MurphyA/hacker-news huggingface dataset:
by 1313ed01 | 2025-11-07 12:28:13+00:00
In a game design context, he is definitely using "uncertainty" in a wider sense, as popularized by Greg Costikyan's Uncertainty in Games book.

In that sense of the word, it's not only about random things, but also things like "will I click at just the right time to head-shot that enemy?" or "I will checkmate the next turn unless my opponent thinks of some clever move that I don't?"). And the theory is that once you run out of uncertain things there is no more a game, as the player know how it will end and there is nothing more that can fail or anything unexpected that can happen. Basically like reading the end of a book you have already read before, so you know exactly what will happen.

And depth from a game design pov is also not necessarily strictly positive. Make the game too deep and there is, as you say, pure random. You could keep adding rules to chess to make it 100% impossible for any human to remotely guess what kind of move to make, and that's when you added so much uncertainty that it became too deep.
by hu3 | 2025-11-07 12:28:35+00:00
Not your parent commenter but their point was clear to me.

To me, they don't buy the argument that the snowball effect is significant enough to overcome technical merits of different frontend frameworks.

And I'll add that: older libraries like React have at least one disavantage: there's a lot of outdated React code out there that AI is being trained on.
by oxfordmale | 2025-11-07 12:28:37+00:00
I think you might be reading a bit too much into this.

He’s been with Meta for 11 years and is likely in a very comfortable financial position, given the substantial stock options he’s received over that time.

He also mentioned the arrival of a new child, and it’s well known that Meta's work-life balance isn’t always ideal.

On top of that, Meta, like many major tech companies, has been shifting its focus toward LLM-based AI, moving away from more traditional PyTorch use cases.

Considering all of this, it seems like a natural time for him to move on and pursue new, more exciting opportunities.
Show HN: Extending LLM SVG generation beyond pelicans and bicycles
by tkgally | 2025-11-07 12:28:50+00:00
Inspired by Simon Willison’s pelican-riding-a-bicycle benchmark, I used Claude, Claude Code, and OpenRouter to get SVGs from six models for thirty similar prompts. Example: “Generate an SVG of a venus flytrap swallowing a street lamp.”

I don’t know what to make of the results, but I had fun with the project.
by pornel | 2025-11-07 12:28:51+00:00
Fil-C will crash on memory corruption too. In fact, its main advantage is crashing sooner.

All the quick fixes for C that don't require code rewrites boil down to crashing. They don't make your C code less reliable, they just make the unreliability more visible.

To me, Fil-C is most suited to be used during development and testing. In production you can use other sandboxing/hardening solutions that have lower overhead, after hopefully shaking out most of the bugs with Fil-C.
by rsynnott | 2025-11-07 12:29:20+00:00
Sidenote: Has there been serious research into _why_ these 'food deserts' happen (or at least their urban form; the rural version seems more explicable), and/or why they seem to happen in the US more than in other developed countries, does anyone know? On the face of it, even as a fairly market-sceptical person, this is one that I would kind of expect the invisible hand to deal with.
by HarHarVeryFunny | 2025-11-07 12:30:52+00:00
> What I find most interesting with this is that it shows they believe there is nothing unique at Meta related to AI

Whether or not this is the case, I don't get this as being the reason for Sousmith leaving - it sounds as if he is just ready for a change.

Still, it is noticeable that with many of the AI companies claiming that their version of "AGI" is just around the corner, developers and staff don't appear to be particularly excited about this (I assume they realize it is just hype, not some momentous advance around the corner), and leave to pursue different things, such as Mira Murati starting a fine-tuning company, Karpathy going back to education, others switching ship (typically from OpenAI to Anthropic), etc.
by joshmn | 2025-11-07 12:31:52+00:00
My experience in finding one (15 people at the company I’m currently at, and I’m one of 3.5 engineers) (.5 because founder still codes more than we’d like him to) was effectively reaching out to companies that I knew didn’t have job postings up, and was the size that I’d fit into. I learned quickly that not every vacancy is posted publicly.
by JohnLocke4 | 2025-11-07 12:32:57+00:00
Good OPSEC is surprisingly simple and boring. Essentially, it just boils down to using tor and not accidentally exposing sensitive information, which is how Ross Ulbricht got caught. (okay, it is more than that but in essence it is true)

There are probably many people in prison right now because tor is awfully slow. If you don't have the patience for tor you probably also don't have the patience for prison.
by Marshferm | 2025-11-07 12:33:33+00:00
Remember, there are no algorithms in nature. The binary has a different relationship to data in and of itself (data is analog).

