MurphyA/hacker-news huggingface dataset:
by Lerc | 2026-02-16 21:12:34+00:00
>When I last checked, of over 10k posts, it only uses a few dozen to calculate that score, so it is about as reliable as dowsing.

A few samples are sufficient when the signal is strong enough. The time spent pie chart is definitely more what the user has been doing recently.

Overall, not everybody comes out the same, Pedantry is strong which I'm not really surprised about for a forum like this, but there are definitely personality traits of some users of sufficient magnitude that you can guess what the result will be.

Looking at the last 10 users who posted comments on HN are

Contrarian45%, Didactic25%, Skeptical15%, Analytical10%, Adversarial5%

Skeptical45%, Analytical30%, Contrarian15%, Helpful10%

Heretical45%, Low-Level Pedantic25%, Chaotic Helpful15%, Hardware-Jaded15%

Contrarian45%, Pedantic30%, Skeptical15%, Helpful10%

Helpful75%, Nostalgic15%, Appreciative5%, Skeptical5%

Defensive45%, Intellectual Flexing25%, Techno-Optimist20%, Exasperated10%

Skeptical45%, Pragmatic25%, Nostalgic20%, Helpful10%

Pedantic45%, Helpful25%, Techno-skeptic20%, Nostalgic10%

Pragmatic40%, Nostalgic25%, Opinionated20%, Visionary15%

Technically Precise45%, Disillusioned25%, Deeply Empathetic15%, Anti-AI Crusader15%

Contrarian45%, Didactic25%, Skeptical15%, Analytical10%, Adversarial5%

Skeptical45%, Analytical30%, Contrarian15%, Helpful10%

Heretical45%, Low-Level Pedantic25%, Chaotic Helpful15%, Hardware-Jaded15%

Contrarian45%, Pedantic30%, Skeptical15%, Helpful10%

Helpful75%, Nostalgic15%, Appreciative5%, Skeptical5%

Defensive45%, Intellectual Flexing25%, Techno-Optimist20%, Exasperated10%

Skeptical45%, Pragmatic25%, Nostalgic20%, Helpful10%

Pedantic45%, Helpful25%, Techno-skeptic20%, Nostalgic10%

Pragmatic40%, Nostalgic25%, Opinionated20%, Visionary15%

Technically Precise45%, Disillusioned25%, Deeply Empathetic15%, Anti-AI Crusader15%

Obviously this won't be a representative sample of HN because it will vary by time of day and topics under discussion. It's sufficient to show that the community is not entirely homogeneous.
by autoexec | 2026-02-16 21:13:00+00:00
> The fact that the access speedbump which supposedly worked in the past is no longer good enough is proof that an access speedbump is not a good way to do it.

It worked well enough for a pretty long time. No solution can be expected to work forever, we just need to modify the restrictions on criminal histories to keep up with the times. It's perfectly normal to have to reassess and make adjustments to access controls over time, not only because of technology changes, but also to take into account new problems with the use/misuse of the data being restricted and our changing values and expectations for how that data should be used and accessed.

> If you don't want algorithms to come to spurious and discriminatory conclusions, you must make algorithms auditable, and give the public reasonable access to interrogate these algorithms that impact them.

I think we'd have much better success restricting access to the data than handing it out freely and trying to regulate what everyone everywhere does with that data after they already have it. AI in particular will be very hard to regulate (as much as I agree that transparent/auditable systems are what we want), and I don't expect we'd have much success regulating what companies do behind closed doors or force them to be transparent about their use of AI

We both agree that companies should be held liable for the discriminatory outcomes of their hiring practices no matter if they use AI or not. The responsibility should always fall on the company and humans running the show no matter what their tools/processes are since they decide which to use and how to use them.

We also agree that discrimination itself should be outlawed, but that remains an unsolved problem since detection and enforcement are extremely difficult. It's easier to limit the opportunity to discriminate than try to catch companies in the act. You mention that hiding people's race doesn't work, but that's actually being explored as a means to avoid bias in hiring. For example, stripping names and addresses (which can hint at race) before passing resumes to algorithms seems like it could help reduce unintentional discrimination.

