MurphyA/hacker-news huggingface dataset:
by rectang | 2025-08-05 17:56:24+00:00
I agree that we shouldn't mix allocators, and so does the post's author. Can you put your technical arguments into terms which aren't so dismissive of people's honest efforts to learn? We ought to be all on the same page. You could affirm and refine the post's conclusions and bring us together, rather than ridicule someone for ever entertaining a notion you consider "insane".

Here's what I got when I asked ChatGPT to rewrite your first comment to be as constructive as possible:

"Totally agree — relying on C and Rust to interoperate at the allocator level is risky at best. Allocating in C and freeing in Rust (or vice versa) assumes a level of compatibility that just isn’t guaranteed. Even within a single ecosystem, allocator behavior can change across versions — whether it’s different versions of libc or updates to Rust’s std. So expecting consistent behavior across language boundaries is, at the very least, unreliable. If you need to free a C-allocated pointer, the safest and cleanest approach is to call back into C to do it."

That's not a drop-in substitute for your original comment and "totally agree" is over-the-top cloying in the usual ChatGPT obsequious way, but I still think it's helpful in suggesting alternative framings.
by 0xbadcafebee | 2025-08-05 17:56:38+00:00
The domination of ads on the internet, as I remember it, came about around the time Google exploded in popularity. At the time there were still free services paid for by ad-banners, but it was to pay for services, not content.

Whether it's mindless "recipe blogs", the YouTube spam of reviewers being paid to lie about products, or the instagram Influencers (the fact that people call them that un-ironically is insane), I welcome the death of content. The "normal" web will still be here when it all goes away.
by Zipzip | 2025-08-05 17:56:41+00:00
The forecasting community often promotes prediction markets as information aggregation tools, but sports betting provides a real-world test case for their claimed benefits vs. actual risks. As a concrete example of what can go wrong: NBA player Jontay Porter was banned for life after deliberately underperforming in games where he and associates had bet on his poor performance. He'd grab a few rebounds then fake an injury to stay under betting thresholds, profiting from markets on his own stats. This isn't isolated - similar manipulation scandals now exist across tennis, football, rugby, cricket, and other sports. The incentive structure is clear: participants don't just predict outcomes, they can influence them. Beyond direct manipulation, prediction markets also incentivize misinformation campaigns. Buy contracts cheap, spread convincing rumors to move market prices, then sell at profit.
by Imustaskforhelp | 2025-08-05 17:56:54+00:00
Okay I will be honest, I was so hyped up about This model but then I went to localllama and saw it that the:

120 B model is worse at coding compared to qwen 3 coder and glm45 air and even grok 3... (https://www.reddit.com/r/LocalLLaMA/comments/1mig58x/gptoss1...)
by tekacs | 2025-08-05 17:56:59+00:00
A few days ago I posted a slowed-down version of the video demo on someone's repo because it was unreadably fast due to being sped up.

https://news.ycombinator.com/item?id=44738004

... today, this is a real-time video of the OSS thinking models by OpenAI on Groq and I'd have to slow it down to be able to read it. Wild.
by nis0s | 2025-08-05 17:57:03+00:00
I am of the view that cultures should not be defined by race or religion, but rather philosophical identities, and those identities should form the basis of what constitutes a state or nation.

Reductive analysis will often find race or religion as a scapegoat for one thing or another, but there are many examples of thriving multicultural nation states, where the key to their success is a strong national identity beyond religion or race.

Why is this distinction important? Consider that immigration is a useful tool for statecraft and economies, but how useful it remains in the long term depends heavily on the integration of new members to the existing society; the onus is often put on the newcomers to assimilate, but integration is often lacking.

Considering the development of human societies up to this point, it’s evident that some ways of thinking are important to highlight and preserve for future generations, while others not so much. But that doesn’t have anything to do with race or religion, per se.