When we speak of syntactical essence, we’re speaking more than simply AI, I’m including the binary cauterization.

The sterile solutionism that tears away the syntax from operation is a massive hole, a void that segregates animal thought from machine.

Know what you are really talking about.

That engineers do not recognize the toyness, the cheap practicality of remaining at the binary at the expense of syntax-analog says an immense amount of where we are and how bad things are.

What's so shocking I think is how comp sci programs separated fromeven basic principles of engineering to craft software. Engineers at least had to adhere to resonances and structural loads and geologic principles. Software has no tether to reality except as A/B tested cog-sci intuition, which has nothing to do with neuroscience or even psychology. The only rules of software is to get the audience using it and extracting value "within reason" which is dictated by Wall Street. Software is in a sense, where animal life goes to die.

Try seeing AI as the symptom humanity can finally recognize about how badly the last 50 years have been of the PC age.
by stared | 2025-11-07 12:34:57+00:00
In 2018, I co-wrote a blog post with the inflammatory title “Don’t use TensorFlow, try PyTorch instead” (https://news.ycombinator.com/item?id=17415321). As it gained traction here, it was changed to “Keras vs PyTorch” (some edgy things that work for a private blog are not good for a corporate one). Yet the initial title stuck, and you can see it resonated well with the crowd.

TensorFlow (while a huge step on top of Theano) had issues with a strange API, mixing needlessly complex parts (even for the simplest layers) with magic-box-like optimization.

There was Keras, which I liked and used before it was cool (when it still supported the Theano backend), and it was the right decision for TF to incorporate it as the default API. But it was 1–2 years too late.

At the same time, I initially looked at PyTorch as some intern’s summer project porting from Lua to Python. I expected an imitation of the original Torch. Yet the more it developed, the better it was, with (at least to my mind) the perfect level of abstraction. On the one hand, you can easily add two tensors, as if it were NumPy (and print its values in Python, which was impossible with TF at that time). On the other hand, you can wrap anything (from just a simple operation to a huge network) in an nn.Module. So it offered this natural hierarchical approach to deep learning. It offered building blocks that can be easily created, composed, debugged, and reused. It offered a natural way of picking the abstraction level you want to work with, so it worked well for industry and experimentation with novel architectures.

So, while in 2016–2017 I was using Keras as the go-to for deep learning (https://p.migdal.pl/blog/2017/04/teaching-deep-learning/), in 2018 I saw the light of PyTorch and didn’t feel a need to look back. In 2019, even for the intro, I used PyTorch (https://github.com/stared/thinking-in-tensors-writing-in-pyt...).
by Earw0rm | 2025-11-07 12:35:30+00:00
There was, due to lead compounds in vehicle exhaust. Nasty stuff.

These days, tyre microplastics and diesel particulates are still a concern, but there's little hard science around the hazards of eating them in small quantities - there's microplastic in basically everything, to some degree, so you're not making it appreciably worse - and agricultural farm machinery is a worse diesel PM offender by far than a street's worth of modern cars.
by throw_m239339 | 2025-11-07 12:36:21+00:00
> Thanks god. The days people kept inventing new JS frameworks or even dialects (coffeescript, remember?) every three months couldn't be gone fast enough.

Coffeescript helped Javascript to evolve the right way, so in retrospect, it was absolutely a good thing. It's like people here don't remember the days of ES3 or ES5...

And the days? Remember Typescript right now? Typescript is not Javascript.
by jwr | 2025-11-07 12:36:27+00:00
> I used per-account email with alias services and password managers.