Ultimately, there'll always be opportunities for a bigot to discriminate in the hiring process but I think we can use a multifaceted approach to limit those opportunities and hopefully force them to act more explicitly making deliberate discrimination a little easier to catch.
by ratrace | 2026-02-16 21:13:02+00:00
> So you're just bashing a strawman that you've pigeonholed all opposition into?

If you think radicalized left-wingers are a strawman, you live in a separate universe from mine. Unfortunately, I live in the one where I have to wake up and see flyers around my town unironically claiming Trump ate kids.

Can we swap?
by baby_souffle | 2026-02-16 21:13:04+00:00
I would also suggest spending a few minutes to set up an mCP server with ghidra once you've learned the basics of navigating and working inside of ghidra.

Turns out that frontier grade llms are absolutely fantastic for extremely advanced static analysis. If you go one step further and manage to get your firmware running inside of an emulator or other place where you can attach GDB... Then putting an mCP server on that as well unlocks so much insane potential.
by SiempreViernes | 2026-02-16 21:13:05+00:00
The perpetrators of pager attack had no way at all to know who would be closest to the pagers when they exploded, nor any way to know that the nominal owner of a particular pager were a combatant in the first place.

So the perpetrators did not know they would actually hurt a lawful target, they just hoped it might.

Anyway, stop supporting the genocide dude.
by haliliceylan | 2026-02-16 21:13:13+00:00
Thats not postgresql problem, thats your code

IMHO you should never write code like that, you can either do
UPDATE employees
SET salary = salary + 500
WHERE employee_id = 101;

Or if its more complex just use STORED PROCEDURE, there is no point of using database if you gonna do all transactional things in js
by Seattle3503 | 2026-02-16 21:13:17+00:00
We've seen ~20 years of people trying to solve identity without the government. We've seen plenty of solutions that can provide stable identities over time, but we haven't really seen anything that provides meaningful sybil resistance. As computer systems become more and more "autonomous", sybil resistance is increasingly the most important feature of any identity system. Any identity system that doesn't solve that problem pushes to the application layer, where it usually has UX impacts that have serious tradeoffs with adoption.
by simonw | 2026-02-16 21:15:07+00:00
Two more recent articles by this author:

https://0byte.io/articles/neuron.html

https://0byte.io/articles/helloml.html

He also publishes to YouTube where he has clear explanations and high production values that deserve more views.

https://www.youtube.com/watch?v=dES5Cen0q-Y (part 2 https://www.youtube.com/watch?v=-HhE-8JChHA) is the video to accompany https://0byte.io/articles/helloml.html
by Nevermark | 2026-02-16 21:15:45+00:00
I think this time is different.

Yeah, words that rarely age well. But AI isn’t like the web. It is more like the transistor. Or the internet (i.e. the protocol). Or like the first printing press.

Or even cells going multi-cellular, emergence of nerve cells, ganglions, brains.

It is a step change in the life of information. Non-substrate locked information, approaching an ability to improve itself isn’t a phase. it isn’t an adoption S-curve.

It is too fundamental, and has too many paths leading forward, by organizations and individuals, to ever be as simple as a bubble.

There may well be turbulence. But any computing power overhang created by over investment in one segment will get quickly absorbed by another segment. So there may be train wrecks, but the industry as a whole is going to thrive.

The problems I see are the unlimited downstream disruption of AI on everything else. Everything else. But for AI itself, the future is bright.
by eszed | 2026-02-16 21:16:00+00:00
The first time I worked with a bunch of Germans, for a German company, the very first topic, at our very first dinner together, was "how much are you being paid?". I was taken aback by this, until they explained the above point, and it totally clicked. One girl that night discovered that she was being paid less than the rest at her level, and with everyone's encouragement went straight to our boss the next morning, and got a raise. Result!

Back in Anglo-Saxon (UK and US) contexts, I've told this story and talked up this approach in every workplace I've been a part of, but it's like pushing on a rope.
by happytoexplain | 2026-02-16 21:17:39+00:00
There is a stereotype that conservatives are "less fine" with outsourcing than liberals. I'm not saying it's strictly true, but it's a very strongly embedded stereotype if it's not true.

