skysight-inc/HackerNews huggingface dataset:
I consider myself principally a software engineer, and like you I have avoided using AI aside from a few "haha look at that" images produced with DALL-E. Very rarely the forced AI content at the top of a search results page has helped me refine the term or concept I was trying to look up.

I like doing the things that AI is supposedly good at. I like learning, I like understanding what I am doing, I like the satisfaction of finally getting to a solution after banging my head against the wall for hours. I like the occasional sensation of being hopelessly lost and finding my way to the light. I like writing text and code. I'm not terribly bored by boilerplate. I like going down rabbit holes and experiencing happy accidents. I can't in good conscience sign my name to something that I did not create and do not understand deeply enough to explain to anybody who asks. I like doing the hard thing.

The immediate response I'm sure that I will get is some variant of "well everybody else is using it as a force multiplier and the way you do it is making the job take too long." And maybe that's true. Maybe I don't care. I am a person who takes pride in my craft. I enjoy the act of making things. Some people don't, I guess.

The day may come where I am viewed as a dinosaur, where the way I work becomes fundamentally incompatible with the way the industry works. And if that day comes, bluntly, fuck the industry. I'll go fix air conditioners instead.
When Bohr got it wrong: the impact of a little-known paper on quantum theory
Your words are fine from my point of view,
I am just tickled at the though of how hard it is to prove a star has no planets.
Even with all stars having planets, it is worthwhile to carefully choose
the field to maximize the chance of detection. Best would be if planetary disks aligned themselves in any predictable manner, but not much hope there.
Transformer Crisis: Deepak Divan Sounds the Alarm
But it isn't this simple, because you wouldn't need a single "maintaining" branch. You would need one for each feature release that the user may stop at.

So if you release the feature branch every 3 months after a few years you will have a dozen maintaining branches to backport fixes to, and in which to carefully test that the fix actually works. The problem is linearly worse if you release the feature branch more often.
I'd wager this is the real market. Ship some company a server rack with Deepseek R1 for a $1M annual rental fee + upgrades to the latest models.

think inside the box
I think they also published the training methodology as well - that others have reproduced, no? The only thing that I'm not sure is their low level nvidia CTX training code was released under the license - but in order for a third party to corroborate the training and testing they would need to have that code (and likely the training data as well) would they not?
You're right. It's been that intense - or I've actually been feeling the shift since the election ended, not the inauguration.
Imho, a good type system for scientific computing will let you check that your computations are well formed. For instance, if one tries to apply a matrix to a vector of the wrong size, the type system should report it, or rather one should be able to express this sort of constraint using the type system to the same effect.

You are however correct, that doesn't mean that C cannot be useful for scientific computing, but I think that there are better alternatives. Although, one can write the core functionalities in C for speed and efficiency, and use another language as a driver.
It was abbreviated incorrectly and it is ambiguous or even misleading in its current form.

> Otherwise please use the original title, unless it is misleading..
There is also a known phenomenon of "starvation euphoria." I don't know at what point of caloric deficit that kicks in, but it probably varies dramatically.
The claim is that this removes the human bottleneck (aka SFT or supervised fine tuning) on domains with a verifiable reward. Critically, this verifiable reward is extremely hard to pin down in nearly all domains besides mathematics and computer science.
DeepSeek actually opening ClosedAI up makes me like them even more.. this is great :)
SmolGPT: A minimal PyTorch implementation for training a small LLM from scratch
The down round that OpenAI will have will hurt...
It has a power button, which effectively reduces the microarchitectural attack surface to zero.
That's a bit US-centric.
https://www.phind.com/search?cache=ef2ftkexs44l8t5u5vzmgkg1

Learning, attentional control and action video games
https://pmc.ncbi.nlm.nih.gov/articles/PMC3461277/

Not nerd sniped at all.

> in other words, an end in and of itself — we have recently considered the possibility that enhanced attention is instead a means to an end, with that end being better probabilistic inference [54]

Improved probabilistic inference as a general learning mechanism with action video games
[54] https://pubmed.ncbi.nlm.nih.gov/20833324/
Or overthrew democracies in South America, leading to the refugee crisis it now refuses to take responsibility for.

The entity that is least problematic (of the available options) and should be entrusted is the European Union.
Microsoft Service Status page is down
Seems to also be affecting admin.microsoft.com. Azure and Office365 are still up and running.
One of my absolute favorite series of YouTube videos is "Building a new Amiga" from RMC, where he basically builds a new Amiga 500 with as many new parts as possible.

