MurphyA/hacker-news huggingface dataset:
by fullstackchris | 2026-02-08 08:13:25+00:00
I mean his rage is somewhat warranted, there is a comment a few threads up of a guy asking what model comparable to Opus 4.6 can be run on 16 gb VRAM...

Supporters and haters alike, its getting pretty stupid out there.

For the millionth time, it seems learning basics and fundamentals of software engineering is more important than anything else.
by lefra | 2026-02-08 08:13:39+00:00
The first working transistor was centimeter-scale, now billions of them fit in that space.

The first useful internal combustion engines were room-sized, now they fit on a moped.

The truck-sized hole in your argument is talking about "the smallest test". First discoveries/demonstrations of interesting phenomenons don't typically happen at the smallest scale (why would they?).
by zozbot234 | 2026-02-08 08:14:31+00:00
There are local models with larger context, but the memory requirements explode pretty quickly so you need to lower parameter count or resort to heavy quantization. Some local inference platforms allow you to place the KV cache in system memory (while still otherwise using GPU). Then you can just use swap to allow for even very long contexts, but this slows inference down quite a bit. (The write load on KV cache is just appending a KV vector per inferred token, so it's quite compatible with swap. You won't be wearing out the underlying storage all that much.)
by parable | 2026-02-08 08:14:34+00:00
I'd edit my other reply to this comment but can't anymore.

Here are the columns from the CSV file I've seen being shared around on forums, including the "internal metadata". This mostly boils down to full name on file, email, Stripe customer ID, activity metrics, usernames, and phone numbers. Everything else is largely irrelevant.

id,name,email,email_confirmed,email_confirmation_token,stripe_platform_customer_id,is_global_admin,is_ghost,created_at,anonymous_id,email_bounce_count,photo_url,publisher_agreement_accepted_at,bio,updated_at,profile_set_up_at,tos_accepted_at,email_digest_at,has_passed_captcha,import_confirmation_required,post_notification_preference,reader_installed_at,activity_items_viewed_at,dismissed_ios_app_promo_at,email_notifications_last_resumed_at,previous_name,release_group,handle,phone,bank_payment_failures,is_globally_banned,session_version
by tvali | 2026-02-08 08:14:42+00:00
My logic meets hilbert spaces here: 3 general and a few specific solutions for DL and AI, including ML visualization: either zoom in, look it in higher space, or zoom out and simplify it's space to yours. https://github.com/tambetvali/LaegnaAIMLBasics - machine learning intro of me, and I got many readers/cloners from here and like 4 stars (typically you guy use 2 computers and do not read online; that's the free stats from github: AIExperiments repo of mine brings some code for GPT) for AIBasics: but I rather found out it was very GPT related by expressing perceptron, basically GPT model is an architecture on perceptron. I am advocating the use of ML here. So all my news in one post if you dare to read comments :)
by TheSpiceIsLife | 2026-02-08 08:15:05+00:00
Empathy isn’t universal.

I care less about poor people in poor countries in far away lands, and far away times, than I do my fellow citizens in my relatively wealthy country.

And my fellow citizens, especially the low income folk, are affected everyday by high energy costs. High energy costs result in higher costs of everything.

Whereas the effects of climate change, to the extent that they’re distinguishable from extreme weather events at all, are largely tolerated by even the poorest here in Australia.

High energy costs makes extreme weather events less tolerable.
by simianwords | 2026-02-08 08:15:16+00:00
> Now, all of these are totally irrelevant to your point other than 4 and 5.

No it is completely relevant.

I don't have empirical proof for 4 -> 5 but I assume you agree that there is meaningful difference between 1 -> 4?

Do you disagree that an agent that simply writes code and uses a linter tool + unit tests is meaningfully different from an LLM that uses those tools but also uses the end product as a human would?

In your previous example

> Well, it could go, 'this is stupid, X-Country is not a thing, this feature is not implemented correctly'.

...but, it's far more likely it'll go 'I tried this with X-Country: America, and X-Country: Ukraine and no X-Country header and the feature is working as expected'.

I could easily disprove this. But I can ask you what's the best way to disprove?

