MurphyA/hacker-news huggingface dataset:
Show HN: Trails – a transparent, privacy-first URL shortener API
by anwitars | 2025-08-17 14:10:24+00:00
Hi HN,

I built Trails, a transparent URL shortener with a simple public API.

Unlike most URL shorteners, Trails: - does not store IPs - visits are counted using a SHA-256 hash, so they can’t be linked back to users - Lets you “peek” at a short link before clicking, so you know where it goes - Has no accounts, paywalls, or tracking — just a free API

You can find examples on the GitHub page, and also it has a public website that uses this service: https://trls.link/

API repo: https://github.com/anwitars/tiny-trails Web client repo: https://github.com/anwitars/trails-web

I would love feedback on design decisions, and the roadmap included in the API's readme, especially the dynamic Trails idea.
by Tiberium | 2025-08-17 14:10:55+00:00
I think Nim and Zig target very different audiences and have very different goals. Nim is about being a big, powerful language with lots of features, so that you have the freedom to use it the way you want, e.g. there is OOP with methods, but it's completely optional. Zig is explicitly against that, even on the homepage you can see: "No hidden control flow. No hidden memory allocations. No preprocessor, no macros.". While memory management in Nim by default is completely automatic, and templates/macros are quite common.

It makes much more sense to compare Nim to, say, Swift, D, or other modern compiled languages with lots of syntax sugar.
by reactordev | 2025-08-17 14:11:13+00:00
I know that, I was making a statement about how you can.

Not exactly sure what your point is. If an LLM can take an idea and spit out words, it can spit out instructions (just like we can with code) to generate meshes, or boids, or point clouds or whatever. Secondary stages would refine that into something usable and the artist would come in to refine, texture, bake, possibly animate, and export.

In fact, this paper is exactly that. Words as input, code to use with blender as output. We really just need a headless blender to spit it out as a GLTF and it’s good to go to second stage.
by gchamonlive | 2025-08-17 14:13:19+00:00
But that's the thing, revenue is a very poor metric for quality. It's a very good metric for marketing as you said, but focusing solely on that, which is what linkedin rewards, and potentially forgetting to invest time in becoming an objectively better developer is why linekdin rewards mediocrity as the article says.

So while you are disagreeing, you are actually reinforcing the article's central argument.
by yusege | 2025-08-17 14:14:11+00:00
Mediocrity is everywhere—on every platform. Even on blogs. Even in scientific publications. But if you’re creating something worth reading, why hesitate to post it on LinkedIn or anywhere else? Valuable content doesn’t lose its quality just because of where it’s published. Dumb comments don’t change that either.

I actually think creators who produce valuable work should be just as visible as the mediocre ones, so that over time, the difference becomes clear and mediocrity slowly fades. Sure, the marketing and self-promotion side of things might feel uncomfortable, especially for developers. But in a society where the ignorant are bold, if the thoughtful and knowledgeable don’t show the same courage, that society is doomed.

No matter what kind of nonsense you come across, let science and reason be your guide—and keep creating and sharing.
by Normal_gaussian | 2025-08-17 14:14:26+00:00
For me - it doesn't support secure and reliable dependency vendoring.

The best way to do this atm. is using (and configuring) yarn for zero-installs.

This keeps dependencies inside the codebase so that: * Issues can be easily traced to the code that actually ran - development and deployment are the same. * Deployment doesn't depend on package repositories. * Deployment is secure from many kinds of attacks. * It is possible to transparently patch packages. * Development is only internet dependent when adding a new package. * and the best ease-of-use - no reinstall when changing branches.
by thechao | 2025-08-17 14:14:32+00:00
My high school was right across from a branch of a university (UHD) where the PhD candidates developed IQ tests. We (the HS students) could take them for extra credit. My favorite example was a block-arranging test (there was a set of blocks & some pictures). Anyways, they printed the blocks "symmetrically"; once I figured that out, making the picture was limited only by how quickly I could move. (The test normally had you looking at all sides of the cube, repeatedly.) My "IQ" was well over 200 on that test. The candidate said that it was going to set their lab back bag years.
by CamperBob2 | 2025-08-17 14:15:23+00:00
The irony isn't that the GP is right, because they are. "AI does not understand anything: it just predicts the next token based on previous tokens and statistics."

