MurphyA/hacker-news huggingface dataset:
by a-breidenthal | 2026-01-05 08:00:43+00:00
Early in 2025, I began an experiment: Could the tools of modern digital transparency (GitHub, Version Control, Plain Text) be used to explore the mechanisms of historical silence?
Friction is a trilogy of "archival fictions." These aren't standard novels; they are constructed as recovered materials—transcripts, internal memoranda, and redacted journals—presented in a format that mirrors the scholarly and documentary forms they interrogate.
The three works explore different ways history is "edited":
Operation Seamless: A fictional Victorian archive detailing a systematic effort to erase a 1888 incident from the City of London Police records.
Victoria Unpublished: A study of the "editorial shaping" of Queen Victoria’s journals—exploring how a public voice is engineered through omission.
The Scalpel Slipped: A narrative of the 1914 collapse, framed around the diplomatic precision of withholding intelligence to trigger a "necessary" catastrophe.

Why GitHub?
Recorded history is often treated as a static fact, but it is a curated output. By hosting these on GitHub, I wanted the "version control" of the site to act as a metaphor for the way history is edited, positioned, and delayed. The use of Markdown allows the stories to be lightweight, future-proof, and accessible as plain text—prioritizing content over "book-like" aesthetics.

The License:
I’ve dedicated the entire repository to the Public Domain (CC0 1.0). I believe stories that examine the "ownership" of history should belong to everyone.

I’d love to hear your thoughts on this "Git-based" approach to storytelling, or any discussions on the ethics of the archive.
Inducing self-NSFW classification in image models to prevent deepfakes edits
by Genesis_rish | 2026-01-05 08:02:01+00:00
Hey guys,
I was playing around with adversarial perturbations on image generation to see how much distortion it actually takes to stop models from generating or to push them off-target. That mostly went nowhere, which wasn’t surprising.

Then I tried something a bit weirder: instead of fighting the model, I tried pushing it to classify uploaded images itself as NSFW, so it ends up triggering its own guardrails.

This turned out to be more interesting than expected. It’s inconsistent and definitely not robust, but in some cases relatively mild transformations are enough to flip the model’s internal safety classification on otherwise benign images.

This isn’t about bypassing safeguards, if anything, it’s the opposite. The idea is to intentionally stress the safety layer itself. I’m planning to open-source this as a small tool + UI once I can make the behavior more stable and reproducible, mainly as a way to probe and pre-filter moderation pipelines.

If it works reliably, even partially, it could at least raise the cost for people who get their kicks from abusing these systems.
by duggan | 2026-01-05 08:02:10+00:00
LLMs work very well for a variety of software tasks — we have lots of experience around the industry now.

If you haven’t been convinced by pure argument in 2026 then you probably won’t be. But the great thing is you don’t have to take anyone’s word for it.

This isn’t crypto, where everyone using it has a stake in its success.
You can just try it, or not.
by olowe | 2026-01-05 08:05:08+00:00
Could you cast your mind 10 years back to 2015 and ask yourself the same question? Then fast-forward to today. What could you have done in 2015 that could have prepared you better for 2025? You may be able to do the same for 2005 and 2015.

It may be worth trying to ignore the feeling of "AI taking over the field". There's so much noise around this stuff as, personally, it's very difficult to differentiate hype and utility at the moment.
Show HN: Transform messy server logs into clean, readable format
by bardonadam | 2026-01-05 08:05:13+00:00
Hey HN,

I often check raw logs - out/error.log, nginx's access.log, etc, but they're hard to read

So I built beautiful parser & formatter with filtering and syntax highlighting

PrettyLog supports many common log formats out of the box:
- nginx access logs
- Apache error logs
- Syslog (RFC 5424)
- Logfmt (Heroku/Go style)
- Docker logs
- standard/node.js logs
- Spring Boot logs
- AWS VPC Flow logs
- Kubernetes kubectl get events

If you need something specific, you can use custom regex parser. Processing is 100% local. Your data never leaves the browser.