"Well, it could go, 'this is stupid, X-Country is not a thing, this feature is not implemented correctly'"

How this would work in end to end test is that it would send the X-Country header for those blocked countries and it verifies that the feature was not really blocked. Do you think the LLM can not handle this workflow? And that it would hallucinate even this simple thing?
by atoav | 2026-02-08 08:15:51+00:00
Hackers care about the truth. I don't think many here would consider the censorship of a US head of state being inherently pro-US (or the criticism of said censorship to be anti-US).

But feel free to elaborate why you feel wanting the US population to be able to see how their political leadership is perceived elsewhere is "anti-US" — cause I would describe it as the exact opposite.
by deaux | 2026-02-08 08:18:05+00:00
Musk said in 2019 that Epstein was “obviously a creep” and claimed that Epstein “tried repeatedly to get me to visit his island. I declined.”

Hmm..

> “Do you have any parties planned? I’ve been working to the edge of sanity this year and so, once my kids head home after Christmas, I really want to hit the party scene in St Barts or elsewhere and let loose. The invitation is much appreciated, but a peaceful island experience is the opposite of what I’m looking for,”

Hmm..

> “Will be in the BVI/St Bart’s area over the holidays. Is there a good time to visit?”

Hmm..

> “What day/night will be the wildest party on your island?”

Hmm..

> The emails between the two moguls come years after Epstein was convicted in 2008 of soliciting prostitution from a minor in Florida.

Yup, sounds like a "better reason" to me.
by doodlebugging | 2026-02-08 08:18:09+00:00
This is true but unfortunately for Apple I don't buy anything from the app store except for a minimal iCloud subscription for temporary photo storage. I am in the process of unwinding that subscription in favor of local storage and periodic sync. I haven't been diligent about syncing things in the past so I did buy a subscription for photo storage to avoid losing photos. I know that lots of people buy apps for all kinds of things. I'm not one of those people though.
by bpavuk | 2026-02-08 08:18:28+00:00
given that:

- AI is what gave Nvidia reason to finally work on GPU drivers (Linux is premier environment for AI/ML workloads)

- LLMs are more useful on Linux than on Windows (although manuals are still unbeatable)

- Windows users are sick of Copilot bullshit

...yeah, I almost agree. the best thing that happened to Linux is Valve consistently pouring a ton of resources into open-source Linux projects, but AI bubble firmly holds the second place position.
Show HN: brew changelog – find upstream changelogs for Homebrew packages
by kolpaque | 2026-02-08 08:19:10+00:00
I often wanted to see what changed in a Homebrew package — but changelogs are usually buried somewhere in the upstream repo.

So I made `brew changelog`. It parses the formula or cask, looks at the upstream repo, and tries to locate changelog-like files: CHANGELOG, NEWS, HISTORY, etc. Then it either prints the changelog to terminal or opens it in your browser.

brew tap pavel-voronin/changelog

brew changelog node -o

You can tweak behavior with options like --pattern, --print-url, or --allow-missing (try --help)

Feedback or contributions welcome!
by fullstackchris | 2026-02-08 08:19:18+00:00
> Using LLM agents is expensive: if they give you already 50% more productivity, and your salary is an average salary, they are not. And LLMs will only get cheaper. They are only expensive in absolute, not in relative terms.

critical distinction: unless your getting paid comparable to your ouput (literally 0 traditional 9-5 software jobs I know unfortunately) this is infact the opposite - a subscription to any of these services reduces your overall salary, it doesnt make it higher...

then there is the case i know the dishonest are doing is firing of claude or whatever and going for a walk
by data_maan | 2026-02-08 08:20:08+00:00
Yes, but people at those labs may be running those problems because a Fields Medalist is in the paper, and it got hype.

Not because of the problems, and not because this is new methodology.

And once the labs report back, what do we know that we didn't know before? We already know, as humans, the answer to the problems, so that is not it. We already know that LLMs can solve some hard problems, and fail in easy problems, so that is not it either.

