skysight-inc/HackerNews huggingface dataset:
There's a big difference between allowing the information to exist and "owning up to it." The US just has a different strategy for dealing with it.
a) As soon as I saw the domain I knew this was an ad (Lago has nothing to add to this conversation)

b) DeepSeek is the most dangerous thing that's happened to Open Source models in recent memory, through no fault of their own.

The hysteria has outrun the reality and now there's a going to be a similarly disproportionate backlash.

It's already happening: Anthropic's CEO simultaneously railing against what they achieved and using it to justify stronger export restrictions, this morning.

And our current government doesn't want to be going on stage talking about $50B mega projects only for laypeople to (mistakenly) believe it only takes a few million to do the same.

And the idea that a Chinese company is the one that did this is going to play into so many hands, so perfectly. You can see the censorship story start taking the narrative despite this not being the first or last Chinese hosted model to comply with Chinese law.

Soon the national security angle will break out, especially if someone jailbreaks or abliterates it and gets "harmful outputs" that other models would also happily produce.

Some will couch the (very temporary and irrational) dip the market faced as a Chinese company managing to harm our markets by providing an unfairly priced product or some nonsense.

Open source AI is not guaranteed. We might still see protectionist bans against releasing models over a certain size and other irrational nonsense, and this has played into the kind of hysteria that allows that to happen.
For inference Nvidia has more significant competition than for training. See Groq, Google's TPU's etc.
> I thought it was relatively solved and problems are due to underprovisioning.

You are right, ~universally so. However, the author is trying his middle-school best to convince us otherwise.

While bandwidth upgrades (ex: d/u to broadband) have increased the amount of information transmitted, this change will actually make the internet feel faster for once.

What made my home internet feel faster was going from cable to fiber. Because fiber is faster in capacity and latency. And my fiber is faster all the time, not just "for once".
Probably optimism talking, but I'd put Boom's chances of bringing to market an airliner capable of supersonic travel at equal-to-first-class-ticket-prices at 60%.

Now I'd put their wilder hopes of eventually taking over the subsonic economy market at considerably below 1%.

But I'm hopeful for that $5-10k ticket to London within the next twenty years.
Thomas Sowell definitively disproved the idea that disparate outcomes are ipso facto proof of discrimination. It's past time this line of jurisprudence was put to bed.
> Surely that's only possible when you have a large barrier to entry?

As you grow bigger, you create barriers to entry where none existed before, whether intentionally or unintentionally.
Debian Testing might be more of your tempo if you don't like the age of packages in Stable
We tested DeepSeek. Here's what you need to know
> Rather OpenAI stole from individual artists and YouTube and other social media

"stole"?

They consumed publicly available material on the Internet

I am no fan of these billionaire capitalists and their henchpersons but condem them for their multitude of sins.

Consuming publicly available Internet resources is not one of them. IMO
OpenAI coping so hard
I haven't used Java much in the last few years but it was pretty straight forward with Maven with the slight caveat you use an IDE to do everything.

Python isn't too bad now-a-days with poetry although the repo spec could use more constraints
I am guessing their point is that pencils can be used to create deceptive content.

Beyond that, I don't see any similarity. In a pencil, all of the "intelligence" is offloaded onto the user. With AI, the company providing the service is playing a more substantial role.
California Dept. of Finance panicking.
Do you have a magic pencil that can do all this without human involvement?
And that strategy, notably, does not include camps or asylums for people like you and me.
interesting perspective. Care to elaborate a bit more? Where you in the design department? Why did they put up chinese walls? Was it to enable the foundry to have other customers other than Intel designs? Why did you have similar type of equipment? Were you also manufacturing chipsets? If so, why didn't they expand your division to become a foundry?
IMHO, there are strategies that could extend this approach to many other domains.

I was discussing this idea (along with a small prototype) with a prominent symbolic AI researcher who also agrees, and thinks that with the emergence of RL as a viable training method for LLMs, it might be possible to pursue neuro-symbolic learning at a large scale.

Current systems are impressive, but reasoning is too fragile to trust them. They fall into obvious logical and statistical fallacies that are evident to a layperson.
They’re already running a plant in Arizona, and I heard it’s going really well. It won’t matter.
Not if the problem as written is "does this code compile", which is still a useful stepping stone for some workflows. Yours is certainly a more useful query in most cases but repositioning or re-scoping the original question can still lead to a net win.
Except the US has only contempt for anything south of Texas. Perhaps "western countries" will be reduced to US and Canada.

Many countries in Latin America have better relations and more robust trade partnerships with China.