I hope you find it as useful as I do. Would love to hear your feedback

Adam
by krystofbe | 2026-01-05 08:05:21+00:00
Hey! I built https://hnbrew.com as a replacement since hndigest.com stopped working. I was a heavy user myself and missed getting my daily digest, so I decided to clone it.

It does the same thing - sends you a daily/weekly digest of top HN stories. Just enter your email on the landing page to subscribe.

Hope it helps!
by bonesss | 2026-01-05 08:06:53+00:00
That’s the issue, though, isn’t it? Why isn’t it black and white? Clear massive productivity gains at Google or MS and their dev armies should be visible from orbit.

Just today on HN I’ve seen claims of 25x and 10x and 2x productivity gains. But none of it starting with well calibrated estimations using quantifiable outcomes, consistent teams, whole lifecycle evaluation, and apples to apples work.

In my own extensive use of LLMs I’m reminded of mouse versus command line testing around file navigation. Objective facts and subjective reporting don’t always line up, people feel empowered and productive while ‘doing’ and don’t like ‘hunting’ while uncertain… but our sense of the activity and measurable output aren’t the same.

I’m left wondering why a 2x Microsoft of OpenAI would ever sell their competitive advantage to others. There’s infinite money to be made exploiting such a tech, but instead we see highschool homework, script gen, and demo ware that is already just a few searches away and downloadable.

LLMs are in essence copy and pasting existing work while hopping over uncomfortable copyright and attribution qualms so devs feel like ‘product managers’ and not charlatans. Is that fundamentally faster than a healthy stack overflow and non-enshittened Google? Over a product lifecycle? … ‘sometimes, kinda’ in the absence of clear obvious next-gen production feels like we’re expecting a horse with a wagon seat built in to win a Formula 1 race.
by arjie | 2026-01-05 08:07:38+00:00
One thing I thought when I read this is that if you only train for the competent adversary you may be unprepared for the one who is different. Could you lose if your theory of mind was wrong? But when I made a rudimentary search for this (Google is so hard to phrase, but I also used LLMs) it seems that in practice every victory is through the commander's skill in navigating their constraints. Surely, some commander out there did a stupid thing and attacked when the rational thing would have been to retreat and it worked, or surely someone didn't keep reserves just out of sheer incompetence and it paid off. But I can't find an example.

Either battles are uniquely unforgiving for bad strategy (entirely possible, they are usually long, which has a law of large numbers effect to it) or military historians back-form rationalizations for the victors or some other third thing I can't think of right away.

I was hoping there'd be some crystal clear example of the equivalent of not folding on an off-suit 2/7 and having it play off. But I found none. Interesting.
by ChiNutri20 | 2026-01-05 08:08:00+00:00
Lesson 11 (Abstractions move complexity) and Lesson 20 (Time > Money) are two sides of the same coin.
In engineering, we talk about "leaky abstractions" in our code. But the biggest leaky abstraction is often our own health. We treat our bodies as a "boring dependency" that will always work, but burnout and RSI (Repetitive Strain Injury) are essentially the ultimate system outages.
Just as "Novelty is a loan" (Lesson 5), neglecting your physical "hardware" early in your career is a high-interest loan that you end up repaying in your 40s. Real seniority isn't just about navigating people and politics—it's about managing your personal energy so you actually have the health to enjoy the "compounding" (Lesson 21) that comes at the end.
by justinclift | 2026-01-05 08:08:10+00:00
> In theory I could use an old version of Hugo, but I have no idea when it broke, so how far do I go back?

Pretty sure the version of Hugo used to generate a site is included in metadata in the generated output.

If you have a copy of the site from when it last worked, then assuming my above memory is correct you should be able to get the exact version number from that. :)
by tristenharr | 2026-01-05 08:08:10+00:00
That is a valid concern! To clarify:

Configurability: We absolutely plan to make the 10ms yield interval configurable (or opt-out) in the runtime settings. It is currently a default safety rail to prevent async starvation, not a hard constraint.

Concurrency Options: It is important to note that LOGOS has three distinct execution primitives, and this yield logic doesn't apply to all of them:

Simultaneously: (Parallel CPU): This compiles to rayon::join or dedicated std::threads. It does not use the cooperative yield check, allowing full blocking CPU usage on separate cores.