The irony is that we've recently learned that "just predicting the next token" is good enough to hack code, compose music and poetry, write stories, win math competitions -- and yes, "give synonyms matching a particular metrical pattern" (good luck composing music and poetry without doing that) -- and the GP doesn't appreciate what an earthshaking discovery that is.

They are too busy thumping their chest to assert dominance over a computer, just as any lesser primate could be expected to do.
by hackyhacky | 2025-08-17 14:16:16+00:00
> I emphasize probably because the duck test doesn't allow you to distinguish a duck from a highly sophisticated animatronic. It's a good test, don't get me wrong, but that "probably" is a pretty important distinction.

Why is it an important distinction? The relevance of the duck test is that if you can't tell a duck from a non-duck, then the non-duck is sufficiently duck-like for the difference to not matter.
by lazide | 2025-08-17 14:16:22+00:00
Honestly, I’ve seen a concerning behavior where the failures seem to escalate and get worse over time.

Flakey WiFi at the start? Just flat out broken now.

Weird stuff like nearly impossible to find/configure network connections? Worse over time.

It’s like whatever criteria the MS PM’s are using is prioritizing ‘engagement’ (aka how frustrated and angry someone gets at the OS) over anything else.
by avinassh | 2025-08-17 14:16:25+00:00
OP has this first:

> If a system has distributed-consensus mechanisms, many different forms of event-driven communication, CQRS, and other clever tricks, I wonder if there’s some fundamental bad decision that’s being compensated for (or if the system is just straightforwardly over-designed).

then later down in the article:

> Send as many read queries as you can to database replicas. A typical database setup will have one write node and a bunch of read-replicas. The more you can avoid reading from the write node, the better - that write node is already busy enough doing all the writes.

isn't this same as CQRS
by FirmwareBurner | 2025-08-17 14:16:41+00:00
Since when is shitty designed HW bricking itself from OS I/O calls, a software issue?

Sure, Microsoft can issue a SW workaround to mitigate this issue as they did for Intel Skylake CPU issues, but that doesn't change the fact that the core issue is faulty designed HW.

Why don't SSD manufacturers test their shit before shoveling it out the door to consumers?
by brookst | 2025-08-17 14:17:46+00:00
With more thorough prompting:

> Complete the following joke. Think carefully and make it really funny! Think like a great comedian and find that perfect balance of simple, short, surprising, relevant, but most of all funny. Don’t use punchlines that are irrelevant, non sequiturs, or which could be applied to any other setup. Make something funny just for this one setup! Here goes: Why did the sun climb a tree?

Claude Opus 4.1:

“To finally get some shade”

GPT-5:

“To demand photon credit from the leaves”
by voidhorse | 2025-08-17 14:18:01+00:00
> I think my biggest objection to mandating formalism (in the abstract - I do find value in it in some situations) in computing is how little we know about computing compared to what we know about aviation or bridge building.

I don't think that we should mandate formalism. I'm just trying to say that diminishing the value of formalism is bad for the industry as a whole.

And point taken about maturity, but in that sense if we don't encourage people to actually engage in defining specification and formalizing their software we won't ever get to the same point of maturity as actual engineering in the first place. We need to encourage people to explore these aspects of the discipline so that we can actually set software on better foundations, not discourage them by going around questioning the inherent value of formalism.

Rust is a particularly good example because, as other commenters have pointed out, if we believe it's a waste of time to formalize the language we purportedly want everyone to use to build foundational software, what exactly would we formalize then? If you aren't going to formalize that because it "isn't worth it", well, arguably, nothing is worth formalizing then if the core dependency itself isn't even rigorously defined.

People also forget the other benefits of having a formal spec for a language. Yes it enables alternative implementations, but it also enables people to build tons of other things in a verifiably correct way, such as static analysis checks and code generators.
by jader201 | 2025-08-17 14:18:26+00:00
Yeah, definitely.