As for the EU, I think it will be great for it to shed its reliance on the US, and act more independently from it.
Also the OS is copying Apple, making it worse.
Depends on a bunch of factors, e.g. which booster, which variant, the person, and the relationship between dependent and independent variables in different risks from both COVID and the vaccine, but for the peak of the pandemic, the answer is no -- the risk of catching COVID was so large and our treatment wasn't honed in that you'd have to assume a pretty high rate of serious unknown long-term issues for it to come out net negative in most cases. Once we got past the peak and with particular variants where the boosters were less effective, that changed (and that was also when you found out that some people's belief in the vaccine was ultimately religious in nature).

There weren't a lot of people trying to model that risk, but of the ones who were the vaccine looked pretty compelling as a better bet for the whole of the peak pandemic. And maybe it turns out that 15 years out from the vaccine everyone suddenly dies! But the expected value there is highly unlikely.
there have been. we really don't talk about those.
pelosi's husband sold millions in nvidia stock https://www.newsweek.com/nancy-pelosi-sells-nvidia-stock-wee...
Boy who stole test papers complains about child copying his answers.
The humor/hypocrisy of the situation aside, it does seem to be true that OpenAI is consistently the one coming up with new ideas first (GPT 4, o1, 4o-style multimodality, voice chat, DALL-E, …) and then other companies reproduce their work, and get more credit because they actually publish the research

I claim one just can't put the humor/hypocrisy aside that easily.

What OpenAI did with the release of ChatGPT is productize research that was open and ongoing with Deepmind and other leading at least as much. And everything after that was an extension of the basic approach - improved, expanded but ultimately the same sort of beast. One might even say the situation of OpenAI to DeepMind was like Apple to Xerox. Productizing is nothing to sneeze at - it requires creativity and work to productize basic research. But naturally get end-users who consider the productizers the "fountain heads", who overestimate the productizers because products are all they see.
This reminds me of a (probably apocryphal) story about fast food chains that made the rounds decades ago: McDonald's invests tons of time into finding the best real estate for new stores; Burger King just opens stores near McDonalds!
Thank you for all the work you guys do. The Arrow ecosystem is just absolutely incredible.

My few gripes related to interop with duckdb are related to Arrow scanning/pushdowns. And this extends to interop with other projects like pyiceberg too.

Registering an Arrow Dataset (or pyiceberg scan) as a "duckdb relation" (virtual view) is still a little problematic. Querying these "relations" does not always result in an optimal outcome.

For Arrow datasets, you can intercept the duckdb pushdown, but duckdb will have already "optimized" the plan to its liking, and any scanning restrictions that may have been more advantageous based on the nuances of the dataset might have been lost. Eg:

WHERE A IN (3, 5, 7)

is presented to the Arrow scanner (pushdown) as "A is between 3 and 7 inclusive" (https://duckdb.org/docs/guides/performance/indexing.html#zon...).

Perhaps in a similar way, turning an pyiceberg scan into a relation for duckdb effectively takes the entire scan and creates an Arrow Table rather than some kind of pushdown/"scan plan" for duckdb to potentially make more efficient with its READ_PARQUET() functionality.

Most of this is probably dependent on duckdb development, but all of the incredible interop work done across communities/ecosystems so far gives me a lot of confidence that these will soon be matters of the past.
Awesome - we'd love to have our CEO/CTO chat with you and your team if you're interested. Shoot me a note at mike.bilodeau @ baseten.co and I'll make it happen!
In my experience, the biggest issue with US public schools is incompitent administrations. Those administrations ultimately report to school boards, whose members are selected via public election.

But how many people can name even a single person on their local school board? How many can account for what that person's platforms are regarding the school? How many can account for anything that person has done while on the board? Where would you even go to find out what your local school board is doing? How many of your school board members have observed the day-to-day goings on of your local schools?

The system seems fundamentally broken to me. It's no wonder school administrations are so incompitent - especially in larger districts.
I was a member of an STC chapter in grad school and I went to a bunch of meetings and networking events hoping to get some practical career advice from more experienced technical writers.

At no point did it feel like anyone in the org could (or would) actually help me in the transition from grad school to technical writing work. It was a really disheartening experience. It does not surprise me that membership has waned over the years.
Earlier today I read a reddit comment[1] about a guy who tried running the quantized version from unsloth[2] on 4xH100 and the results was underwhelming (it ended up costing $137 per 1 million tokens).

Any idea of what they're doing wrong?

[1]: https://www.reddit.com/r/LocalLLaMA/comments/1icphqa/how_to_...

[2]: https://unsloth.ai/blog/deepseekr1-dynamic
every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now
There's levels to that, it's not an argument that can be dismissed that easily.
I usually ignore people who say this because it guarantees they're not actually doing anything meaningful with these models and just want to squibble over semantics from the sideline...

https://huggingface.co/blog/open-r1

They didn't just toss model weights over a fence, they shared exactly how to do what they did. They made a meaningful contribution that people are replicating with other models readily.
Maybe so. Thank you for the perspective.