Attempt all: (Async Join) & Launch a task: (Green Threads): These compile to tokio async tasks. The cooperative yield is specifically here to prevent a single heavy async task from blocking the shared reactor loop.

So if you need raw, uninterrupted CPU cycles, you would use Simultaneously, and you wouldn't be forced to yield.
by sph | 2026-01-05 08:08:27+00:00
> Why do we even have linear physical and virtual addresses in the first place, when pretty much everything today is object-oriented?

What a weird question, conflating one thing with the other.

I’m working on a object capability system, and trying hard to see if I can make it work using a linear address space so I don’t have to waste two or three pages per “process” [1][2] I really don’t see how objects have anything to do with virtual memory and memory isolation, as they are a higher abstraction. These objects have to live somewhere, unless the author is proposing a system without the classical model of addressable RAM.

—-

1: the reason I prefer a linear address space is that I want to run millions of actors/capabilities on a machine, and the latency and memory usage of switching address space and registers become really onerous. Also, I am really curious to see how ridiculously fast modern CPUs are when you’re not thrashing the TLB every millisecond or so.

2: in my case I let system processes/capabilities written in C run in linear address space where security isn’t a concern, and user space in a RISC-V VM so they can’t escape. The dream is that CHERI actually goes into production and user space can run on hardware, but that’s a big if.

The memory management story is still a big question: how do you do allocations in a linear address space? If you give out pages, there’s a lot of wastage. The alternative is a global memory allocator, which I am really not keen on. Still figuring out as I go.
by servo_sausage | 2026-01-05 08:09:07+00:00
China is already doing abominable things; how people react to additional surveillance is always related to what the state is actually doing with that information.

So a system that supports the abduction of polital rivals (an actual human rights violation) is not the same as a system that supports the lawful arrest of someone breaking a law that's accepted as part of a democracy.

I also think the scale of investment plays a part, the investment in surveillance in China is absurd. Its a significant number of people (per capita) that do nothing but monitor people. These new systems are rather cheap; so much so that they feel a whole lot more inevitable.
by dzhiurgis | 2026-01-05 08:10:45+00:00
US seems outlier while rest of the world fatalities are decreasing - https://en.wikipedia.org/wiki/Motor_vehicle_fatality_rate_in...

This also needs to be divided per miles driven as those are constantly increasing.

Finally, my guess pedestrians are disproportionately more at fault here - mostly impairment (meth, fentanyl), but also smartphones and headphones in particular. Drivers are mostly distracted by phones mostly, not by adjusting climate controls for 2-3 seconds.
by ma1or | 2026-01-05 08:10:48+00:00
Your tool might actually do more than you planned.

When I started building Replay, the mission was specific: Behavior-Driven UI Reconstruction. I wanted to solve the problem where static screenshots fail to capture the flow, animations, and logic of a complex SaaS or mobile app.

But I got curious:
"What happens if I feed the engine a video of a physical product instead of a digital interface?"

I grabbed a pack of wipes, recorded a 5-second clip, and dropped it into the pipeline.

The result? Watch the video.

Even though this isn't Replay's core function, the engine "understood" the product. It extracted the color palette, the brand vibe, and the context-generating a fully responsive, sales-ready Landing Page in one shot.

It turns out that Video-to-Code isn't just about UI flow; it's about context.

I am still laser-focused on polishing the core engine and shipping new features, but seeing the tool handle edge cases like this is the best part of the founder journey.

If you are curious to see what Replay looks like right now: Link to the tool in the first comment.
by visarga | 2026-01-05 08:11:19+00:00
Why train to pedal fast when we already got motorcycles? You are preparing for yesterday's needs. There will never be a time when we need to solve this manually like it's 2019. Even in 2019 we would probably have used Google, solving was already based on extensive web resources. While in 1995 you would really have needed to do it manually.

Instead of manual coding training your time is better invested in learning to channel coding agents, how to test code to our satisfaction, how to know if what AI did was any good. That is what we need to train to do. Testing without manual review, because manual review is just vibes, while tests are hard. If we treat AI-generated code like human code that requires a line-by-line peer review, we are just walking the motorcycle.

