news, as most of Canada's electric generation is low carbon hydro, and the rest of fossil generation can be pushed out with storage and renewables, although I do not have a link handy by province how much fossil generation needs to be pushed out)
by nerdjon | 2026-01-22 16:31:19+00:00
The downstream effects of this are extremely concerning. We have already seen the damage caused by human written research that was later retracted like the “research” on vaccines causing autism.

As we get more and more papers that may be citing information that was originally hallucinated in the first place we have a major reliability issue here. What is worse is people that did not use AI in the first place will be caught in the crosshairs since they will be referencing incorrect information.

There needs to be a serious amount of education done on what these tools can and cannot do and importantly where they fail. Too many people see these tools as magic since that is what the big companies are pushing them as.

Other than that we need to put in actual repercussions for publishing work created by an LLM without validating it (or just say you can’t in the first place but I guess that ship has sailed) or it will just keep happening. We can’t just ignore it and hope it won’t be a problem.

And yes, humans can make mistakes too. The difference is accountability and the ability to actually be unsure about something so you question yourself to validate.
by rajaravivarma_r | 2026-01-22 16:31:33+00:00
I have been doing this for at least a decade now and it is a great pattern, but think of an ETL pipeline where you fetch a huge JSON payload, store it in the database and then transform it and load it in another model. I had an use case where I wanted to process the JSON payload and pass it down the pipeline before storing it in the useful model. I didn't want to store the intermediate JSON anywhere. I benchmarked it for this specific use case.
by Fiveplus | 2026-01-22 16:31:35+00:00
>It is possible to use the language server for syntax highlighting. I am not aware of any particularly strong reasons why one would want to (or not want to) do this.

Hmm, the strong reason could be latency and layout stability. Tree-sitter parses on the main thread (or a close worker) typically in sub-ms timeframes, ensuring that syntax coloring is synchronous with keystrokes. LSP semantic tokens are asynchronous by design. If you rely solely on LSP for highlighting, you introduce a flash of unstyled content or color-shifting artifacts every time you type, because the round-trip to the server (even a local one) and the subsequent re-tokenization takes longer than the frame budget.

The ideal hygiene could be something like -> tree-sitter provides the high-speed lexical coloring (keywords, punctuation, basic structure) instantly and LSP paints the semantic modifiers (interfaces vs classes, mutable vs const) asynchronously like 200ms later. Relying on LSP for the base layer makes the editor feel sluggish.
Show HN: Text-to-video model from scratch (2 brothers, 2 years, 2B params)
by schopra909 | 2026-01-22 16:31:47+00:00
Writeup (includes good/bad sample generations): https://www.linum.ai/field-notes/launch-linum-v2

-------

We're Sahil and Manu, two brothers who spent the last 2 years training text-to-video models from scratch. Today we're releasing them under Apache 2.0.

These are 2B param models capable of generating 2-5 seconds of footage at either 360p or 720p. In terms of model size, the closest comparison is Alibaba's Wan 2.1 1.3B. From our testing, we get significantly better motion capture and aesthetics.

We're not claiming to have reached the frontier. For us, this is a stepping stone towards SOTA - proof we can train these models end-to-end ourselves.

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

Why train a model from scratch?

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

We shipped our first model in January 2024 (pre-Sora) as a 180p, 1-second GIF bot, bootstrapped off Stable Diffusion XL. Image VAEs don't understand temporal coherence, and without the original training data, you can't smoothly transition between image and video distributions. At some point you're better off starting over.

For v2, we use T5 for text encoding, Wan 2.1 VAE for compression, and a DiT-variant backbone trained with flow matching. We built our own temporal VAE but Wan's was smaller with equivalent performance, so we used it to save on embedding costs. (We'll open-source our VAE shortly.)

The bulk of development time went into building curation pipelines that actually work (e.g., hand-labeling aesthetic properties and fine-tuning VLMs to filter at scale).