So what do we really learn?
by mdf | 2026-02-08 08:20:25+00:00
You're right. Perhaps clean would better capture the distinction in favor of solar in this context? Both corn and solar convert insolation to usable power with a short time between capture and use. Solar, on the other hand, is net negative when it comes to emissions, while the corn harvest is just burnt with the CO2 escaping back to the atmosphere. (And potentially, the solar panels can just be recycled back to new solar panels when they reach the end of their lifetimes. They're mostly aluminum and glass after all.)
by Suppafly | 2026-02-08 08:20:26+00:00
My job is like that, although it's mostly driven by my direct boss and not the whole company, but our yearly review depends on reaching out to our vendors and seeing if an AI solution is available for their products and then doing whatever is necessary to implement it. Most of the software packages we support don't have anything where AI would improve things, but somehow we're supposed to convince the vendor that we want and need that.
by pjmlp | 2026-02-08 08:22:11+00:00
Except that C++ provides the tools to do just like C, Rust, or whatever one feels like doing for dispatching, even if it requires a few pages of template metaprogramming mixed with compile time executions, or writing exactly the same C code on the common subset across both languages.

Now with reflection even more tools will be available.

Which is why despite all its warts and security flaws, many inherited from C source code compatibility, many domains will keep using it, because they will complain about their missing 1% that no one else uses.
by jvuygbbkuurx | 2026-02-08 08:22:11+00:00
One persons output doesn't scale for an entire org no matter how expensive and good the AI is. It's either good enough that anyone can do it, or bad enough that a human needs to be in control which caps the output at human understanding. It will always be more efficient to have every engineer be boosted a little bit than a single one a lot.
by Zakodiac | 2026-02-08 08:22:12+00:00
Yeah we've seen the same thing with a few smaller Rust tools I've built with agents. The strictness definitely helps - when it compiles you can be pretty confident there aren't weird runtime surprises lurking. So the fewer functional bugs theory checks out from what we've seen too.

The tradeoff is LLMs still struggle to produce good idiomatic Rust consistently so it takes more iteration cycles to get there (good agent tooling helps, linting/checks/etc.) The compile times on those iterations can be brutal sometimes depending on the project size which adds up for sure. The crafty agents can still find ways to satisfy the compiler without actually solving the problem correctly too, so the cheating risk of course doesn't fully go away.
by data_maan | 2026-02-08 08:23:22+00:00
It wasn't like this in any way.

CASP relies on a robust benchmark (not just 10 random proteins), and has clear participation criteria, objective metrics how the eval plays out, etc.

So I stand by my claim: This isn't scientific. If CASP is Japan, a highly organized & civilized society, this is a banana republic.
Show HN: I built a free dictionary API to avoid API keys
by suvankar_m | 2026-02-08 08:24:33+00:00
Hi HN,

I built a free, open-source dictionary API for developers who need quick
word lookups without authentication or paywalls.

It’s powered by Wiktionary and returns definitions, parts of speech,
pronunciations (IPA), and examples in clean JSON.

The repository contains the API layer only; the data ingestion and
processing pipeline that imports Wiktionary data into the database
is maintained separately.

Details:
- No authentication required
- JSON-only REST API
- English language support for now
- Licensed under CC BY-SA 4.0 (same as Wiktionary)

API example:
GET /dictionaryapi/v1/definitions/en/happy

Project link:
https://github.com/suvankar-mitra/free-dictionary-rest-api

I’d really appreciate feedback on:
- API design / response shape
- Missing fields developers usually expect
- Anything that would make this more useful

Thanks for taking a look.
by renewiltord | 2026-02-08 08:24:53+00:00
A natural question to ask in response, but I did not find out in each case in a way where I should say anything about their engineering so since I've said something about that I can't say who. I get how it comes off. If the Criterion of Embarrassment helps, I find myself sorely unqualified for this new world.

To be honest, I shouldn't have said anything in the first place. It isn't useful as it is because you can't reasonably believe me. I just feel blindsided by the stuff that's working now.
by wiradikusuma | 2026-02-08 08:27:02+00:00
OpenClaw made the headlines everywhere (including here), but I feel like I'm missing something obvious: cost. Since 99% of us won't have the capital for a local LLM, we'll end up paying Open AI etc.

How much should we budget for the LLM? Would "standard" plan suffice?