I have a Kia EV6, and just saying that if the same “patch” is offered for it, I won’t think twice about paying $65 for it.

I’d also not be super happy they didn’t cover it, but I saw a comment about never buying a Hyundai because of this, and not sure I’d be that upset about it.

There’s a line, for sure, but $65 wouldn’t be it, for me.
by fsflover | 2025-08-17 14:18:42+00:00
I don't understand why obsolete technologies by MS are often upvoted on HN and become the first replies, while the corresponding working, decentralized technologies go to the bottom. Matrix exists and has a preliminary P2P version [0,1]. Other messengers were also mentioned in the comments here. Another example of such tendency is here: [2].

[0] https://arewep2pyet.com/

[1] https://news.ycombinator.com/item?id=23393935

[2] https://news.ycombinator.com/item?id=44898242#44898884
by gyulai | 2025-08-17 14:19:39+00:00
So, there I was in 2016. It had been 13 years since I had last entertained the desire to learn a new programming language (I had landed on Python, back then). The serious contenders were Go, Nim, and Rust. I landed on Nim back then, thinking to myself: Man, this language really has a future. I did my next side project in Nim, and loved it like I've loved no other language over the course of my (as of now, in 2025) 28 years of programming. But no actual job ever materialized to make me into a professional Nim programmer that would actually pay the bills. I stuck it out with Python, with growing discontentment. I took a Perl job in 2018, which lasted until 2022, which I never should have taken in the first place. I was relatively free in my choice of language from that point forward, and decided to switch from Python to Lua after a short period of disorientation where I kind of liked Haxe. Right now, I'm learning Rust, crying tears over that future for Nim that never materialized.
by afandian | 2025-08-17 14:20:21+00:00
Depends how nitpicky you want to be. There was an ARM “BBC Micro”.

https://www.retro-kit.co.uk/page.cfm/content/Acorn-BBC-Archi...

And the IBM PC used an 8088.

https://en.m.wikipedia.org/wiki/IBM_Personal_Computer
by pama | 2025-08-17 14:20:40+00:00
IQ of 200 (or higher) do not exist according to the original definitions of this metric. You need a population of 219 billion or higher to have a 95% chance that a sample exists with 6.66 standard deviations away from the mean (assuming mean of 100 and std of 15). Ofc the tests are of limited value and things can be gamed, but it would be silly to try and identify samples that have no chance of existing.
by FrustratedMonky | 2025-08-17 14:20:46+00:00
That critique is half-right: large language models don’t “understand” in the human sense, but they do apply learned patterns across vast data in ways that often look like knowledge, even if it’s statistical pattern-matching. The real frontier is that these statistical engines can already combine rules, constraints, and creativity in ways their critics dismiss too quickly—making the line between “mere prediction” and “applied knowledge” fuzzier than it seems.
by thomasahle | 2025-08-17 14:21:00+00:00
> > Doing work that matters might.

> This is a pre-requisite for winning on LinkedIn. The kind of content that performs best are strong opinions informed by actual expertise.

> > Go for depth over frequency.

> Unfortunately that's not the way marketing works. 95% of your audience is not 'in-market' and ready to buy when they see your content. Sometime over the next 3-5 years they may move into a buying lifecycle, and they are much more likely to trust you, and therefore buy from you, if they've seen your content 1,000x vs a couple of long reads.

Don't you think there's a contradiction or trade-off here?

If you've written about your content 1,000x, you could have spent that time on doing more "work that matters".

Perhaps the "practical impact" is something like `quality-of-work * times-you-share-it`, but let's not pretend optimizing one doesn't take time away from the other.
Show HN: Chatbang – Access ChatGPT from the terminal without an API key
by ahmedhosssam | 2025-08-17 14:21:08+00:00
How does it work? Chatbang works by scraping ChatGPT's website. It opens ChatGPT on a headless session when you execute Chatbang and take your prompt and paste it in ChatGPT's prompt area and execute that prompt, then it waits for the response and copies it as a markdown format and then render it in the terminal.
by pm215 | 2025-08-17 14:21:08+00:00
(I work on a project that uses an email workflow. "Should we switch to something else?" is a perennial topic.)