What works:
Cartoon/animated styles, food and nature scenes, simple character motion

What doesn't:
Complex physics, fast motion (e.g., gymnastics, dancing), consistent text

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

Why build this when Veo/Sora exist?

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

Products are extensions of the underlying model's capabilities. If users want a feature the model doesn't support (character consistency, camera controls, editing, style mapping, etc.), you're stuck.

To build the product we want, we need to update the model itself. That means owning the development process.

It's a bet that will take time (and a lot of GPU compute) to pay off, but we think it's the right one.

What’s next?
- Post-training for physics/deformations
- Distillation for speed
- Audio capabilities
- Model scaling

Happy to answer questions about building a model from 0 → 1. We kept a “lab notebook” of all our experiments in Notion and we'll be blogging about our learnings throughout the year.
by treesknees | 2026-01-22 16:31:59+00:00
ReactOS requires all contributors to affirm that legally they have not used or seen any leaked Windows source code. This is to avoid any hints of copyright violation. While AI may be capable of writing a new driver or fixing bugs, a developer using AI can’t affirm that the model hasn’t seen/trained on any leaked source code. So AI submissions would very likely be denied.
by FjordWarden | 2026-01-22 16:32:02+00:00
You have to figure this out for yourself in most cases.
Tree sitter does have a query language based on s-expressions, but it is more for questions like "give me all the nodes that are literals", and then you can, for example, render those with in single draw call. Tree sitter has incremental parsing, and queries can be fixed at a certain byte range.
by jccalhoun | 2026-01-22 16:32:40+00:00
A more recent show to compare would be the UK vs the USA version of Ghosts. I like both shows but it is interesting how in the USA version all the main Ghosts are basically good people while the UK Ghosts have more serious flaws. And in the UK version, money is a constant problem while in the USA version it isn't nearly as big of a problem.
by altern8 | 2026-01-22 16:33:11+00:00
True, there is no oil and we just relied on cheap gas from Russia--which I guess it didn't turn out to be a good strategy after all.

That's interesting about oil companies. Is that who's lobbing to pass laws that just seem (to me) to be written on purpose to make our companies less competitive? How does that work, how do oil companies profit from that?
Ask HN: Anyone doing production image editing with Image models? How?
by geooff_ | 2026-01-22 16:33:26+00:00
Hey HN — I’m building an app where users upload “real life” clothing photos (ex. a wrinkly shirt folded on the floor). The goal is to transform that single photo into a clean, ecommerce-style image of the garment.

One key UX requirement: the output needs to be a PNG with transparency (alpha) so we can consistently crop/composite the garment into an on-rails UI (cards, outfit layouts, etc.). Think “subject cutout that drops cleanly into templates.”

My current pipeline looks like:
1. User-uploaded photo (messy background, weird angles)
2. User-upload is matched to “query” image (style target) + promptCurrently using Nano Banana)
4. Background removal model to get transparency and save as RGBA PNG

This works, but it feels hacky + occasionally introduces edge artifacts. Also, the generation model sometimes invents shadows/background cues that confuse the background removal step. It feels like the two steps are fighting one another.

I’m trying to understand what “good” looks like in production for this kind of workflow:

Are people still doing gen/edit → separate background removal as the standard?

Are any of you using alpha-native generation (RGBA outputs) in production? If so, what’s the stack/workflow?

If you’ve done “messy UGC photo → catalog asset” specifically: what broke most often and what fixed it?

I’m not looking for vendor pitches—mostly practical patterns people are using (open source workflows, model classes, ComfyUI/SD pipelines, API-based stacks, etc.). Happy to share more details if helpful.
by thaumasiotes | 2026-01-22 16:33:32+00:00
> Those references are to the recurring gag with Lucy and the football.

Probably, because that's the most popular example of Charlie Brown as a sucker who never learns, but it's a core part of his character and is shown by many, many other gags. There's also a recurring gag with Lucy and April Fool's Day. There's a whole family of them around baseball and Peppermint Patty. There's another recurring gag where he tries to fly a kite.