Early human settlements formed around the same idea: groups of people living together to produce a set of functions to contribute to a greater community, for safety and resource optimization. We can assume that these people possessed some degree of like-mindedness towards societal organization, which is why they went on to develop proto-cities etc. When viewed through the lens of a larger historical view, the importance of nation states is not merely about concentration of power or interests of the elites. I think there are more fundamental reasons why groups of humans formed a people, an identity, a culture etc. All of this is our way of stabbing in the darks of uncertainty, and dealing with why we’re here at all.
by ryandrake | 2025-08-05 17:57:09+00:00
Am I the only one super confused about how to even get started trying out this stuff? Just so I wouldn't be "that critic who doesn't try the stuff he criticizes," I tried GitHub Copilot and was kind of not very impressed. Someone on HN told me Copilot sucks, use Claude. But I have no idea what the right way to do it is because there are so many paths to choose.

Let's see: we have Claude Code vs. Claude the API vs. Claude the website, and they're totally different from each other? One is command line, one integrates into your IDE (which IDE?) and one is just browser based, I guess. Then you have the different pricing plans, Free, Pro, and Max? But then there's also Claude Team and Claude Enterprise? These are monthly plans that only work with Claude the Website, but Claude Code is per-request? Or is it Claude API that's per-request? I have no idea. Then you have the models: Claude Opus and Claude Sonnet, with various version numbers for each?? Then there's Cline and Cursor and GOOD GRIEF! I just want to putz around with something in VSCode for a few hours!
by christopher8827 | 2025-08-05 17:57:09+00:00
Location: Currently in Sydney, Australia.

Remote: Yes.

Willing to relocate: Yes. I am a PR of Spain and an Australian citizen.

About: I'm looking for any AI/ML roles at the moment. I have experience applying ML to biology in research so I am looking to see if anything fits.

Technologies: - Web Dev: JavaScript, TypeScript, React, Redux - Data Science: Python (SciPy, NumPy, Pandas, TensorFlow, PyTorch, Jupyter), Neural Networks, SVMs, PCA, MDS, Decision Trees, Random Forests, R

Résumé/CV: https://drive.google.com/file/d/1Ytr4T52WnYPu2kx13MSjYkeu-YH...

Email: me [at] christopheragnus.com
by akkartik | 2025-08-05 17:57:17+00:00
The architect Christopher Alexander has this pattern in "A Pattern Language" called "Home Workshop". (Summary: https://www.henrikkarlsson.xyz/p/christopher-alexanders-arch...) Roughly, the idea was that if you can walk around your neighborhood and see different people working at their professions, the wholeness of the neighborhood increases. It comes more alive. Children imbibe a more realistic sense of what different professions involve. Professionals are connected to the world as they create. I try to keep this in mind. As our world has grown more digital, this consideration was one of the reasons I created this devlog last year: https://akkartik.name/freewheeling-apps
by coltonv | 2025-08-05 17:57:37+00:00
Appreciate the comment!

> I mentioned in another comment the major flaw in your productivity calculation, is that you aren’t accounting for the work that wouldn’t have gotten done otherwise. That’s where my improvements are almost universally coming from. I can improve the codebase in ways that weren’t justifiable before in places that do not suffer from the coordination costs you rightly point out.

I'm a bit confused by this. There is work that apparently is unlocking big productivity boosts but was somehow not justified before? Are you referring to places like my ESLint rule example, where eliminating the startup costs of learning how to write one allows you to do things you wouldn't have previously bothered with? If so, I feel like I covered this pretty well in the article and we probably largely agree on the value that productivity boost. My point is still stands that that doesn't scale. If this is not what you mean, feel free to correct me.

Appreciate your thoughts on hallucinations. My guess is the difference between what we're experiencing is that in your code hallucinations are still happening but getting corrected after tests are run, whereas my agents typically get stuck in these write-and-test loops and can't figure out how to solve the problem, or it "solves" it by deleting the tests or something like that. I've seen videos and viewed open source AI PRs which end up in similar loops as to what I've experienced, so I think what I see is common.