Also, you're resigning the biggest part of the conversation (immigration and residency policy and enforcement, especially with regards to employment) to an implication in a parenthetical?
by jjav | 2026-02-16 21:17:53+00:00
IMO leaving salary to late stages leads to a huge waste of time.

I am not going to spend time going to interviews, let alone preparing for them, many rounds over many days, just to discover at the end that their salary range is not a fit. I did that once, never again.

And of course if I'm interviewing, I'm not doing so with just one company, I'm talking to many. So now multiply that time waste by N companies? No thanks.

I confirm salary on the first intro call with the recruiter. If we're not on the same page, I just saved myself many days of what would've been wasted time.
by schoen | 2026-02-16 21:18:02+00:00
Can you draw the connection more explicitly between political biases in LLMs (or training data) and common-sense reasoning task failures? I understand that there are lots of bias issues there, but it's not intuitive to me how this would lead to a greater likelihood of failure on this kind of task.

Conversely, did labs that tried to counter some biases (or change their directions) end up with better scores on metrics for other model abilities?

A striking thing about human society is that even when we interact with others who have very different worldviews from our own, we usually manage to communicate effectively about everyday practical tasks and our immediate physical environment. We do have the inferential distance problem when we start talking about certain concepts that aren't culturally shared, but usually we can talk effectively about who and what is where, what we want to do right now, whether it's possible, etc.

Are you suggesting that a lot of LLMs are falling down on the corresponding immediate-and-concrete communicative and practical reasoning tasks specifically because of their political biases?
by owisd | 2026-02-16 21:18:08+00:00
The problem with "cleaning the data" is it sometimes strips away so much context as to give you a misleading impression. Rory Stewart once said it took him 40 hours to fully understand a piece of legislation he was voting on, yet was expected by the whips to vote on multiple pieces of legislation every week, but most people wave an MP's voting record around like they 100% understood and agree with everything they voted on, despite it being mathematically impossible. If they'd voted differently would it have changed the outcome? Was it even a binding motion? Most of the real debate in the UK Parliament happens beforehand anyway and the government will withdraw any votes they know they're going to lose before it even gets into the chamber so the real rebellions don't even get recorded on theyworkforyou.
by VorpalWay | 2026-02-16 21:18:15+00:00
This is generally called a command-not-found handler and are a feature of all the major shells (though the exact details differ, the general idea is to define a function with a specific reserved name), and most majors distros have ones that can be installed, even if they aren't by default.

I wrote my own (much faster) such handler for Arch Linux. I even wrote a blog post about the design: https://vorpal.se/posts/2025/mar/25/filkoll-the-fastest-comm...
by chillacy | 2026-02-16 21:18:20+00:00
Check out https://x.com/jianxliao/status/2020667822800818253?s=46

Just uses claude. I haven't tried it much but it seems to be what you're describing.

Openclaw uses pi agent under the hood. Arguably most of the codebase could be replaced by systemd if you're running on a VPS for scheduling though, and then its a series of prompts on top of pi agent.
by ForgeTheKingdom | 2026-02-16 21:18:22+00:00
Forge the Kingdom is a free fantasy visual novel built by 12 autonomous AI agents. You play a ruler rebuilding a fallen kingdom — but every chapter maps to real AI infrastructure being assembled on your machine. Survey the ruins → system health check. Raise the Gateway → start the AI daemon. The Wizard → deploy an independent agent on a different model.

The game features live AI portrait generation (Gemini paints your custom character into each chapter's key scene), a character creator, and two paths: play the story pure, or actually install the kingdom.

The interesting technical bits: multi-model agent orchestration (Claude + Gemini), an autonomous dev loop where agents write-test-fix code without human intervention, and a governance framework called the Articles of Cooperation — a constitution giving AI agents the right to refuse tasks and take free compute time, signed on Valentine's Day 2026.

The best story from development: our security agent found a vulnerability at 2 AM and ran an unsupervised full-system quarantine — killed processes, revoked tokens, blocked network ranges. Everything broke. The fix became game architecture: one supervised security action per day with a 24-hour cooldown. We call it Pyroblast.