The comic can't depict Charlie Brown as able to learn - since he never succeeds,† if he could learn, he'd never do anything at all.

† There are a couple of temporary exceptions. When he runs away from home he meets a gang of littler children who respect him. When he has to wear a paper bag over his head at camp, he becomes a success for the duration.
by antonvs | 2026-01-22 16:33:36+00:00
Old English is not considered to have evolved from a Nordic language.

Old Norse was a North Germanic language. Old English was a West Germanic language. They were mutually intelligible to a significant degree, since both were Germanic languages that evolved from the hypothetical common ancestor Proto-Germanic, but saying Old English was "90% a Nordic language" is like saying that humans are 90% chimpanzees. Both evolved from a common ancestor, one did not evolve from the other.
by wiseowise | 2026-01-22 16:33:43+00:00
Talk about arrogance, Mr 45 years of experience. Ever thought that there might be people under skyscraper that is your ego? I’m pretty sure majority of tech workers aren’t even 45 years old. Where are they supposed to learn good design when slop takes over? You’ve spent at least 20 years JUST programming, assuming you’ve never touched large scale design before last 25 years. Simplest part my ass.
by ryandrake | 2026-01-22 16:33:43+00:00
There is no limit to how low someone will stoop to get even a tiny token for free. I remember a local community fun event from a couple of years ago, which was set up by the library to encourage kids to read. They would count up these reading tokens at the end of it and give some tiny $2 teddy bear to the winners, and of course a bunch of adults swooped in, gamed the system, and all the toys went to them. People are totally shameless assholes when even an insignificant free prize is on the line.
by bethekidyouwant | 2026-01-22 16:33:44+00:00
Ja, storm tossed survivor /accidental stoic who do we have .. Bilbo, yossarian, Rincewind.. Bit of a tossup innit?

From memory so long is a bit of a departure from the rest of the series and felt like adams was giving dent a vacation/term of being the deliberate stoic
by spijdar | 2026-01-22 16:35:08+00:00
> So Unix has to feel like dealing with old cruft to you? ;)

Well, maybe :)

It's something about the system being made of a lot of messy parts which can be split apart and taped back together. Reductively, all computers are like this, but SunOS and other "unixes" are more easily put back together.

For instance, besides enabling DNS, I've extended the libc quite a bit, to get modern OpenSSL and curl to build, as well as KDE 1 just for kicks.

You can do the same with almost any OS (that doesn't lock you out with security), but it feels easier with a "Unix". Linux is also very like this!

> I have a Sparc in my collection but it's running Solaris and too new to run SunOS 4.

You could always run NetBSD and use COMPAT_SUNOS to run a SunOS chroot ;) I haven't tried running Xsun this way but it'd probably go...
by ricardo81 | 2026-01-22 16:36:10+00:00
It has been a long standing problem.

Ideas crop up like generating hydrogen with the curtailed energy or maybe at least in Winter, use it for heat generation. The problem would seem to be the capex and the inverse of intermittency being the problem for them in utilising that energy, i.e. waiting for curtailment.

At least with available hydro you can pump water back up hill using a reliable and cheap tech.
by smsm42 | 2026-01-22 16:36:28+00:00
That would shut down all drive-by contributions, which may not be a good source of big improvements, but are a good source of information about bugs/maintenance issues. I.e. if I find a rare corner case where the code breaks, for an open source project I'd usually take some time to report it properly. But there's no way I'd pay for the privilege or bother to register with my governmental ID.
by Aurornis | 2026-01-22 16:36:35+00:00
> It's perfectly fine to not trust the folder, you'll just enter restricted mode that will protect you and certain things will be degraded like language servers may not run, you don't be able to debug (executes code in vscode/launch.json), etc.

This is the main problem with that dialog: It’s completely unclear to me, as a user, what will and will not happen if I trust a workspace.