How do we automate our human in the loop vibe reactions?
by robocat | 2026-01-05 08:11:23+00:00
> I wonder if it might qualify as the best thought terminating cliché I've seen this year!

That is certainly the best back-handed compliment I have received in the last hour. Thank you. Your writing molests me.

> is that the people I find interesting are usually busy doing interesting things, so the boring ones are the only regular people at social gatherings.

That comes across to me as a refreshingly honest self-centred view.

You recall me of a friend who does contemporary dance as part of a troupe. I find them interesting - however I also find sewage plants and stink beetles interesting.

What's I'm trying to say is that finding people interesting (or boring) probably says more about you than it does about them.

The smartest people I know seem to be interested in everything. I often find people interesting for reasons I would find difficult to admit to them. I'm not that smart.

I find myself boring - contemptuous through familiarity?

And yeah, I'm rather cynical about "norms" too.
by divan | 2026-01-05 08:11:34+00:00
Taclking layer of complexity often feels like this. I remember 2010 and my frustration of evergrowing complexity of networking backend programming (C++ mainly), and disovering Go. "Go made programming fun again" was a common phrase at the conferences.

I feel similar with web-apps development too, except we're not solving complexity here – we just outsorcing it to "AI-developer". None of the deficiencies of web stack are solved here. The worst part is that complixity of web stack is mainly _accidental_ – i.e. coming from the tooling, not from the problem domain.
by nobody9999 | 2026-01-05 08:12:06+00:00
>President Trump was elected democratically by the people of the US, by majority in both the electoral and popular vote.

It was a plurality of the popular vote, not a majority[0]. A majority is >50%. Trump received 49.8%.

[0] https://en.wikipedia.org/wiki/2024_United_States_presidentia...
by arkh | 2026-01-05 08:12:58+00:00
> I've literally never had the thought of "how do I influence other people." Why is that considered a valuable skill?

If you're a software developer you must have thought "current priorities are not right, we should do X for the users / Y to get better quality" and tried to influence your management to get those priorities moved. Maybe by starting a campaign with your users so the demands come from multiple services and not just you, or by measuring quality indicators and showing how what you want to implement would improve them etc.

That's why you want to start getting coffee with people, maybe go outside with the smokers. It can take months of "work" to get people to propose the idea you want done.

But this kind of influencing won't help your career.
by doomvwr | 2026-01-05 08:12:59+00:00
N26 SE | Sr. SREs, Sr. Cloud Security Engineers, JVM Backend Engineers | Berlin, Barcelona| https://www.n26.com | Full-time

N26 has reimagined banking for today’s digital world. Technology and design empower everything we do and it’s how we are building the global banking platform the world loves to use.

We've eliminated physical branches, paperwork, and hidden fees for an elegant digital experience and supreme savings. Giving people the power to live and bank their way is what gets us out of bed in the morning and inspires the work that we do.

We are headquartered in Berlin with offices in multiple cities across Europe, including Vienna and Barcelona, and a 1,500-strong team of more than 80 nationalities.

Full job descriptions here: https://n26.com/en-eu/careers, you can email me with questions/resumes dmitrii.sergeev@n26.com
by renewiltord | 2026-01-05 08:14:31+00:00
Well, you're supposed to get the lifelong disability by claiming PTSD, Tinnitus, Migraine. That's why disability has skyrocketed recently. Social media has allowed spreading the game plan pretty widely and most veterans know how to get as close to 100% disability as possible. There are entire channels dedicated to gaming this.

There are lots of healthy people out there claiming this (though obviously there are lots of people who do actually have the conditions as well).
by kabes | 2026-01-05 08:14:56+00:00
To me, what sucks the most about programming is dealing with ecosystem issues. You want to write a little tool for personal use, but NPM starts acting out. Then you need to do something in java, which you don't use very often, and you get a giant maven error stack trace which you now need to try and understand. All of this frustration is gone since I use AI and I can focus solely on the thing I'm trying to accomplish.
by 89netraM | 2026-01-05 08:15:13+00:00
A friend and I wrote our master thesis on how to ergonomically fit monads into imperative programming languages [^1], taking inspiration from Haskells do-notation to get away from the method chaining. It's more on the theoretical side, but we did write a small implementation in C# [^2] (we use the exclamation mark as bind). We really could have used som better types though, this post seems have found a better direction.

