MurphyA/hacker-news huggingface dataset:
by c1ccccc1 | 2025-07-19 18:42:51+00:00
Would you also get triggered if you saw people make a bet at, say, $24 : $87 odds? Would you shout: "No! That's too precise, you should bet $20 : $90!"? For that matter, should all prices in the stock market be multiples of $1, (since, after all, fluctuations of greater than $1 are very common)?

If the variance (uncertainty) in a number is large, correct thing to do is to just also report the variance, not to round the mean to a whole number.

Also, in log odds, the difference between 5% and 10% is about the same as the difference between 40% and 60%. So using an intermediate value like 8% is less crazy than you'd think.

People writing comments in their own little forum where they happen not to use sig-figs to communicate uncertainty is probably not a sinister attempt to convince "everyone" that their predictions are somehow scientific. For one thing, I doubt most people are dumb enough to be convinced by that, even if it were the goal. For another, the expected audience for these comments was not "everyone", it was specifically people who are likely to interpret those probabilities in a Bayesian way (i.e. as subjective probabilities).
by nickpsecurity | 2025-07-19 18:43:00+00:00
I'll add something I read in books on human intuition when I was younger. The authors pointed out that reasoning and intuitive parts of the brain are different. They can work together or override each other situation by situation.

Reasoning can establish the facts, analyze them, generalize/analogize, weigh possible outcomes, and even backtrack. Memories of successes and failures might be considered with all the techniques I described available for them. It takes lots of time and energy, though.

Intuition finds patterns in what our senses observe and how we respond to it. It tries to approximate a good enough reaction. Over time, it tries to do that by default unless we consciously override it. We can train it with conscious practice.

The authors proposed this was for efficency and survival. For efficiency, most of our tasks are repetitive in various ways. Using quick shortcuts saves time and energy. For survival, we seem to more vividly remember horrible things that can hurt us, our experiences or others' stories. Intuition's fast response, milliseconds, might save our life from a threat that would hurt us if we took time to analyze it.

We also have memory that connects to both components. We have multiple layers of memory. I'm not sure how often our reasoning and intuitive components consult with our memory vs use their own internal state. I imagine God gave the brain heuristics on that.

There's also one part of the brain that's damaged in people who hallucinate a lot. It might be designed to mitigate hallucinations. I speculate it is a combination of it and memory work together for this.

Finally, incoming data begins grounded in the senses that see our actual reality. What humans tell us is integrated with that. We also constantly generate our own predictions, especially as children play, which are tested in the real world.

There's also continuous training with different, reward mechanisms. There's also changes to learning rates balancing adaptability vs stability. Whatever this is can work without fine tuning (human feedback) but works much better with it.

So, whatever architecture the AGI (or scientist replacement) will need these components. Minimum: a goal-oriented, reasoning system; intuitive system; memory; hallucination mitigation. We can use the first model like that to help us build the rest.
by com2kid | 2025-07-19 18:44:48+00:00
I'd argue that many CLI tools output too much log spew by default and rely on making humans take up the burden of parsing through masses of output to find the one useful line.

For another example of where this is a problem, look at any large company that pays to keep logs in kibana, the amount of over logging paid for is insane.

Approximately 1/3rd of my Claude code tokens are spent parsing CLI output, that is insane! Often Claude doesn't catch what it needs in the massive log outputs and I have to go through the logs myself to find the problem and point it out to Claude.
by Al-Khwarizmi | 2025-07-19 18:44:53+00:00
Maybe yes, it's too much?

I'm a non-native English speaker who writes many work emails in English. My English is quite good, but still, it takes me longer to write email in English because it's not as natural. Sometimes I spend a few minutes wondering if I'm getting the tone right or maybe being too pushy, if I should add some formality or it would sound forced, etc., while in my native language these things are automatic. Why shouldn't I use an LLM to save those extra minutes (as long as I check the output before sending it)?

And being non-native with a good English level is nothing compared to people who might have autism, etc.
by lgvln | 2025-07-19 18:44:58+00:00
Singapore is an interesting case because it looks great on paper but it’s arguably one of the worst/best form of authoritarianism. For example, ~80% of Singaporeans live in public housing, which they don’t technically own (99 year lease), whose rules are dictated by a government agency. Eg, there are police cameras right up to the apartment lifts and every entry/exit; the number of pets they’re allowed to own is regulated and interestingly less than those living in private property arbitrarily (without consideration for property size). Nevermind the complete lack of civil liberties and high gini coefficient/inequality.
by duncanfwalker | 2025-07-19 18:46:54+00:00
The --no-verify example is interesting because I can imagine the same hint being useful for junior engineers. In general it's hard to give the the right level of advice in cli docs because you don't always know who the consumer will be and so what knowledge can be assumed. The think that makes LLMs different is that there's no problem to being verbose in the docs because you're not wasting any human's time. It would be cool if you could docs that provide extra advice like in the example and then the interface adapted for the users context - for LLMs provide everything, for human users learn what they know give them just the right level of advice
by bwfan123 | 2025-07-19 18:47:00+00:00
I fed the problem 1 solution into gemini and asked if it was generated by a human or llm. It said:

Conclusion: It is overwhelmingly likely that this document was generated by a human.