Or is cost not important because "bro it's still cheaper than hiring Silicon Valley engineer!"
by robinzfc | 2026-02-08 08:27:33+00:00
Mizar source was "available upon request" for maybe 30-40 years. It got completely open-sourced under GPL some 3 years ago (maybe earlier, not sure), see [1], also [2] and [3] about an alternative implementation in Rust.
Mizar is indeed "scarcely advertised", but all the information is publicly available, who wants to know knows. As for Mizar semantics, see for example [4].

[1] https://github.com/MizarProject/system
[2] https://github.com/digama0/mizar-rs
[3] https://arxiv.org/pdf/2304.08391v2
[4] https://link.springer.com/article/10.1007/s10817-018-9479-z
by 9dev | 2026-02-08 08:27:36+00:00
> […] overflowing with smart guys. […] smart guys build […] he's the smartest guy […] local rich guy would start a business […]

It’s pretty clear this entire crude theory is based on a thinking system that has little place for women in active positions.

And if your immediate reaction to that is annoyance because it seems like an unimportant detail, maybe reflect on that for a moment.
by hunterpayne | 2026-02-08 08:27:51+00:00
> Very few are American or ethnic Americans

Maybe you should look into why that is. Or specifically look at the scores and grades of students applying to grad school in the US vs who is admitted (this isn't a recent thing either) by country. If anything, we are probably behind where we should be because we don't admit the best into the best schools. We admit those that don't need grants (usually because their government pays instead).
by noodletheworld | 2026-02-08 08:28:32+00:00
> it would send the X-Country header for those blocked countries and it verifies that the feature was not really blocked.

There is no reason to presume that the agent would successfully do this.

You haven't tried it. You don't know. I haven't either, but I can guarantee it would fail; it's provable. The agent would fail at this task. That's what agents do. They fail at tasks from time to time. They are non-deterministic.

If they never failed we wouldn't need tests <------- !!!!!!

That's the whole point. Agents, RIGHT NOW, can generate code, but verifying that what they have created is correct is an unsolved problem.

You have not solved it.

All you are doing is taking one LLM, pointing at the output of the second LLM and saying 'check this'.

That is step 2 on your accuracy list.

> Do you disagree that an agent that simply writes code and uses a linter tool + unit tests is meaningfully different from an LLM that uses those tools but also uses the end product as a human would?

I don't care about this argument. You keep trying to bring in irrelevant side points to this argument; I'm not playing that game.

You said:

> I can empirically show you that this spectrum works as such.

And:

> I don't have empirical proof for 4 -> 5

I'm not playing this game.

What you are, overall, asserting, is that END-TO-END tests, written by agents are reliable.

-

They. are. not.

-

You're not correct, but you're welcome to believe you are.

All I can say is, the burden of proof is on you.

Prove it to everyone by doing it.
by jonplackett | 2026-02-08 08:29:01+00:00
Gold for Anthropic but kinda shit for everyone else no? Now they have a profit motive for slowing down the normal service.

This is the Deliveroo playbook of offering a ‘premium’ service that is really just the original service with the original slowed down.

Same with speedy boarding for airlines. Now almost everyone pays for it so you don’t even get a benefit.
by Jamesbeam | 2026-02-08 08:29:26+00:00
Keep fighting Harvard.
Pete Hegseth is so embarrassing.

I feel sorry for all American soldiers serving their country right now and that whole room full of generals he held his Ted talk to who looked as embarrassed as I feel right now.

He talks about the warrior class, but he allowed that US soldiers roll out the red carpet for Putin kneeling in the dirt, while putins bodyguards where laughing their ass off that US soldiers were crouching over the ground for them, so the commander in chief could impress the worlds greatest war criminal.

https://www.youtube.com/watch?v=hpUJK_yIRHE

Make kneeling great again I guess.

He talks about the highest moral and fitness standards, but the commander in chief is adipose and has a button in his office to get a fresh BigMac and a fresh coke while spending his time posting ai slop and racist memes on his social media platform.

Americans are lucky that you have so many good men and women serving your country putting the safety and well being of their people as priority over themselves, because otherwise by now three quarter of your troops would have quit.