For people who want to do this, be sure to get it right. I run a SaaS with a free tier, and I see people register with "fancy+nospam+servicename@gmail.com" addresses. Many of those become undeliverable or are left unread forever because of filtering rules. So when my system sends a warning E-mail that the account will be deleted due to inactivity, it doesn't get read, which leads to suboptimal outcomes for everyone involved.
by joshmn | 2025-11-07 12:37:28+00:00
I’m sorry you got the idea that my users were spamming Reddit with referral links. It was hardly like that and I personally checked that every user was being tasteful, and sent “don’t spam” only a handful of times. I had alerts setup for each source of referrer (via analytics) and for each one that came from reddit (parsed by the ID of the post) I'd individually check to ensure that it wasn't "bad," and that the user wasn't just schilling—if an unreasonable (see: 3) last comments were slinging a referral link, I'd straight up ask them to remove them.

That probably doesn’t change your perception—I, too, feel like Reddit is pretty bad these days—but I felt the need to say something anyway. I ran a pretty tight ship and had placed a lot of importance on perception and reputation. Building trust was important to my operation, from both a growth standpoint and a customer service standpoint. When shit broke (as it often did, considering I operated as the mouse instead of the cat), my users took my word that an attempted fix was in the works.
by JohnLocke4 | 2025-11-07 12:38:30+00:00
Preventing someone from getting value out of their work is theft - not matter how it is done. Copying a dead person's work isn't theft because a dead person can't create value, but stealing a dead person's car is still theft, because something of value is gone.

Stealing a car you were never going to buy and making an exact replica of a car you were never going to buy is two entirely different things.
by wakawaka28 | 2025-11-07 12:40:29+00:00
Don't get me wrong, it sounds positive. A direct attack on Fil-C would have seemed mean-spirited so there is a lot of misdirection. Maybe the author doesn't even see what he's doing because he's so deep in it. But to me the message is clear. No matter what tools are developed for C and C++ to mitigate memory issues, Rust people will never concede that enough of these issues have been solved. They demand a complete rewrite of everything, or at least gradual replacement of all C and C++ code with Rust. Even if Rust is worse in other ways, does not deliver true safety, has technical shortcomings and worse licensing, etc.

This post is very polite compared to what I've seen from some Rust fanatics. But it still strikes me as talking down to the C and C++ community, as if these languages are beyond redemption because they don't work the same as Rust.
by whstl | 2025-11-07 12:40:54+00:00
It's so funny how much that reminds me of working in a university acquired by a large for-profit corporation.

After the MBAs arrived, the whole thing was about selling shitty packages for students.

- The college was somehow legally allowed to charge a minimum, so people only needing one single class was still paying for 3.

- They would push high distance learning for anything they legally could, showing the same video of the same teacher to all their 10 universities and paying "tutors" a minimum wage to moderate hundreds of Moodle classes (if not putting Masters students to do it for half the minimum wage). So 80 students paying $1000 on average to take a 5 class, and some of those cost on average $2000 + server costs. What a business.

- Of course classes that had 10 people in it suddenly had 40. And for when there wasn't 40 people to attend, they would consolidate classes with another group and half would have to go to the other side of town for the one class that, if they didn't attend, would set back their tuition by one year.

But yeah, sure it makes more money.

When you don't even have to compete on quality, that's what happens.
by dudefeliciano | 2025-11-07 12:41:28+00:00
From what I understand the tetris effect is not caused by the video game itself, but by playing it for an inordinate amount of time. The "tetris effect" has been there way before tetris, any monotonous activity done for an excessive amount of time will cause a tetris effect (i think a lot of people are familiar with hypnagogic hallucinations while falling asleep after driving all day, for example).
by embedding-shape | 2025-11-07 12:42:09+00:00
> Every effort needs to be a part of the pipeline of demand and supply

It's almost unthinkable the amount of technology and innovations we would have never gotten if this was actually true in practice. So many inventions happened because two people happen to be in the same place for no particular reason, or someone noticed something strange/interesting and started going down the rabbit-hole for curiosities sake, with demand/supply having absolutely zero bearing on that.