----

Self-Correction/Refinement and Explicit Goals:

"Exactly forbidden directions. Good." - This self-affirmation is very human.

"Need contradiction for n>=4." - Clearly stating the goal of a sub-proof.

"So far." - A common human colloquialism in working through a problem.

"Exactly lemma. Good." - Another self-affirmation.

"So main task now: compute K_3. And also show 0,1,3 achievable all n. Then done." - This is a meta-level summary of the remaining work, typical of human problem-solving "

----
Show HN: An Intuitive Go Library for Handling Complex Permission/RBAC
by LimitlessDonald | 2025-07-19 18:47:01+00:00
Permitta is an intuitive go library, which aims to help handle any kind of permission/access control in a simple and easy to understand way, even for beginners .

The fact that its intuitive doesn't take away how powerful it is to handle very complex and frequently used permission scenarios in different types of projects .

Why Almost everything we do in computing these days needs permissions . There are hundreds of permission/access control systems and methods, many of them are not intuitive .

I wanted to create a system that would cover most popular use cases, where you can get started in minutes, whether you are a novice or very experienced programmer, yet still powerful enough to handle complex permissions .

I took inspiration from the linux permission system e.g rwxr--r-x, but took it some steps further and made it more intuitive

I wanted to be able to handle permissions/access control in a SaaS (or any application), with multiple access levels and users

I wanted to be able to control access for actions including Create, Read, Update, Delete, Execute (remember CRUDE - more on this later), I wanted to be able to control how much of each of those actions can be carried out by each user, and how frequently within a specific period of time they can carry out those actions .

I wanted to create a permission/access control system with no dependencies, except the go standard library

I wanted to enjoy writing and reading permissions/access control

I wanted to be able to write extremely complex permissions/access control for a user/org/entity on a single line

I did not want to have to write complex DB queries to verify permission and resource usage, which can get very tedious as an application gets more complex.

Features Ability to set create, read, update , delete, execute (CRUDE) operations permissions Ability to control start and end time for permissions Ability to set quota limit (Quota is how many of a certain resource can exist at any given time) Ability to set batch limit Ability to set time based limits (all time , per minute, per hour, per day, per week, per fortnight, per month, per quarter, per year, custom time duration [in progress] ) Ability to verify permission against usage (you would need to store usage in your preferred DB ) Ability to verify permissions based on entity i.e (user, role, group, domain, organisation) Ability to set entity permission order (the flow/order in which the permission should be checked e.g org->domain->group->role->user)

https://github.com/LimitlessDonald/Permitta
by hollerith | 2025-07-19 18:47:41+00:00
If some bright young person (or more realistically some sequence of bright young people building on each other's work) were to devise a plausible method to control an AI such that it stays under human control even if it become much more cognitively capable than the most capable people, then I would be OK with going ahead with AI research.

I don't think most readers here realize just how little control the AI labs have over their creations and how reliant they are on trial and error for implementing what control they do have. Of course, as soon as it becomes critical to keep an AI under control (namely, when its capabilities start to exceed human capabilities) is exactly when a lab will stop being able to rely on trial and error: specifically, the next time the lab makes an unsuccessful try, the AI will tend to arrange things so that the lab doesn't get any more tries.
by 0x000xca0xfe | 2025-07-19 18:47:42+00:00
Tens of thousands die falling on stairs every year. You don't have to fall out of the sky to die. Just last year a good friend of mine had to bury her sister because she fell while exiting her car. You are more likely to survive than in small aviation crashes but most people (probably Felix included) spend way more time on that than flying. As a motorcyclist I'm surely not well of either when I suddenly fall unconscious.

There are many activities where you really should not pass out while doing them, but they don't make for interesting headlines.
by xyst | 2025-07-19 18:47:50+00:00
It’s a shame he doesn’t self host an internal mail server at least with restricted outbound/smtp.

Something like this is very easy to setup with projects such as stalwart which also offers CardDAV and CardDAV (think easy synchronization of calendar and contacts without relying on "cloud").

He already has tailscale + headscale, adding in an internal only mail/collaboration server would be a win.
by jononor | 2025-07-19 18:48:12+00:00
A) We are out of the Internet-scale-for-free data. Of course the companies deploying LLM based systems at massive scale are of course ingesting a lot of human data from their users, that they are seeking to use to further improve their models.