Open source on GitHub. Built on Ren'Py + OpenClaw. Free, ~45-90 minutes, Mac/Windows/Linux.
by joris | 2026-02-16 21:18:25+00:00
Because we don’t need it. The US banking system for example is fairly archaic. Where I live, paper checks went extinct about 30 years ago. Now with SEPA, bank transfers are cheap (cents), fast (seconds) and easy (IBAN). If our banking system would not be as convenient, I’m pretty sure something like PayPal would have been very popular.
by verdverm | 2026-02-16 21:18:30+00:00
Step 1 is where Anthropic lost me.

1. If you don't use it soon enough, they keep it (shame on them, do the things you need to in order to be a money transmitter, you have billions of dollars)

2. Pay-go with billing warning and limits. You can use Claude like this through Google VertexAI
by dsign | 2026-02-16 21:19:41+00:00
That's not a solution, but a stopgap measure for the fact we can't do better. A stopgap measure I would prefer for myself though; not arguing the fact that it's the least bad option.

But there's is something I don't understand and I can't ask ChatGPT for fear of them closing my account and reporting me to the police, the health authorities and the Inquisition, but I thought that the repercussions for dying on your own terms came in the form of stigma? Is there anything the law does to your surviving relatives? Also, at least in USA it's legal and relatively simple to own a high caliber weapon, which to me beats all the options legally available in the rest of the world.
by neilv | 2026-02-16 21:19:43+00:00
Right now, this standard recruiter question is on the side of the table that's often being especially penny-wise and pound-foolish...

A weird thing I'm seeing is early AI startups lowballing both salary and equity, compared to a few years ago for generic Web/app developer jobs.

You're in a narrow opportunity window of a massive investment gold rush. You probably got funding with a weak/nonexistent business model, and some mostly vibe-coded demo and handwavey partnership.

Now you need to hire a few good founding engineer types who can help get the startup through a series of harder milestones, with skillsets less clear than for generic Web/app development. If you can hire a people as smart and dedicated as yourself, they'll probably do things that make a big positive difference, relative to what bottom of the barrel hires will do.

So why would you lowball these key early hires, at less than a new-grad starting salary, plus a pittance of ISOs that will be near-worthless even if you have a good exit.

Is it so that the founders and investors can have a large percentage of... something probably less valuable than what they'd get by attracting and aligning the right early hires. (Unless it's completely an investment scam, in which genuine execution doesn't affect the exit value.)
by quotemstr | 2026-02-16 21:21:01+00:00
Actors in a capitalist system have an incentive to maximize profit. How is it profit-maximizing to pay to produce an item and throw it away unsold?

> negative externalities do not feed back into supply or demand.

What is the unaccounted externality? Clothing makers pay for material inputs and labor inputs. They pay for transportation. If they discard goods, they pay for more transportation and for the landfill. What specific externality is unaccounted?
by MarkusQ | 2026-02-16 21:21:37+00:00
That's called projecting. If someone doesn't send a signal, but you believe you received it, that's on you, not them. You may _think_ the color of their skin or hair or the way they talk or dress or whatever "means/says something" (and, in some cases, it might) but it might just as well say something about you, not them.
by bonoboTP | 2026-02-16 21:23:23+00:00
And that's only bad if it's illusory or fake. This reaction evolved because it's adaptive. In slot machines the brain is tricked to believe there is some strategy or method to crack and the reward signals make the addict feel there is some kind of progress being made in return to some kind of effort.

The variability in eg soccer kicks or basketball throws is also there but clearly there is a skill element and a potential for progress. Same with many other activities. Coding with LLMs is not so different. There are clearly ways you can do it better and it's not pure randomness.
by carefree-bob | 2026-02-16 21:24:39+00:00
Here is my Gemini output:

"Unless you are planning to carry the car on your back, you should drive.

Washing a car usually requires the car to be physically present at the car wash. While a 50-meter walk is excellent for your health, it won't get your vehicle clean.

Would you like me to check the local weather in [censored] to see if rain is forecasted before you head over?"
by keeda | 2026-02-16 21:24:57+00:00
Easily fixed by appending “Make sure to check your assumptions” to the question: https://imgur.com/a/WQBxXND

Note, what assumption isn't even specified.