My going hypothesis is that the US cannot politically solve their Modern Monetary Theory national debt blackhole problem and is therefore itching for WW3 to be able to "reset" debts when the dust settles. Taiwan seems like a good opportunity for that, but maybe there is some sanity left in Congress.
That's only true if you assume that O1 synthetic data sets are much better than any other (comparably sized) opensource model.

It's not apparently obvious to me that that is the case.

Ie. do you need a SOTA model to produce a new SOTA model?
> Just kidding, we all know it's stolen.

This thief has small pockets, about 500x smaller than the "stolen" material. Where to stash all that?
They should have redesigned it. Spending so much money on a fundamentally inferior design is pretty stupid.
US unipolar hegemony has averted an all-out war, but it has also been very bloody, or at least immiserating, for people at its periphery.

Multipolarity doesn't imply we go back to the 1910s. The idea would be to strengthen multilateral institutions that put a check on things like the World Wars.
While I disagree with Ed Zitron's opinion that we are at 'peak AI', I do completely agree with his other points, especially the points he was making that OpenAI and Anthropic had no incentives to make things more efficient and therefore they were just drinking from the money hose. If anything it is shocking that nobody had done what DeepSeek did earlier. Good for them.
Hey, OpenAI, so, you know that legal theory that is the entire basis of your argument that any of your products are legal? "Training AI on proprietary data is a use that doesn't require permission from the owner of the data"?

You might want to consider how it applies to this situation.
The main problem SDKs solve is providing a way to interact with an API without having to worry about nitty gritty details like serialization and deserialization, authentication, error handling, retries, connection and thread pooling, etc.

When you have a good SDK, interacting with an API feels like working with regular functions and objects in your chosen programming languages, so it _greatly_ simplifies integrating with an API.

Stainless's Series A announcement post might be a good one to read to get the gist: https://www.stainless.com/blog/stainless-series-a
I suppose that may be in fact why you don't have the money
My $100k line of credit against my house was approved today. I plan to pay off my house with the gains.
Just curious, does current LineageOS close these CVEs?

I know they pull firmware from disparate devices when possible.
Depends on the radius, as I understand it. It could have more or less gravity depending on that, but we don't know what the radius of this planet is yet.

Wolfram Alpha has a good calculator if you want to play around with this [0]

[0] https://www.wolframalpha.com/input?i=surface+gravity+calcula...
I was commenting specifically about on:change
Even though this has come from State Department...

https://www.state.gov/emergency-humanitarian-waiver-to-forei...

...someone in charge appears to be interpreting PEPFAR as not a "life saving" activity. Therefore, all data systems are still down.
Even electric signals are plenty fast enough. The issue as always is all the processing like modulation and demodulation. And ofc routing. Fibre does help with this as you can do longer distance without doing it.
That's already happened. Its well established now that the internet is tainted. After essentially ChatGPT's public release, a non-insignificant amount of internet content is not written by humans.
Ask HN: Currently, is it better to work on AI shovels or AI applications?
Typically, conventional wisdom has been to sell shovels during a gold rush. Except people aren't quite selling shovels as much as they are still inventing them. You could spend a year designing and manufacturing your shovels, only to have the guy next door come out with the Shovel-o-matic 2000 and render your work obsolete in an instant.

I would consider selling shovels to be anything that can be classified as "hard tech". No, hooking up a LLM to a wrapper doesn't count. That comes next.

I would split up applications into two distinct types: mini-shovels and end products.

As an example of a mini-shovel, consider a shop that sells toys. They have to manage and track their inventory. Someone spins up an AI system to automatically input sales data, update inventory, and place orders for low stock. That saves on staff time and costs. The person who made this AI system goes around selling it, primarily in a B2B fashion. Yes, this "application" helps the toy store a little, but it's not really serving the main purpose of getting people to buy toys.

The end product application is a little more obvious. A toy designer with no manufacturing skills uses AI to prototype complex 3D patterns, iterate, and come up with an ingenious mechanical design. He proceeds to sell that toy directly to customers (B2C) or to toy shops (B2B). The use of AI is irrelevant to the final paying customer.

Of course, it can be more nuanced. The inventory system IS the end product for the software designer. Mini-shovels can become obsolete in an instant, too, but they could have slightly deeper moats depending on industry inertia among an unsophisticated client base. The market scope and dynamics are also very different for B2B versus B2C.

I am seeing a lot of mini-shovel applications, the typical low-effort examples like building a AI-based financial advisor, a new AI-based writer assistant and so on. I assume that it's more difficult to tell how much AI is being used in end products (other than obvious AI art on printed shirts and such), especially if people have no incentive to talk about it.

I'm kind of thinking at the moment that both true shovels and quality end products are difficult. Mini-shovels are easy, and that's why they are a dime a dozen at the moment, but I'm not really seeing their value for creator or clients. I can see that you might be able to make a quick buck, though, if that's your thing.