I think the main pain points are:

* unfamiliarity -- if you're not already set up then it's extra admin work. And the "obvious" approach (paste the patch into your usual email client and hit send) generally mangles the patch.

* developers are no longer largely using local plain text based email clients -- if you are already doing email that way then sending patches like that is a natural extension. If your email client is gmail, or worse still some Microsoft mail server, now you have to figure out how to send email in a way that's not html and doesn't get mangled by your mail server.

* how to set it up is not a single well documented path. Github has an incentive to do a decent tutorial/docs, and how to create an account and send a patch is the same for everyone. Using git send-email, there's a lot more variability: how to install it depends on your OS, and how to configure it to talk to your mail server depends on your mail server. In these days of two factor authentication and app specific passwords, it's no longer as easy as "put the server name and your account name and password into the config file".

This has got better in that now you can point people at e.g. https://git-send-email.io/ which has info on e.g. setting up gmail app specific passwords and what kind of authentication to use.

If you're a regular developer on a project, this is a one-time pain and thereafter sending patches is straightforward (assuming you're not totally allergic to the command line). But for one-off or first time contributions it can be a barrier.
by ericmcer | 2025-08-17 14:21:09+00:00
Worked with many great ones but the one who really stands out seemed levels above everyone in terms of just raw intelligence.

When you explain a concept to another human you have to provide context and a certain level of detail. It can be hard to calibrate based on who you talk to. What always blew me away about this guy was he needed like almost 0 context, even for non-programming subjects. He understood within seconds what you were doing, even for complex problems. I had to recalibrate my entire approach to explanation with him because he got things instantly.
by einrealist | 2025-08-17 14:21:09+00:00
A World Model is a theoretical type of model that has knowledge about the "real world" (or whatever world or bounds you define). It can infer causalities from concepts within this world.

Yes, you can use Genie 3 to train other models. Its far from perfect. You still need to train Genie 3. And its training and outputs must be useful in the context of what you want to train other models with. That's a paradox. The feedback loop needs to produce useful results. And Genie 3 can still hallucinate or produce implausible responses. Symbolism is a wide term. But a "World Model" needs it to make sense between concepts (e.g. Ontologies or the relation of movement and gravity).
by A4ET8a8uTh0_v2 | 2025-08-17 14:21:31+00:00
It is not controversial at all. It was deemed inappropriate due the amount of 'wrongthink' it causes. We can argue about what followed and whether its claims have been nullified, but given how much conversations started with it, I sincerely doubt the argument that it should not be the introduction is reasonable. In a sense, it is the source of the debate.
by nickpinkston | 2025-08-17 14:21:32+00:00
Re: "succumb to the tendency to rest on their laurels the moment they start making money"

I don't find that true of any of the companies I mentioned at least. They all were/are going hard on development, but the tech problems were just really hard compared to funding, and they never made any major breakthroughs on capability.

Chinese companies have been great at going from 1 to n in the 3D printing space with a lot of cost, reliability, etc. refinements, but I haven't seen a transformative technology from them yet either (re: comment on ~1995 SolidWorks being the last one).
by Spivak | 2025-08-17 14:21:43+00:00
I, and everyone else, are doing illegal things all the time. As you point out, that's the nature of our legal system. So it's not terribly persuasive to say, "well are you sure you're not doing anything illegal" because it misunderstands the reason why we all haven't gotten arrested. It's not because of privacy but because almost all of us are below the notice of the police. You even cite an example of how privacy doesn't save you in exactly the cases where you think it ought to.

There are arguments for maintaining privacy but I don't really think this is one of them.
by LocalH | 2025-08-17 14:22:31+00:00
> A friend got a warning from reddit just yesterday for saying not that a certain dictator should be assassinated but that he should be sentenced to death and then executed by anyone in a position to do so.