Perhaps that's an indication of that we're trying to solve different problems with agents, or using different languages/libraries, and that explains the divergence of experiences. Either way, I still contend that this kind of productivity boost is likely going to be hard to scale and will get tougher to realize as time goes on. If you keep seeing it, I'd really love to hear more about your methods to see what I'm missing. One thing that has been frustrating me is that people rarely share their workflows after makign big claims. This is unlike previous hype cycles where people would share descriptions of exactly what they did ("we rewrote in Rust, here's how we did it", etc.) Feel free to email me at the address in my about page[1] or send me a request on LinkedIn or whatever. I'm being 100% genuine that I'd love to learn from you!

[1] https://colton.dev/about/
by foundry27 | 2025-08-05 17:57:40+00:00
Model cards, for the people interested in the guts: https://cdn.openai.com/pdf/419b6906-9da6-406c-a19d-1bb078ac7...

In my mind, I’m comparing the model architecture they describe to what the leading open-weights models (Deepseek, Qwen, GLM, Kimi) have been doing. Honestly, it just seems “ok” at a technical level:

- both models use standard Grouped-Query Attention (64 query heads, 8 KV heads). The card talks about how they’ve used an older optimization from GPT3, which is alternating between banded window (sparse, 128 tokens) and fully dense attention patterns. It uses RoPE extended with YaRN (for a 131K context window). So they haven’t been taking advantage of the special-sauce Multi-head Latent Attention from Deepseek, or any of the other similar improvements over GQA.

- both models are standard MoE transformers. The 120B model (116.8B total, 5.1B active) uses 128 experts with Top-4 routing. They’re using some kind of Gated SwiGLU activation, which the card talks about as being "unconventional" because of to clamping and whatever residual connections that implies. Again, not using any of Deepseek’s “shared experts” (for general patterns) + “routed experts” (for specialization) architectural improvements, Qwen’s load-balancing strategies, etc.

- the most interesting thing IMO is probably their quantization solution. They did something to quantize >90% of the model parameters to the MXFP4 format (4.25 bits/parameter) to let the 120B model to fit on a single 80GB GPU, which is pretty cool. But we’ve also got Unsloth with their famous 1.58bit quants :)

All this to say, it seems like even though the training they did for their agentic behavior and reasoning is undoubtedly very good, they’re keeping their actual technical advancements “in their pocket”.
by tart-lemonade | 2025-08-05 17:57:43+00:00
> If you use this kind of software to hire, you are the loser. The good talent doesn’t need you—it’s the other way around.

Kinda like stack ranking for layoffs, especially when done regularly. The idea of cutting the fat always seems appealing, but it also signals things are going to get unpleasant, and top talent tends to jump ship early unless you're paying top dollar to make it all worthwhile. The ones you wanted to retain the most are the first out the door, and within a few years, the only ones left are those who don't have great prospects elsewhere, frequently with reduced output since the stress induced by the omnipresent prospect of layoffs isn't great for morale or productivity.
by blibble | 2025-08-05 17:57:47+00:00
> Replacing the EU management with a US manager would at a minimum be a breach of contract - and since some of these contracts are for sensitive / national security use cases, possibly much more serious legal consequences than just garden variety breach of contract

and the EU has no leverage to do anything about it

if they did they wouldn't have selected AWS "Sovereign" cloud in the first place
by QuantumGood | 2025-08-05 17:58:00+00:00
Seeing how Perplexity (my preference) or Kagi is much better than Google for 98% of queries, Google has indeed killed itself, at least for the time being. Xfinity has a one year free on Perplexity Pro, which is how I tested the switchover.

Early attempts at search-emabled AIs (e.g. ChatGPT) were nowhere near as good at first, so I stopped testing them against Perplexity/Kagi
by Rooster61 | 2025-08-05 17:58:18+00:00
+1 to using Frigate. I've had it running on my home server for a couple of years and it has served us very well. Detect is running on an old GTX 960 I had laying around, and it works a charm (though I'll probably run out of legroom once I bring up more cameras).

One of the big advantages is that I can pick and choose which camera I use, and then segment it off on it's own firewalled VLAN so it's only talking to my server applications. That lets me know that its not phoning home, and I can run PoE cameras that are immune to wifi jammers.