B) Has learning though "self-play" (like with AlphaZero etc) been demonstrated working for improving LLMs? What is the latest key research on this?
by kazinator | 2025-07-19 18:48:37+00:00
Looking at the monitor circuit with the two transistors, it's odd how the emitter is just connected to ground through only 122 ohms. Why would you have that 100 ohm resistor there?

Indeed, I found other versions of the circuit which don't have that:

https://www.cocopedia.com/wiki/index.php/CoCo_1_composite_vi...
by fragmede | 2025-07-19 18:48:55+00:00
Ah yes, that "is that 6 spaces or 8" in a yaml file itch that just has to be scratched. Programming has a lot of doldrums. LLMs still get stuck at places, and that's just where the new itch to scratch is. Yeah, it's not the same as code golfing an algorithm really neatly into a few lines of really expressive C++, but things change and life goes on. Programming isn't the same as when it was on punch cards either.
by atleastoptimal | 2025-07-19 18:50:38+00:00
Lol the OpenAI naysayers on this site are such conspiracy theorists.

There are many things that are hard for AI’s for the same reason they’re hard for humans. There are subtleties in complexity that make challenging things universal.

Obviously the model was trained on human data so its competencies lie in what other humans have provided input for over the years in mathematics, but that isn’t data contamination, that’s how all humans learn. This model, like the contestants, never saw the questions before.
by YZF | 2025-07-19 18:51:00+00:00
The entire point might be to offload the blocking call and do something else while it's blocking.

There's a style of "asynchronous programming" where everything is designed to be non-blocking and there can be asynchronous programming with blocking code. In fact the first style can be emulated by offloading every blocking call to a different thread/greenthread/fiber and that's basically what's happening under the hood unless there is some fundamental support for non-blocking at the lower levels (sometimes all the way down to the hardware).
by karmasimida | 2025-07-19 18:51:22+00:00
> if OpenAI ran this 10000 times in parallel and cherry-picked the best one

This is almost certainly the case, remember the initial o3 ARC benchmark? I could add this is probably multi-agent system as well, so the context length restriction can be bypassed.

Overall, AI good at math problems doesn't make news to me. It is already better than 99.99% of humans, now it is better than 99.999% of us. So ... ?
by c1ccccc1 | 2025-07-19 18:51:42+00:00
You should basically assume they are pulled from thin air. (Or more precisely, from the brain and world model of the people making the prediction.)

The point of giving such estimates is mostly an exercise in getting better at understanding the world, and a way to keep yourself honest by making predictions in advance. If someone else consistently gives higher probabilities to events that ended up happening than you did, then that's an indication that there's space for you to improve your prediction ability. (The quantitative way to compare these things is to see who has lower log loss [1].)

[1] https://en.wikipedia.org/wiki/Cross-entropy
by jacobr1 | 2025-07-19 18:51:45+00:00
Yeah, this example isn't great - you can just tell the llm to run pwd more frequently or something.

But for the `$command | head -100` example, the usage is a bit different. I run into this myself on the cli, and often ended up using `less` in similar context.

Two cases

1) sometimes I use head to short circuit a long running, but streaming output, command so I just assess if it is starting to do the right thing but not bear the time/computational cost of full processing

2) sometimes the timing doesn't matter but the content is too verbose, need to see some subset of the data. But here head is too limited. I need something like wc & head and maybe grep in one command line with context. Maybe something like

$command | contextual-filter -grepn 5 -grep error -head 10

some data ... first the first 10 lines ... an error message with 5 lines of context surrounding before and after

Summary: 100000 total lines 15 printed exited with code 0

You can do all that already with grep and others, but you need to run multiple commands to get all the context
by danlitt | 2025-07-19 18:53:34+00:00
> If the variance (uncertainty) in a number is large, correct thing to do is to just also report the variance

I really wonder what you mean by this. If I put my finger in the air and estimate the emergence of AGI as 13%, how do I get at the variance of that estimate? At face value, it is a number, not a random variable, and does not have a variance. If you instead view it as a "random sample" from the population of possible estimates I might have made, it does not seem well defined at all.
Show HN: Transform passive YouTube watching into active learning
by AzharKhann | 2025-07-19 18:53:44+00:00
I've been self-learning from YouTube for years—everything from coding to design to business skills. But I kept hitting the same wall: YouTube learning has no structure. Your knowledge gets scattered across random playlists, you're passively consuming content without real retention, and when you're confused, there's nobody to ask.

I built Notetube to fix this by layering organizational tools with AI to create a proper learning system:

Organizational layer: Build structured collections by topic/course/skill, visualize your learning progress with dashboards, create and track your learning goals

AI layer: Automatically generates detailed notes (3000+ words for 1 hour of content) and summaries, identifies key moments with timestamps, creates personalized quizzes for retention testing, and provides a chat interface for instant help when concepts aren't clear

...plus additional features like timestamped note-taking, but I'll keep this brief.