A lot of social media today will sanction you if you say such things as "it's ok to punch a Nazi". But won't sanction a Nazi for saying things like "white replacement theory".
by technothrasher | 2025-08-17 14:23:03+00:00
> The way human IQ testing developed is that researchers noticed people who excel in one cognitive task tend to do well in others

My son took an IQ test and it wouldn't score him because he breaks this assumption. He was getting 98% in some tasks and 2% in others. The psychologist giving him the test said it was unlikely enough pattern that they couldn't get an IQ result for him. He's been diagnosed with non-verbal learning disability, and this is apparently common for nvld folks.
by brookst | 2025-08-17 14:23:19+00:00
Same thing we saw with game playing:

- It can play chess -> but not at a serious level

- It can beat most people -> but not grandmasters

- It can beat grandmasters -> but it can’t play go

…etc, etc

In a way I guess it’s good that there is always some reason the current version isn’t “really” impressive, as it drives innovation.

But as someone more interested in a holistic understanding of of the world than proving any particular point, it is frustrating to see the goalposts moved without even acknowledging how much work and progress were involved in meeting the goalposts at their previous location.
by joshwa | 2025-08-17 14:23:51+00:00
https://onion-cutting-simulator.streamlit.app/

I made my own version of this a while back, and it lets you create your own cutting methods, plot the statistical distribution, and share your ideas via permalink. It also lets you tweak onion parameters, such as number of layers and the layer thickness distribution curve).

Along the way I discovered two things:

1. I came up with my own method ("Josh’s method" in the app above) where the neither the longitudinal cuts nor the planar cuts are full depth, so the number of cuts at the narrower core is less than at the wider perimeter.

2. After all this hyper-optimization about size, it turns out what really matters when cooking is the THICKNESS, since ultimately determines the cooking rate. The only way to avoid thin outliers that burn long before the rest are cooked is to discard more of the tip of the onion, where the layers are the thinnest.

The 3D version of the simulator is still in progress--turns out 3D geometry is a lot harder than 2D. :)

Pull requests are welcome! https://github.com/joshwand/onion-simulator
by apwheele | 2025-08-17 14:24:34+00:00
I don't disagree with the fundamentals of the post (most stuff is vapid, not just on LinkedIn but other platforms as well). But I still personally have benefitted the most from LinkedIn for grass roots for my business most recently, https://andrewpwheeler.com/2025/02/20/linkedin-is-the-best-s.... It is higher quality referrals than I ever got from X for example.
by latexr | 2025-08-17 14:24:37+00:00
I wish that “it will get better” wasn’t the response every time someone shares actionable advice and thoughtful specific criticism about the state of the art.

You don’t know if it will get better. Even if it does, you don’t know by how much or the time frame. You don’t know if it will ever improve enough to overcome the current limitations. You don’t know if it will take years.

In the meantime, while someone is sitting on their ass for years waiting for the uncertain future of the tool getting better, someone else is getting their hands dirty, learning the craft, improving, having fun, collaborating, creating.

There is plenty of garbage out there where we were promised “it will only get better”, “in five years (eternally five years away) it will take over the world”, and now they’re dead. Where’s the metaverse NFT web3 future? Thrown into a trash can and lit on fire, replaced by the next embarrassment of chatting with porn versions of your step mom.

https://old.reddit.com/r/singularity/comments/1mrygl4/this_i...
by FrustratedMonky | 2025-08-17 14:24:46+00:00
Right now LLMs are like students that study for years, then get their brains frozen into a textbook before they’re released. They can read new stuff during use (context window), but they don’t actually update their core weights on the fly. The “infinite context window” dream would mean every interaction is remembered and folded back into the brain, seamlessly blending inference (using the model) with training (reshaping it).

Within 2–3 years, we’ll see practical “personal LLMs” with effectively infinite memory via retrieval + lightweight updates, feeling continuous but not actually rewriting the core brain.

Within 5–10 years, we’ll likely get true continual-learning systems that can safely update weights live, with mechanisms to prune bad habits and compress knowledge—closer to how a human learns daily.

The rub is less can we and more should we: infinite memory + unfiltered feedback loops risks building a paranoid mirror that learns every user’s quirks, errors, and biases as gospel. In other words, your personal live-updating LLM might become your eccentric twin.