The idea that the surroundings of my house aren't being beamed straight into an Amazon datacenter somewhere is particularly satisfying.
by vkou | 2025-08-05 17:58:21+00:00
It won't work out because the people in charge are the dumbest motherfuckers imaginable. They are utterly convinced that every one of their stupid ideas is brilliant, that they know everything, and that the way forward is to liquidate anyone who has the audacity to say 'no' or 'you're wrong' to them.

That's not the roadmap to good management of anything, as literally anyone who has ever worked a job will tell you. How people can see an amalgamation of all the traits they despise in a peer or leader that they actually have to interact with, and go 'oh yeah this guy should be running the entire country, this will end well' is mindblowing.

The empire can run on fumes and momentum... for a while. No company or country is so exceptional that it can survive enough mismanagement, eventually you burn through the furniture, and piss away whatever lead or competency you had.
by jjani | 2025-08-05 17:58:33+00:00
On the other hand, they have always exposed their raw chain of thought, so you know exactly what you're paying for, unlike OpenAI who hides it. Similarly they allow an actual thinking budget rather than vague "low, medium, high", again unlike OpenAI. They also allow API access to all their models without draconic send-us-your-personal-data-KYC, once more unlikely OpenAI.

They might not fit your personal definition of "openness", but they do fit many other equally valid interpretations of that contept.
by ClassAndBurn | 2025-08-05 17:58:53+00:00
Open models are going to win long-term. Anthropics' own research has to use OSS models [0]. China is demonstrating how quickly companies can iterate on open models, allowing smaller teams access and augmentation to the abilities of a model without paying the training cost.

My personal prediction is that the US foundational model makers will OSS something close to N-1 for the next 1-3 iterations. The CAPEX for the foundational model creation is too high to justify OSS for the current generation. Unless the US Gov steps up and starts subsidizing power, or Stargate does 10x what it is planned right now.

N-1 model value depreciates insanely fast. Making an OSS release of them and allowing specialized use cases and novel developments allows potential value to be captured and integrated into future model designs. It's medium risk, as you may lose market share. But also high potential value, as the shared discoveries could substantially increase the velocity of next-gen development.

There will be a plethora of small OSS models. Iteration on the OSS releases is going to be biased towards local development, creating more capable and specialized models that work on smaller and smaller devices. In an agentic future, every different agent in a domain may have its own model. Distilled and customized for its use case without significant cost.

Everyone is racing to AGI/SGI. The models along the way are to capture market share and use data for training and evaluations. Once someone hits AGI/SGI, the consumer market is nice to have, but the real value is in novel developments in science, engineering, and every other aspect of the world.

[0] https://www.anthropic.com/research/persona-vectors > We demonstrate these applications on two open-source models, Qwen 2.5-7B-Instruct and Llama-3.1-8B-Instruct.
by dmitrygr | 2025-08-05 17:58:56+00:00
God damn, that is some good reply!

> The job of a search engine is to produce the most relevant search results, period. Kagi excels at this precisely because we remain unimpressed by world politics. The moment 'politics' becomes a factor in search results is the moment I stop working on a search engine.

> We set out to fix search, not the world.
by justanotherunit | 2025-08-05 17:58:58+00:00
Whatever makes you feel great.

I am kinda in the same boat (but I do not write articles), spending most of my free time either learning or developing.

Frankly, I love it. It makes me happy, so why change it? If I feel burnt out, I usually switch to something else for a short time, but I can mostly switch between reading/coding/watching tech influencers.