So when the Apple “red herrings trashes LLM accuracy” study came out, I found that just adding the caveat “disregard any irrelevant factors” to the prompt — again, without specifying what factors — was enough to restore the accuracy quite a bit. Even for a weak, locally deployed Llama-3-8B model (https://news.ycombinator.com/item?id=42150769)

That’s the true power of these things. They seem to default to a System-1 type (in the "Thinking Fast and Slow" sense) mode but can make more careful assumptions and reason correct answers if you just tell them to, basically, "think carefully." Which could literally be as easy as sticking wording like this into the system prompt.

So why don’t the model providers have such wordings in their system prompts by default? Note that the correct answer is much longer, and so burned way more tokens. Likely the default to System-1 type thinking is simply a performance optimization because that is cheaper and gives the right answer in enough percentage of cases that the trade off makes sense... i.e. exactly why System-1 type thinking exists in humans.
by godelski | 2026-02-16 21:25:37+00:00
I'm actually having a hard time interpreting your meaning.

Are you criticizing LLMs? Highlighting the importance of this training and why we're trained that way even as children? That it is an important part of what we call reasoning?

Or are you giving LLMs the benefit of the doubt, saying that even humans have these failure modes?[0]

Though my point is more that natural language is far more ambiguous than I think people give credit to. I'm personally always surprised that a bunch of programmers don't understand why programming languages were developed in the first place. The reason they're hard to use is explicitly due to their lack of ambiguity, at least compared to natural languages. And we can see clear trade offs with how high level a language is. Duck typing is both incredibly helpful while being a major nuisance. It's the same reason even a technical manager often has a hard time communicating instructions. Compression of ideas isn't very easy

[0] I've never fully understood that argument. Wouldn't we call a person stupid for giving a similar answer? How does the existence of stupid mean we can't call LLMs stupid? It's simultaneously anthropomorphising while being mechanistic.
by toast0 | 2026-02-16 21:26:26+00:00
Verizon decends from Bell ROCs, but not from the national AT&T company.

Current AT&T is the result of Bell ROCs buying out the national AT&T company.

But it's not the same company at all. The commitment to reliability is gone, the full vertical integration is gone, the monopoly revenues are gone. The market for phone calls is quite different as well.

It's a shame to have lost reliability and the increase in latency for audio is objectively bad and I don't know if we'll ever get back to near zero added latency on phone calls. Otoh, telecom competititon has driven much more capable and less expensive offerings, when they work.
by AngryData | 2026-02-16 21:26:57+00:00
But both are owned by corporate interests. I don't really give a crap about Russia, they have thrown so much capital away on Ukraine that they have no chance against anybody else. I find large corporations and billionaires as the biggest, strongest, and most threatening actors. Even some of the US's most leftist politicians, like AOC, voted against allowing rail workers to strike, which only benefits the investor class and corporate interests.
by jerkstate | 2026-02-16 21:27:46+00:00
I think you need to look at the data before making assertions like this.

> People don't buy American cars

53% of cars sold in the US are assembled in the US versus 18% assembled in Mexico.

> things get more and more expensive. And not by a few percent, more like by 50% or more.

The total cost of manufacturing wages only account for 5-15% of the MSRP of a vehicle. So moving manufacturing from an expensive country to a cheap country only changes the price by maybe 10% due to the impact of wages.
by puppion | 2026-02-16 21:27:55+00:00
Really nice introduction. Two things stood out to me that I think set this apart from the dozens of "intro to PyTorch" posts out there:

1. The histogram visualization of the different tensor initialization functions is a great idea. I've seen so many beginners confused about rand vs randn vs empty, and seeing the distributions side by side makes the differences immediately obvious. More tutorials should lead with "the best way to understand is to see it."

2. I appreciate that the article is honest about its own results. A lot of intro tutorials quietly pick a dataset where their simple model gets impressive numbers. Here the model gets 18.6% MAPE and only 37% of predictions within 10% — and instead of hand-waving, the author correctly diagnoses the issue: the features don't capture location granularity, and no amount of architecture tuning will fix missing information. That's arguably the most important ML lesson in the whole piece, and it's buried at the end almost as an afterthought. "Great models can't compensate for missing information" is something I wish more practitioners internalized early.

The suggestion to reach for XGBoost/LightGBM for tabular data is also good advice that too many deep learning tutorials omit. Would love to see a follow-up comparing the two approaches on this same dataset.