I got to be honest, it's slightly strange to see something like that stated here out of all places, where most of us dive into rabbit-holes for fun and no profit, all the time, and supply/demand is probably the least interesting part of the puzzle when it comes to understanding and solving problems.
by aatd86 | 2025-11-07 12:42:33+00:00
As someone who is currently writing their own js framework, llms are able to generate code quite easily. So I am not worried that we will be able to see new frameworks.

Now, about the incentives? Probably less inference costs for llms, which probably means that they are more legible than the current state of the art for humans as well.

Less API changes than let's say react also means that the generated code as less branching although llms can adapt anyway. Cheaper.

Will probably be closer to the platform too (vanillaJS).
by HarHarVeryFunny | 2025-11-07 12:42:37+00:00
It's not clear if the ultimate SLMs will come from teams with less computing resources directly building them, or from teams with more resources performing ablation studies etc on larger models to see what can be removed.

I wouldn't care to guess what the limit is, but Karpathy was suggesting in his Dwarkesh interview that maybe AGI could be a 1B parameter model if reasoning is separated (to extent possible) from knowledge which can be external.

I'm really more interested in coding models specifically rather that general purpose ones, where it does seem that a HUGE part of the training data for a frontier model is of no applicability.
by stared | 2025-11-07 12:44:21+00:00
Actually, I opened “Teaching deep learning” and smiled as I saw how it evolved:

> There is a handful of popular deep learning libraries, including TensorFlow, Theano, Torch and Caffe. Each of them has Python interface (now also for Torch: PyTorch)

> [...]

> EDIT (July 2017): If you want a low-level framework, PyTorch may be the best way to start. It combines relatively brief and readable code (almost like Keras) but at the same time gives low-level access to all features (actually, more than TensorFlow).

> EDIT (June 2018): In Keras or PyTorch as your first deep learning framework I discuss pros and cons of starting learning deep learning with each of them.
by sweetgiorni | 2025-11-07 12:44:50+00:00
> The core issue is that, with otel, observability platforms become just a UI layer over a database. No one wants to invest in proper instrumentation, which is a difficult problem, so we end up with a tragedy of the commons where the instrumentation layer itself gets neglected as there is no money to be made there.

I don't think it's fair to say "no one wants to invest in proper instrumentation" - the OpenTelemetry community has built a massive amount of instrumentation in a relatively short period of time. Yes, OpenTelemetry is still young and unstable, but it's getting better every day.

As the article notes, the OpenTelemetry Collector has plugins can convert nearly any telemetry format to OTLP and back. Many of the plugins are "official" and maintained by employees of Splunk, Datadog, Snowflake, etc. Not only does it break the lock-in, but it allows you to reuse all the great instrumentation that's been built up over the years.

> The core issue is that, with otel, observability platforms become just a UI layer over a database.

I think this is a good thing - when everyone is on the same playing field (I can use Datadog instrumentation, convert it to OTel, then export it to Grafana Cloud/Prometheus), vendors will have to compete on performance and UX instead of their ability to lock us in with "golden handcuffs" instrumentation libraries.
by Libidinalecon | 2025-11-07 12:46:05+00:00
It isn't a bad deal for shareholders.

In the grand scheme of things though it is very much a "let them eat cake" moment in the history of neoliberalism. It is just such a total absurdity. I even read the words "equitable pay package"

The same week NYC elects Mamdani. As if these are two unrelated events.

I have been a free market person my whole life but when you start approving pay packages to jump Marcus Crassus in historical wealth, maybe it is a sign something is severely broken?

The neoliberal shareholder class will never fix this because they are unable to see anything is broken. "The market is always right".

The problem is people vote in a democracy.

The counter arguments will be so trivial to make too. How are these Tesla goals hit without public roads again? What is the AMZN share price without the use of public roads?

I completely forget in all this that the current federal government is so dysfunctional that it not even running.