So, what's your take HN? Shovels, mini-shovels, or end products?
> Those users aren't really what I would consider "people"

I think you want to edit that.

Apart from that piece of advice, I don’t see how your comment relates to mine, and have nothing to add.
It's not a sufficient criteria by itself, but where no better criteria is possible it would still produce better results in reinforcement learning than if the model has no reward for producing correctly compiling code vs code that failed to compile.
This was, I believe, the problem that Microsoft wanted to resolve with their gradual burndown of WSUS - a lot of shops (including one I used to work at) would selectively roll out updates based on whether they thought they were relevant, resulting in an explosion of configurations that Microsoft had absolutely never tested against, and naturally, a lot of breakage.
Hyundai unveils electric rickshaws "tailored to India's unique environment"
“Only 1 in ten signatories in deals with the devil doesn’t regret decision in hindsight”
Didn't that drive the 1950 & 1960s in the USA? Taxing the rich and giving to the working class to build bridges, schools, GI homes, ... Isn't that classified as the most stable economy in their history? The only decades where the stock market was stable?

Part of the hilarious hypocrisy of the modern Republican Party is that they long for the glory days of the 1950s...but are completely opposed to the tax policy required to pay for it or the economic and social policies to implement it.
They haven't updated those predictions publicly in a little while. Definitely a huge setback from Rolls-Royce dropping out, and putting together the coalition that's developing the Symphony engine. Latest I've seen on Twitter is Blake Scholl stating the first full-size engine core should be making thrust by end of 2025.

I'm guessing rollout realistically is more like 2029-2030... but even that is a tall order. Unless, of course, they're a lot farther ahead on Overture development generally than they've revealed.
Or use the bluesky link: https://bsky.app/profile/carrigmat.bsky.social/post/3lgsoqsx...
> The most promising idea is to use reasoning models to generate data, and then train our non-reasoning models with the reasoning-embedded data.

Why is it promising, aren’t you potentially amplifying AI biases and errors?
Per the technical report:

> The minimum deployment unit of the decoding stage consists of 40 nodes with 320 GPUs.

but realistically, >=671GB of VRAM to run at full precision on GPU, or >=131G VRAM to run the most heavily quantized version[1], or >=671GB RAM, and a dose of patience to run on CPU.

[1]: https://news.ycombinator.com/item?id=42850222
Old Lyme resident here, high school class of 82 - it was the younger siblings of my classmates who were getting "Lyme arthritis" starting in the late 70s or maybe 1980.

Like most of the populated Northeast, most the area was cleared farmland or grazing land until WWI or maybe WWII - all those stone walls in the middle of the woods used to be borders between open fields. I don't think there was an ecosystem that could support Lyme disease near where people lived until maybe the 70s - the cycle needs both deer and mice, in proximity to humans to get infected, and you're not going to get either of them on open land that's grazed or under cultivation.

So Lyme disease could have been around long before that, farther into the woods, and probably wouldn't have been noticed by people who had good enough doctors to figure out what was going on, or were healthy (rich?) enough for the symptoms of Lyme disease to be considered something out of the ordinary.

The fact that Lyme disease exists in Europe already seemed to indicate that it's been around a very long time, but this finding adds more evidence. For that matter, I wonder if it's yet another disease that was introduced to the Americas by Europeans?
It is, of course not going to produce a “child” model that more accurately predicts the underlying true distribution that the “parent” model was trying to. That is, it will not add anything new.

This is immediately obvious if you look at it through a statistical learning lens and not the mysticism crystal ball that many view NN’s through.
Not useless by any means, but if given the choice between somebody who interviewed the principal and a somebody else who's son actually attends, I'd prefer the latter every time. Wouldn't you?

Also, while I get your point, you're in the very unique position of informed non-users. That's a valuable category if you can access it, but it's the most difficult. (How can you determine which library non-attendants have done their research?)
Well there was also like 10-15 year period where Java was taken over by enterprise factoryClassFactoryFactoryClassFizzBuzz architectural cancer that really made things suck as a want to be user of the language.

If you just stuck to the core language and the JVM, it's kind of always been a nice place to code even if the language was changing glacially.
We're splitting hairs here.
Open up the ChatGPT window and have it do something nefarious without you touching the keyboard. Then these AG actions would make sense.
As opposed to the country openly supporting the ongoing genocide in Gaza, which would not be possible without its help?
The proper solution is to work with an editor that asks clarifying question not to rewrite the whole thing into something totally different.

For published work, if it's not worth editing then it's not worth reading (I would go further personally and say that most publish, edited and peer reviewed work, in your area of interest isn't worth reading anyways)

For unpublished work, like an email, ask the AI to translate the passage while maintaining style and tone. It will still flatten it, but not as much as the complete dogshit I read in the article.

Communication is a job requirement, faking it with AI is going to go about as well as someone faking programming skills. Not very!