1: https://www.youtube.com/watch?v=ECu6i6WR7mo

2: https://www.youtube.com/watch?v=87q-TTG48Ew

3: https://www.youtube.com/watch?v=Va91bISkpKI

4: https://www.youtube.com/watch?v=4EsRI-5fblM
Like this? https://www.researchgate.net/publication/342626956/figure/fi...
https://www.nature.com/articles/nature.2012.10130

Is the grandparent of this article and is not near as annoying to read. Can we replace the link?
Surely MS has bug bounty programs, no? Maybe I'm missing something.
Powerful people are playing chess and the rest of us are pawns - whatever could go wrong?
But it does mean moat is even less defensible for companies whose fortunes are tied to their foundation models having some performance edge, and a shift in the kinds of hardware used for inference (smaller, closer to the edge.)
Same. Lost both smell and taste for a few days after I got COVID in 2021. My taste came back quickly, but my sense of smell took longer to come back and is still probably about 50% diminished. I can't smell things from a distance, only up-close or if it's really strong. Like I can't tell if my house stinks because my garbage needs to be taken out, but I can smell it up close. I've just relegated myself to not being able to smell much anymore which I guess is better than some of the other Long COVID symptoms out there. I do miss smelling things though.
They might show DeepSeek's model calling itself ChatGPT, which users have already alleged. Same as how Cisco proved Huawei was stealing router code.

Except in this case, nothing was stolen, unless they want to call ChatGPT's own training on source data theft too.
If the US doesn't do it, China will and this argument has nothing to do with deepseek although it does prove the point.
Stepping back, it feels like the techies working on ai have been celebrating the expense of it all. That techies have been wanting bigger and bigger clusters as some kind of bragging rights and that suits the suits who see it as a most to protect their crazy stock validations. The money was being made not by the miners but by nvidia selling shovels they could name the price of.

Necessity is the mother of invention. Deepseek is demonstrating that if the incentives are aligned to want efficiency then good engineers - and there are plenty of them in every country - can make things fly.
Thanks, I didn’t realize this was possible. Will take a look into it.
Article 1 Section 8 does not enumerate Congress with the power to provide healthcare, so Medicaid should onoy be a state level program.

I know removing it harms those at the low end of the income spectrum, and that is a bummer, but I really would prefer Congress ammend the Constitution instead of just passing laws of which they have no authority to pass.
> even if your body has had more than enough food to sustain itself for a whole week.

For reference, that amount of food is zero. You're already set for the week.

Animals don't operate like cars where they're constantly on the verge of death.
Essentially object proxies. When you invoke a property getter in tracked contexts (like DOM element construction), a dependency is registered for that object property. When you invoke the corresponding setter, the update function for the list of dependencies are notified.

That's the idea, in a nutshell.

I'm definitely not the first one to come up with this idea, but I hadn't found another UI framework that worked exactly like how I wanted. Solid is kind of close, but not it. Vue has some of this as well, but it's a bit more kitchen-sink in its scope.
> The truth is that it's just not a way of working that comes naturally to people who write a lot of SQL for a living.

I don't know... Refactoring Databases is pretty old now. Plenty of DBAs can handle this style and even prefer it. I think this is more of a cultural problem in the small than in the large.
DeepSeek R1 vs. OpenAI's best reasoning models
>Ultimately, R1-Zero demonstrates the prototype of a potential scaling regime with zero human bottlenecks – even in the training data acquisition itself.

I would like this to be true, but doesn't the way they're doing RL also require tons of human data?
Download your weights while you still can I guess…
DeepSeek model not providing answer on the Tiananmen Square and ChatGPT providing answer on ethnic cleansing of Palestine are two sides of the same coin.
By verifiable do they mean it in the complexity theory P/NP sense of the word?
100% ditto for me. It's the interface for my external brain. The file search and global search are exactly what I need, and the multi-cursor editing, etc. are fantastic.

I always have my IDE up on one monitor and Sublime on the other.
Well, he's not wrong

Subdivisions that only work for one customer grow around all their idiosyncrasies and it's hard to adapt later

Actually putting the design work into something you can work with is hard work
If you haven’t listened to it already, Acquired remastered their TSMC episode. You can find that here https://www.acquired.fm/episodes/tsmc-remastered

If you are interested in getting more context on TSMC, this is a great place to start.
> explanation for repealing net neutrality ... You want to ship some cargo over plane, some over ships, and some over trains - so why wouldn't network packets be any different?

To make this analogy fit better, let's re-imagine each transport method as having similar cost.

The answer is that neutrality would prohibit cargo corp from rerouting cargo over slower ship...
1) when there is no logistical necessity to do so
2) because there is loads of available air-transport capacity.

Even if doing so yields fatter dividends for stockholders and execs.
This might be llama's fault, since the Meta AI in WhatsApp also frequently says it's ChatGPT. I think it's already feeding on ChatGPT's slop. or that's what most people mention about AI models.
Title is: Elon Musk Lackeys Have Taken Over the Office of Personnel Management
If you don't think it's a good thing to recognize your own mistakes and try to warn others about them, then I don't think I have anything else to discuss.
Kudos to all the people who shared their opinions and mentioned many alternative services and tools. When I wrote the original post, I didn't know it would end up on HN and spark a discussion here, which means the topic is relevant. It's not just about the slop music itself, but the way Spotify is evolving (or devolving).