They could make more money in the private sector and not have to deal with the bullshit the secretary seems to come up with while finding the bottom of a whiskey bottle. Still they put up with it every day, out of duty. I have nothing but respect for everyone serving under Laurel and Hardy.

I hope history will look back favorably at those heroes and expose Hegseth and Trump for what they really are. An embarrassment for all military standards and values we established for the past century.

Nobody wants mindless killing machines, they want fully functional members of society who serve their country and then go back to having a family and a well paying day job that is making a difference in the lives of others.
by noduerme | 2026-02-08 08:29:41+00:00
They're also very good at demoralizing people who actually code for a living. Or at least the hype surrounding them is. Up until a couple years ago I'd simply dive into a new coding assignment and be excited to solve all the problems. Some new widget? Maybe I could find a way to make the UX flow better, or add a couple neat little transitions, or heck even improve the mechanics or the business logic. I now find myself looking at assignments and thinking: Is this a waste of my time? I know how I'd do it, but I'm prone to a lot of yak-shaving and perfectionism. Should I just ask Claude to do it?

So alright, let's see what Claude does.

And then I get presented with a piece of code and a product that I wouldn't have chosen - but in some cases would be perfectly sufficient for minor work I shouldn't be yak-shaving. On the other hand, going beyond the simplest or most obvious is how I built my business. Bringing ideas to the table and executing them. So then I discard the Claude code and sit down to write it from scratch.

It is at that exact point that I start to wonder: does anyone even care?

People who take the time to think deeply through their work, and who "hammer in the extra nail" as my grandpa used to say (he was a general contractor) do so most often for their own sense of pride in a job well done, and for the trust that comes from their clients or employers knowing that they will go the extra mile and do a job well. But what happens when employers or clients don't treat the work as important - and when they would be okay with a bad or mediocre version on the cheap? That's not a new problem - usually I just won't work for those people.

But I just hate the pessimistic feeling I get about doing what I always loved to do - writing bespoke code - when I constantly keep asking myself do these vibe coders know something more than I do? Should I try yet again on that route? Worse, am I just wasting my time perfecting something that no one else will appreciate?

To anyone outside engineering, writing code looks like wizardry. I think the most common and most demoralizing outcome of LLM vibe coding has been to incorrectly make them think it's suddenly easier. And a new crop of vibe coders who think they don't have to think for themselves? They're not engineers. They may even be enemies of good engineering. They have more in common with the bosses and clients we've always had who said "hey, this should be a very easy request, can you just add a button to the customer app that will (fill in the blank with some wildly complicated business transaction that definitely can't be handled in one click).

All of it is sapping my motivation to do what I was good at, which is, solving problems and writing well tested code.

Sorry for the rant.
Show HN: Analyzing 9 years of HN side projects that reached $500/month
by haileyzhou | 2026-02-08 08:32:34+00:00
After analyzing 9 years of HN side project posts, I found some counter-intuitive patterns about what makes projects profitable.

Three things that stood out:

1. B2B dominates: 73% of $500+/month projects target businesses, not consumers

2. Speed matters more than polish: Average 47 days from launch to first sale. Most started charging with 3-5 core features.

3. Pricing cluster: 87% price between $20-49. Low enough for impulse purchase, high enough to be sustainable.

I compiled this into a dataset of nearly 700 projects with tech stack, pricing, and timeline data. Built it because I was planning my own side project and wanted to see patterns, not just success stories.

Available at [https://haileyzhou.gumroad.com/l/pknktt] ($49) as html report and raw .csv data.

Data source note: All from public HN posts 2017-2025. I did the cleaning, categorization, and cross-referencing.

Happy to discuss methodology or findings. Curious what patterns others see or if this matches your experience?
by phil21 | 2026-02-08 08:34:28+00:00
GLP-1s are already under immense pricing pressure due to competition, direct to consumer sales outside of the insurance system (to the point of forging entire new markets for drug companies never possible before - what other drugs have millions of customers paying $300-400/mo for prescription medications?), compounding pharmacies end-arouding patent law like this article is about, and outright black market sales that dwarf any other prescription medication classes outside of opioids and benzodiazepines.

It has nearly nothing to do with government action. These will be the most profitable medications ever invented once the dust settles and it’s a game of price optimization right now.