Quick signup via Google OAuth for a smooth onboarding experience.

Try it free: https://www.notetubeai.com/

Would love your thoughts and feedback from the HN community!
by archon1410 | 2025-07-19 18:54:02+00:00
Yes, and there's also languages of ex-USSR countries, whose competitors presumably all understand Russian, and so on.

The real reason might be that there's an enormous class of self-loathing elites in India who actively despise the possibility of any Indian language being represented in higher education. This obviously stunts the possibility of them being used in international competitions.
by atleastoptimal | 2025-07-19 18:54:06+00:00
>AI model performs astounding feat everyone claimed was impossible or won’t be achieved for a while

>Commenters on HN claim it must not be that hard, or OpenAI is lying, or cheated. Anything but admit that it is impressive

Every time on this site lol. A lot of people here have an emotional aversion to accepting AI progress. They’re deep in the bargaining/anger/denial phase.
by nielsbot | 2025-07-19 18:54:12+00:00
Let me clarify.

I’m saying Democrats are ineffective at doing good and are therefore part of the problem.

If the Democrats do not effectively wield power to solve people’s real world (economic) problems the country will slide further to the right.

I obviously meant wealthy donors and corporations (the ownership class) — the minority or entities that the Democrats are beholden to as opposed to the bulk of their voters.
by tedsanders | 2025-07-19 18:55:05+00:00
Not to beat a dead horse or get into a debate, but to hopefully clarify the record:

- OpenAI denied training on FrontierMath, FrontierMath-derived data, or data targeting FrontierMath specifically

- The training data for o3 was frozen before OpenAI even downloaded FrontierMath

- The final o3 model was selected before OpenAI looked at o3's FrontierMath results

Primary source: https://x.com/__nmca__/status/1882563755806281986

You can of course accuse OpenAI of lying or being fraudulent, and if that's how you feel there's probably not much I can say to change your mind. One piece of evidence against this is that the primary source linked above no longer works at OpenAI, and hasn't chosen to blow the whistle on the supposed fraud. I work at OpenAI myself, training reasoning models and running evals, and I can vouch that I have no knowledge or hint of any cheating; if I did, I'd probably quit on the spot and absolutely wouldn't be writing this comment.

Totally fine not to take every company's word at face value, but imo this would be a weird conspiracy for OpenAI, with very high costs on reputation and morale.
by briantakita | 2025-07-19 18:55:16+00:00
Yet we have Microplastics, PFAS, & a slew of other dangerous contamination across the planet. And the military industrial waste is rarely mentioned.

It seems the EPA cares more about enforcing CO2 production & making sure a homesteaders doesn't build a pond...than it does about extremely harmful & destructive chemicals dispersed across the planet by industrial & military waste.

So I suppose the research is good but the emphasis & enforcement is what really matters. And while there have been historical wins, the agency seems increasingly like a political revolving door to entrench industrial incumbants.
by zbentley | 2025-07-19 18:55:51+00:00
An easy solve for this is to buy public domains for the sites you want to use, run a static website on them that says “turn on tailscale to access this site, set that up here (link to download a client preconfigured for my tailnet, invite only of course)”, then use tailscale DNS overrides to set up CNAMEs for that public DNS’s (sub)domains which point to the tailnet internal service domains.
by codingdave | 2025-07-19 18:55:53+00:00
Sounds like you are remembering the book "Thinking Fast and Slow". It is definitely an interesting model, but less than half the research on which it is based has been successfully replicated.

Besides, TFA wasn't trying to figure out how to architect AGI. They were just testing if LLMs were a potential basis for it. And while I just read the article, not the underlying study, it seems like their conclusion is "No."
by seanhunter | 2025-07-19 18:56:06+00:00
Hawking explained the origin of Hawking radiation. That’s kind of why it’s called Hawking radiation. His explanation includes why it comes out of a black hole.

The rest of it is, respectfully, “not even wrong”, so it wouldn’t really benefit either of us for me to try to engage further. Thanks for your response though. If you actually want to develop these ideas I would strongly encourage you to do some actual work to understand first classical mechanics and then the standard model. For example, if you read Kolenkow and Kleppner and do the exercises, you will realise the flaws in what you have written about oscillation and interference. Oscillation and the phenomenon of resonance really doesn’t work anything like you have written.
by LorenPechtel | 2025-07-19 18:57:36+00:00
The problem comes from "legal transactions".

The Pornhub problem came from going after the payment processors for facilitating supposedly illegal transactions--namely, underage porn. The crusaders (in every direction) keep looking for ways to undermine the protections (Section 230 in this case) and all too often the government doesn't fight back.

As for keeping it in the family games--we still have "obscenity" on the books and such games fall afoul of it. I find the concept of "obscenity" bonkers amongst consenting adults.