It is hard to imagine a future in America that is not socialism with American characteristics.
by the8472 | 2025-11-07 12:46:09+00:00
> It does give you an awful lot of information about the specific hacks

No it doesn't. Enter <old email address> → 5 data breaches → first one says:

> During 2025, the threat-intelligence firm Synthient aggregated 2 billion unique email addresses disclosed in credential-stuffing lists found across multiple malicious internet sources

It doesn't tell me which site or which of the many passwords used together with that address. Just that it has been in a generic data dump.
Show HN: Phrasing.app – learn and maintain multiple languages
by barrell | 2025-11-07 12:46:31+00:00
Happy Friday HN -

For the past two years, I’ve been working on http://phrasing.app, a language learning application that makes it simple to learn and maintain multiple languages.

I built Phrasing for four main reasons:

1. *I wanted an app to learn multiple languages in parallel.* I don’t want to have to choose between improving a language I speak or learning a new language. I just want to open an app and ‘do languages’, and let technology decide what’s most effective for my goals

2. *I wanted an app that was as pleasant to use as it was effective.* was I was tired of choosing between form or function. It takes hundreds to thousands of hours to learn a language. I want an app that is nice to look at and enjoyable to use, while maximizing for efficacy, not engagement.

3. *I wanted an app that integrated spaced repetition & user experience.* Every spaced repetition application I’ve used has been a pure expression of the forgetting curve. This is maximally accurate… but also maximally stressful. The UX designer in me sobbed every time I used Anki. I want to enjoy my reviews, and would gladly sacrifice 1% of algorithmic accuracy if it means completing 2x the reviews.

4. *I wanted an app to learn all languages.* This whole project actually was kick-started when I tried to learn Arabic, and struggled for months to find quality learning materials. And Arabic is a major language! There’s still a bit of work to go before I can support all languages, but it already supports ~90 languages really well.

I’ve been using Phrasing every day since May of this year, and I’ve been very happy with my progress. I’ve been able to study multiple languages, at various speeds, all without mixing them up and never really leaving the application.

I’ve been getting really good reviews from recent users, and I’m hoping that this project is helpful to other language learners & polyglots in the crowd.

Especially if you’re learning an underserved language, I really hope you’ll consider Phrasing! I would absolutely love to get at least one person learning every language we support.

Technically, this project is also a one person project, built with Elixir on the server and ClojureScript on the client. It’s gone through probably 5 major versions, and the most recent version hand rolls nearly everything. As a solo dev I’m always more than happy to talk about the technology :D

—

PS: if this project is interesting to anyone who is looking to set off on their own, I’m actively looking for a co-founder :D (Europe only)

PSS: A big thanks to Jarrod Ye and all the maintainers of FSRS for making this project possible
by csomar | 2025-11-07 12:47:50+00:00
It's more than a couple. The fundamental issue is not the bugs themselves (these are expected) but that, from my perspective, otel is at odds with the observability business because these actors have little interest to contribute back to telemetry agents since anyone can reap the rewards of that. So instead they'd focus more on their platforms and the agents/libraries get neglected.

It's a great idea, in principle, but unless it gets strong backing from big tech, I think it'll fail. I'd love to be proven wrong.
by ehnto | 2025-11-07 12:48:11+00:00
Interesting, anecdotally I would say all my more serious gaming friends hit this at some point. But it takes serious consecutive hours in game.

Something interesting about VR gaming is the very heightened sense of spatial involvement. If I play a game on the screen I remember it as if I played it on a screen. But if I play in VR my instinct when reflecting on it is that I was there, in the game world. It can feel silly to talk about with people who don't play VR, but all the ways in which you remember experiences in the real world are the same. When you talk about it you can't disconnect them without just adding "in VR" to the end of everything. It's never that your character did the thing, it was you, you were there.

I've had the opportunity to drive on race tracks that exist both in VR and in real life and the spatial relationship is 1:1. So your brain is blending the two experiences seamlessly as you drive around, and you apply all the same spatial cues from VR onto the real world.