[^1]: https://odr.chalmers.se/items/91bf8c4b-93dd-43ca-8ac2-8b0d2c...

[^2]: https://github.com/master-of-monads/monads-cs/blob/89netram/...
by 1718627440 | 2026-01-05 08:15:19+00:00
Dieselgate essentially happened due to the interaction of the EU and US emission standards. EU emission standard got lowered until it wasn't physically possible, without reducing the machine power, which the market doesn't want. Thus, they introduced test mode, which does have the emissions actually allowed, but is worse in all other aspects. This worked in the EU, because the tests environment is defined and no other tests are performed.

The US regulators wouldn't have cared about higher emission levels as all cars in the US have them anyway, but the cars were still introduced with the EU specs. First because otherwise they would need to remeasure all the car emissions and second, because even as the real emissions would still be low by US standards that would have questions why the same car has different emissions in EU and US. That plan however didn't work out, as the US doesn't do tests in a controlled environment, but while actually driving. Thus, the scandal started becoming public. That is the official part.

The following comes from an "industrial expert", that held a guest lecture at our university: This whole thing was actually done with knowledge (and silent agreement) of the EU regulators, as they aren't dumb and know what is physically possible. However they were still forced to act once this became public in the US, as the politicians and the general voter don't like regulators doing there own thing against the law. Also this was done by a VW supplier, which is basically the only shop in town, so of course this wasn't specific to VW.

So in my opinion, blaming VW, while legally correct, is actually kind of dumb. At last a bit anecdotal evidence: We also did the update for our car. Of course we tried to delay it, but eventually the car would have lost it's street legality, so we needed to do it. And of course afterwards the car is louder, has visible emissions and smells (more). Thanks. Sometimes the best option would have been to just keep quite and stick to gentlemen agreements.
by pipo234 | 2026-01-05 08:16:28+00:00
I guess it's a bit more subtle.

The point that the article makes is about opening up NeXT to other hardware platforms. So while from one perspective, you might argue it lives on inside Apple, you could also argue that's where nextstep died.

In the early 2000s I worked for a company that went all in on NeXTSTEP a decade earlier. The product was developed in a "4 GL" called 4D or 4th dimension.

We had to do a painful migration to windows nt/xp because NeXTSTEP was discontinued and apple actively fought to kill attempts to fork or open source the code base.
Show HN: Kandinsky AI Video Generator
by Zach_HE | 2026-01-05 08:17:38+00:00
Kandinsky AI is a generative model family designed for fast, high-quality video and image creation. Supporting text-to-video, text-to-image, and image-to-video workflows, it delivers high-resolution results with style consistency, realism, and full creative control. Ideal for content creators, marketers, studios, designers, and developers. Kandinsky AI empowers users to produce animations, visual storytelling, branding assets, prototypes and production-ready content, instantly and at scale.
Show HN: Spectra – Visuals should be infrastructure, not manual work
by JannikJung | 2026-01-05 08:18:15+00:00
Marketing visuals are stuck in 2010.

Copy gets versioned. Data flows through pipelines. Code deploys automatically. But visuals? Someone's still in Figma resizing assets and copy-pasting translations.

Spectra treats visuals as infrastructure.

- Define a template once with variables (text, images, locales, themes)
- Generate all variations programmatically
- Output is deterministic — same input, same result
- API-ready for automation pipelines

There's an AI layer (Iris) for template modifications via natural language: "add German locale" or "create dark mode variant."

But the core is structured generation, not generative AI magic.

Built for teams shipping high volumes of App Store screenshots, ads, banners — anywhere consistency at scale matters more than pixel-level creativity.

Site: https://spectra.fm

Live on Product Hunt today: https://www.producthunt.com/products/spectra-fm?launch=spect...

Would love feedback — especially pushback on the approach.