I enjoyed the Weekly Discovery and Made For You playlists for years and its suggestion mechanisms were just what I needed. I could tolerate the terrible UI and other minor flaws of the app as long as the excitement of discovering new music continued. I also didn't like the special promotions, podcast recommendations, and other novelties, but the real value was there and it kept me paying for the service.

The changes started around 2020, when the playlists started to contain fewer songs and artists that I could call discoveries. I found myself in what could be called a music bubble, and it didn't feel like it was growing much.

Also, when I first started using Spotify, I spent a lot of time in the app and was more involved in the listening process. Now I was mostly just hitting the button, minimizing the window, and listening to music in the background. It became less interesting. I'd come back to the app occasionally to skip (or block) a track I didn't like. And then came the AI-generated tracks, which was the last straw for me.

Not that I hate Spotify, it's just sad to see it go the way of enshitification. It probably still meets the needs of millions of other users, but I'm no longer one of them. In fact, I'm grateful for the opportunity to be more conscious about my hobby again. I still have my mp3 library that I've been building since the late 1990s, and I still buy and donate to artists from time to time, so I just have to relearn how to do the things that AI algorithms have tried (and failed) to do for me.
Would you please not do this here? We're trying for an opposite sort of conversation.

https://news.ycombinator.com/newsguidelines.html
> no other civilization in Americas, Africa or Asia really ever want to colonize

Barley warrants a response but

https://en.wikipedia.org/wiki/Japanese_colonial_empire
> Are people really locked out of LLMs due to price?

I think so. When you start using a good model, you soon learn that it can make a difference. If you have domain knowledge, you can treat LLMs like a design partner. With DeepSeek, what would have cost 50 dollars to use Sonnet is now 5 dollars. Having to only spend 5 dollars a month can be a real game changer for many.
That's not correct. First of all, training off of data generated by another AI is generally a bad idea because you'll end up with a strictly less accurate model (usually). But secondly, and more to your point, even if you were to use training data from another model, YOU STILL NEED TO DO ALL THE TRAINING.

Using data from another model won't save you any training time.
Quite frankly I don't care either way, I just wanted a source. I'm happy you feel that way though.

This is the best write up I found for lighting source, efficiency, and consumption relationship.

As a first step in looking at this, I’ve taken the data from the analysis and plotted it on a log scale. This shows clearly how consumption has increased as population and incomes (measured by per capita GDP) have risen and prices have fallen. The central role played by efficiency is indicated by the dashed line, which shows the number of kWh needed to produce a unit of light (1/efficiency). Comparing this with the solid blue price line showing price indicates that the 100 fold fall in price per unit of light in the 20th century was due to increased efficiency, mainly as electricity replaced gas. Yet demand rose strongly over the period, increasing more than 100 fold. This challenges the view that increases in efficiency translate into reductions in demand, and thus emissions.

https://onclimatechangepolicydotorg.wordpress.com/low-carbon...
This has more ads than content
sounds like an SNL skit, Zuckerberg is setting up extremely targeted advertising for Trump.
Apologies, I'm not clear on what you're pointing out in my comment as incorrect. I specified that DEAs are used in products that come from industrialized processes, that you won't find them in home-made products of the same nature.

I'm using "industrialized" to mean "mass-produced" here, as in stuff that comes from a factory rather than a small work station in a DIYer home. I wonder if this may be causing confusion?

The article you linked is likely speaking of mass-produced products, but does not go so far as to specify that (they should). I separate the two since they really are quite different. If you put one of my bars of soap up against Dove, for example, you'll notice that mine do not lather nearly as much a Dove, nor do they have that "buttery" texture of commercial soaps. DIY soaps typically only need lye, distilled water, and olive oil then whatever you want to add for scent. DEA does not enter the equation in a home-made version.
If you are testing a single hypothesis, sure. But nowadays, statistics training really weighs on bonferroni correction, or other methods to deal with the issue raised by the above-referenced XKCD, whentesting multiple hypotheses.
Spot on! I concur most European business and public sector institutions would be eager to rent this because they are not allowed by law to use US datacenters like AWS or Azure.
I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps".
Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc.

It tests the software by compiling and running it reading errors and failed tests and fixing the code.

Then, it deploys the software in production for you. It compiles your app to an APK file and publishes it on the Google play store for example.

Sure an LLM now may still not be able to get everything perfect as far as it's outputs go. But surely there's already systems and workflows in place that will auto run your code, compile it, feed errors back to the LLM, some api to interact with cloud providers for hosting etc?
> Click bait headline. Nothing was proven about open source as the future.