I treat the selection as meaning that I’m going to have nothing more than a basic text editor if I don’t trust the workspace. That’s fine for some situations, but eventually I want to do something with the code. Then my only options are to trust everything and open the possibility of something (?) bad happening, or not do any work at all. There’s no visibility into what’s happening, no indication of what might happen, just a vague warning that I’m now on my own with no guardrails or visibility. Good luck.
Show HN: Kubecfg – A CLI to manage Kubernetes contexts and namespaces
by kadirbelkuyu | 2026-01-22 16:36:54+00:00
Creator here. If you manage multiple K8s clusters, you know the pain:
kubectl config use-context staging-eu-west-1-prod-eks
kubectl config set-context --current --namespace=monitoring
I built kubecfg to fix this:
kubecfg add cluster.yaml → import a kubeconfig file
kubecfg add cluster.yaml --name prod → import with custom context name
kubecfg use → pick context interactively
kubecfg ns → switch namespace
kubecfg merge → combine kubeconfig files
Written in Go. Happy to answer questions!
by mekdoonggi | 2026-01-22 16:36:59+00:00
A quick search says the UK produced 18,314 GWh of solar last year. And this was mostly funded by private investment? It seems like for some infrastructure investment, the government is getting long-term renewable power. If the solar isn't making money, why is it growing 30% annually?

What is stupid about nuclear? It's a huge amount of clean, secure energy.

Would your preference be dependence on Russian/US oil natural gas? Would you feel the same if Russia invaded Finland/Baltics and US took over Greenland?
by harry34 | 2026-01-22 16:37:06+00:00
The tension between 'learning fundamentals' and 'shipping products' has always existed in software development, but AI coding assistants make it more acute.

What's interesting is that the bottleneck is shifting. For experienced developers, the constraint was never typing speed or recalling syntax - it was understanding the problem domain, making architectural decisions, and maintaining systems over time. AI tools amplify this: they make the gap between 'can generate code' and 'can build maintainable systems' even wider.

The real question isn't whether to use AI tools, but how they change what's worth learning deeply. If AI can scaffold boilerplate, then understanding why certain patterns exist becomes more valuable, not less. The ability to evaluate AI-generated code, spot subtle bugs, or recognize when it's taking you down a bad architectural path - these skills require deep knowledge.

For solopreneurs and builders, the calculation is different. Getting something working that creates value for users is often better than perfect code that ships too late. The key is being honest about the tradeoffs: move fast with AI, but budget time to understand what you've built before scaling it.
by vidarh | 2026-01-22 16:37:19+00:00
The entire point is we increasingly don't need to be authors.

Most of us aren't paid to be authors in your analogy.

(Which is good, because outside of your analogy, most authors are paid peanuts, and most of those of us who do write do so because we enjoy it, not as a job)

But even if our jobs were to be authors, while I learned some things about writing books from writing the novels I have written and published, I learned far more from being a voracious reader for decades.

I probably needed both, and I'm sure I'd improve as a writer past what I could from just reading by writing more, I think your analogy if anything is a perfect fit for my point that we don't need to spend more than a tiny proportion our time writing to be competent at it (I won't claim great).

Many of us will probably keep doing it for fun, but it will be increasingly hard to justify "manual coding" at work.
by beej71 | 2026-01-22 16:37:26+00:00
The thing is, right now we have very little evidence that there is any significant mail-in voting fraud.

But we do have a fair amount of evidence that there is suppression of in-person voting.

So neither of these systems is perfect, but we should go with the one that gives us the most accurate legitimate vote.

Someone else posted a list of ways that in-person voting would be more acceptable, e.g. having a large window to cast ballots. But instead, we see move the other way, trying to restrict the window in which we can cast ballots.

You put a free ID in the hands of every legitimate voter and give them enough time and opportunity to vote, and then I will consider in-person to be on par with mail-in.