So if you do not feel unhealthy (exercise can always help to take a natural break anyway), keep on learning and developing :)
by narrator | 2025-08-05 17:59:02+00:00
Yeah, China is e/acc. Nice cheap solar panels too. Thanks China. The problem is their ominous policies like not allowing almost any immigration, and their domestic Han Supremacist propaganda, and all that make it look a bit like this might be Han Supremacy e/acc. Is it better than wester/decel? Hard to say, but at least the western/decel people are now starting to talk about building power plants, at least for datacenters, and things like that instead of demanding whole branches of computer science be classified, as they were threatening to Marc Andreessen when he visited the Biden admin last year.
by PaulHoule | 2025-08-05 17:59:16+00:00
What I think is funny is that everybody wants to create manufacturing jobs today but prior to deindustrialization people seemed to think factory jobs were the worst, see

https://genius.com/The-kinks-working-at-the-factory-lyrics

Germany in the Altes Reich had a rigid system of noble status where you were a landowner (solar economy) or nothing. They were quite shocked to find that all of a sudden you had status because you were a doctor or a lawyer or a scientist or a businessmen and that the people who were in these positions were people who were excluded from the old systems.

I think Chinese people have idealized education for a really long time (it is not enough to be a warrior, you have to be a warrior and a scholar) but yeah, the "100 years of humiliation" adds to the pressure. Other ethnic groups come to different conclusions as many blacks would say "they won't let you do anything with that degree"
by dekhn | 2025-08-05 17:59:19+00:00
If there was a coherent plan in DOGE to make more money available to do important science, maybe that could work. However, nothing DOGE has done has shown any sort of logic in terms of outcome maximization. The collection of activities (partly DOGE, partly Trump org) applied to scientists has been super-impactful (in an entirely negative way) for science we already know is important

The pool of skilled scientists to be hired shrinks when you cut funding in arbitrary ways.
by mandolingual | 2025-08-05 17:59:34+00:00
"Consciousness" is an overloaded thought killer that swerves all conversation into obfuscated semantic arguments. One person will be talking about 'internality' and self-image (in the testable, mechanical sense that you could argue Chain of Thought models already have in a petty way) and the other will be grappling with the concept of qualia and the ineffable nature of human experience.
by K0balt | 2025-08-05 17:59:56+00:00
As a renderer, given a POV, lighting conditions, and world mesh might be a very, very good system. Sort of a tight MCP connection to the world-state.

I think in this context, it could be amazing for game creation.

I’d imagine you would provide item descriptions to vibe-code objects and behavior scripts, set up some initial world state(maps), populated with objects made of objects - hierarchically vibe-modeled, make a few renderings to give inspirational world-feel and textures, and vibe-tune the world until you had the look and feel you want. Then once the textures and models and world were finalised, it would be used as the rendering context.

I think this is a place that there is enough feedback loops and supervision that with decent tools along these lines, you could 100x the efficiency of game development.

It would blow up the game industry, but also spawn a million independent one or two person studios producing some really imaginative niche experiences that could be much, much more expansive (like a AAA title) than the typical indie-studio product.
by CalChris | 2025-08-05 18:00:17+00:00
The business model, not the utility.

… it would simply make up confident answers. Dunno if you've used Google recently, but it will very confidently provide simply useless search results, pages and pages and pages of useless search results.

I look at using LLMs as like using another programming language. My C++ skills are not going to be directly useful in Rust or OCaml. I need to learn a new paradigm.

I'd ask AI the same questions. This is the crux. You really have to learn how to write (engineer) good prompts and you have to check what the LLM wrote. You use 'confidently' as a disparaging word. I think LLMs clear writing is quite useful in the reduction phase.

LLMs are perfect? No, just way better than Google.

To organize the world's information and make it universally accessible and useful has become To serve an ad and call it a day.
by hollerith | 2025-08-05 18:00:18+00:00
The drug companies are very good at making new drugs with high profit potential look better than they are. I'm worried semaglutides are another fen-phen, Vioxx, Quaaludes, Trovafloxacin or OxyContin.

One thing to watch for is effect size: how big of an anti-aging effect does Ozempic confer relative to other good interventions? Were the subjects doing other solid anti-aging interventions like the Fasting Mimicking Diet (FMD)? If not, the 2 interventions might affect the same pathways with the result that if you are doing FMD, you get no additional benefit from Ozempic.

Fen-phen is particularly interesting here because people reported that not only did it help them lose weight, it gave them more willpower and changed their personalities for the better.