Well sure but generally speaking proofs about reality are an oxymoron, so who on earth was taking the headline at face value to begin with? This is a rhetorical technique referred to as "hyperbole".
>I'm saying "mild" as he was not bedridden, but the cognitive decline was noticeable.

I've noticed some of that with family members that have gotten covid, but it's hard to say that it's definitely that as they are older and age related decline is hard to nail down too.
I mean, we had actual doctors doing the "In your head" song and dance before my wife was diagnosed with RA for a good 3-4 months. There was visible swelling/blistering and she couldn't walk unassisted. This is an anecdote of course, but, this was a very obvious condition, and we got "in your head"ed.
Related:

Freeze on grant funding temporarily halted by federal judge

https://news.ycombinator.com/item?id=42859421
It seems to be undermined by the same principle that says that going into a library and reading a book there is not stealing when you walk out with the knowledge from the book.

OpenAI seems to feel that way about the their use of copyrighted material: since they didn't literally make a copy of the source material, it's totally fair game. It seems like this is the same argument that protects DeepSeek if indeed they did this. And why not, reading a lot of books from the library is a way to get smarter, and ostensibly the point of libraries
For people who don't want to click into X or don't have an account to see the thread: The rig in question uses dual-socket EPYCs and no GPUs, relying on 768 GB of fast DDR5 RAM. It gets about 6 - 8 tokens/sec for the full DeepSeek R1 model.
So did he change his mind about the whole "I am selling almost all physical possessions. Will own no house" thing?
I've been working in this space for a long time, where I'm one of the co-founders of Write the Docs: https://www.writethedocs.org/ -- we focus more on software docs.

The view from the industry is basically that STC was a bit behind the times, and was slowly dwindling in terms of reach and value. They still had some active chapters, magazine, and academic journal that provided value for folks, but membership wasn't as valuable as it had been.

They have been around a long time, and had a wider purview that WTD, focusing on many different types of technical writing. They had members in industries like Automotive, Engineering, and Aerospace, as well as Software.

The best way to think about them is something like the ACM in the software industry. They have been taken over by more current community approaches in various areas (eg. Pycon), but also still doing some more traditional stuff that adds value but isn't as relevant to day-to-day practitioners.
Hypocrisy or not, the US government has managed to make this work for a long time now, the Biden administration just proves the point. Thankfully, other countries are starting to catch up to this scam.
Isn't it the same from the point of view of the consumer's calculus? If suddenly my car can do the same trip for half the fuel and in half the time, whether it's because the road is better or because my car is better, it won't affect how I'm going to use my car from then on.
Then how can you explain that China’s hostility towards Western tech companies being present inside their own country has not created what you’re describing?

Is hostility a bad idea only for America? Sure hope not.
Comments moved to https://news.ycombinator.com/item?id=42861475, which has the more informative of the two articles that this one was lifted from.

Submitters: "Please submit the original source. If a post reports on something found on another site, submit the latter." - https://news.ycombinator.com/newsguidelines.html

Please especially don't submit knock-off articles that jack up the linkbait and indignation. That's what we're trying to avoid on Hacker News. There are enough places to get that hit elsewhere.
This has been in the back of my head since the news broke. Has anyone built their own R1 from scratch and validated it?
There is a reason I kept emphasizing the ChatGPT product. The (paid) ChatGPT product is not just a text based LLM. It can interpret images, has a built in Python runtime to offload queries that LLMs aren’t good at like math, web search, image generation, and a couple of other integrations.

The local LLM on iPhones are literally 1% as powerful as the server based models like 4o.

That’s not even considering battery considerations
Is there a benchmark for AI code suggestion, or some comparative review? I don't have the time to test them all; I wonder if I could be using a better one?
They outlined the methodology. They didn't publish their code or the training set.
[dupe] https://news.ycombinator.com/item?id=42853174
I was going to suggest the book you mentioned, but as you mentioned it's not an description of the design process, it's a description of the system as designed.

I don't think I've seen a holistic document that tries to explain the whys of everything. You'd really need to just go through the history of changes, and look at contemporaneous mailing lists, announcements and conference presentations.

If you go back far enough, most of the algorithms are pretty basic. Lots of full array scans, which wasn't horrible because the arrays were tiny. The algorithms come from what was convenient at the time it became a bottleneck; lots of hashing, and then you increase the size of the hashtable because there's too many collisions (although sometimes that gets silly --- it doesn't make a lot of sense to hash port numbers when your hash table size is 64k, and it makes no sense to have a hash table size larger than that, because that's the size of the input space). And sooner or later you need to deal with having large numbers of cores all working on the same tables, and how that interacts with locking, NUMA, etc.

If there's some choice really puzzling you, you can probably ask on the freebsd-net mailing list, and get a reasonable answer.
As soon as they dropped the Nexus line, things went downhill fast. IMO the nexus 5 was peak android.