Most people dislike Trump, Musk, and Vance. Musk is by far the least liked of the three.
People in the country very much dislike the status-quo, hence the dislike of the Democratic Party and voters going for ape-shit change over Biden.
I sincerely doubt working people will see their lives improves this term and change their mind on Trump; there wasn't a liberal shift to the right and we'll likely see a major reaction of everyone going to the left when they realize dismantling everything hurt everyone.
> Chrome is their project, they should be free to do whatever they want with it.
Google has a long history of "accidentally" breaking gmail on firefox and funneling users to Chrome back in the day. It's beyond stupid to argue they should be able to do whatever they want with their vertically integrated monopoly.
Like, if you want to dig holes in your own driveway sure whatever, but if you own all the roads in Detroit and you want to dig holes in them, then make a killing selling new tires and suspension repair a fair society wouldn't move out of Detroit, they'd fucking run you out of town.
Why be bitter at the people dealing with the shit, why not be angry at the people making the world shit? My company uses gmail so I'm forced to use it.
How is your company forcing you to use gmail any worse than your company forcing you to use outlook? Is it your company that is making the world shit, or google.
Indeed. I'd like to. Except Google also make it nigh impossible for anyone hosting their own email (the original-internet ideal) to get email into gmail reliably enough to be useful. I have my own address on my own domain, but can't rely on it (yes, DKIM and DMARC and SPF are properly set up) not to be marked "spam" for opaque reasons, so gmail remains my "main" address. It's a network-effect problem: once enough people are "captured", then everyone else is forced to join - or else be unable to participate.
It's a collective action problem: you'll have to persuade millions and millions of "normies", who have no idea what's going on, or what internet privacy is, or what's broken about the system, and who don't care to learn, and won't listen to us - or you'll have to impose regulation. Those are the choices. The second seems more possible than the first. Us nerds saying "walk away" is idealistic; we will, and always will, get squished, because the corps have the power and most folks won't (ever) care.
GP Post:
> My company uses gmail so I'm forced to use it.
Your post:
> Everyone dealing with gmail is doing so because they chose to.
No, it's clear that not everyone dealing with Gmail is doing so because they chose to. Repeating your incorrect statement does not make it correct.
Further, everyone has to deal with its impacts on the email ecosystem as it's practically impossible for somebody who works a 9-5 to run their own mail server that Gmail will deign to not only accept mail from but also successfully deliver it to its intended recipient.
So even if I never use Gmail I still have to deal with replies going to / coming from it.
GSuite/Workspace and consumer GMail is not the same thing in the slightest. They may use the same mail servers but that is about where the similarity ends.
I would recommend Google Workspace to any company because it gives them a ton of business productivity tools.
I would probably not recommend gmail as a users default personal email because frankly it's not that good.
The reality is most users have a Google account ans just use their Gmail account which is bundled.
Most of my circle which cares effectively use their Gmail account for sites that insist on it and never open that e-mail if they can get away with it.
True, and applies to many other things as well. Anyone claiming otherwise is shirking responsibility for their own actions. Every single sibling comment here suffers from this.
Arguments in the form of "other people do it, so I must also" are unpersuasive and pathetic.
Except for anyone whose employer requires them to use Google services, since Google Apps (or whatever they call it these days) is a hugely popular offering for central company email/contacts/calendar/office suite. And frankly, it's better than dealing with Outlook and its unrelenting AI slop machine advertising.
You don't own the roads in Detroit; the government owns most of them.
Gmail is not a government service. Google is free to make that work with only one browser, if they want.
You can't assert that Google must make Gmail work with any browser whatsoever, because that means supporting someone using Windows 95 with Internet Explorer 5.5.
I'm not going to waste my time explaining to you what a metaphor is, but I will say this Firefox was the dominant player in the 00's 2010's when they did this, not the 2% market share it is now.
Steve Jobs said something to the effect that he made maybe three CEO decisions a year. I mean, I think these are decisions like, "We're going to open our own line of Apple retail stores", but, still.
Being a CEO isn’t all that different from being a parent of a child from the POV of impactful decisions.
How many critical “parental decisions” have you made in the past week? Probably very few (if any), but surely you did a lot of reinforcement of prior decisions that had already been made, enforcing rules that were already set, making sure things that were scheduled were completed, etc.
Important jobs don’t always mean constantly making important decisions. Following through and executing on things after they’re decided is the hard part.
The banter is actually quite easy to automate. You can hire a human to play golf for a small fraction of what the CEOs get paid, and then it's best of both worlds.
Is it? Take a look at the bot accounts filling up social media (the non-obvious ones). It wouldn't seem to hard to make one that makes 2am posts about '[next product] feels like real AGI' or tells stock analysts that their questions are boring on an earnings call, which is apparently what rockstar CEOs do.
Sneers aside, I think one common mis-assumption is that the difficulty of automating a task depends on how difficult it feels to humans. My hinge is that it mostly depends on the availability of training data. That would mean that all the public-facing aspects of being a CEO should by definition be easy to automate, while all the non-public stuff (also a pretty important part of being a CEO, I'd assume) should be hard.
That's the plan for every other federal service. For public land in particular there's an extra fun bonus step of selling the land to be exploited fully. Look at the Secretary of Interior's record in North Dakota.
I'm not sure how that relates to original comment. Do you mean you want everything that is or could be better than American technology banned/destroyed so we stay the best...?
Like, any global hegemony will be increasingly corrupt given the power that gives, IMO.
Also, DeepSeek is allegedly... better? So saying they just copied ClosedAI isn't really sufficient of an answer. Seems to be just bluster because the US Govt would probably accept any excuse to ban it, see TikTok.
It’s not better. In most of my tests (C++/QT code) it just runs out of context before it can really do anything. And the output is very bad - it mashes together the header and cpp file. The reasoning output is fun to look at and occasionally useful though.
The max token output is only 8K (32K thinking tokens). O1 is 128k, which is far more useful, and it doesn’t get stuck like R1 does.
The hype around the DeepSeek release is insane and I’m starting to really doubt their numbers.
Is this a local run of one of the smaller models and/or other-models-distilled-with-r1, or are you using their Chat interface?
I've also compared o1 and (online-hosted) r1 on Qt/C++ code, being a KDE Plasma dev, and my impression so far was that the output is roughly on par. I've given both models some tricky tasks about dark corners of the meta-object system in crafting classes etc. and they came up with generally the same sort of suggestions and implementations.
I do appreciate that "asking about gotchas with few definitive solutions, even if they require some perspective" and "rote day-to-day coding ops" are very different benchmarks due to how things are represented in the training data corpus, though.
I use it through Kagi Assistant which has the proper R1 model through Together.ai/Fireworks.ai
My standard test is to ask the model to write a QSyntaxHighlighter subclass that uses TreeSitter to implement syntax highlighting. O1 can do it after a few iterations, but R1’s output has been a mess. That said, its thought process revealed a few issues that I then fixed in my canonical implementation.
Thanks for adding detail! My prompts have been very in-the-bubble-of-Qt I'd say, less so about mashing together Qt and something else, which I agree is a good real-world test case.
I haven’t had the chance to try it out with R1 yet but if you implement a debugger class that screenshots the widget/QML element, dumps its metadata like GammaRay, and includes the source, you can feed that context into Sonnet and o1. They are scarily good at identifying bugs and making modifications if you include all that context (although you have to be selective with what metadata you include. I usually just dump a few things like properties, bindings, signals, etc).
R1 is trained for a context length of 128K. Where are you getting 8K/32K? The model doesn't distinguish "thinking" tokens and "output" tokens, so this must be some specific API limitations.
> max_tokens：The maximum length of the final response after the CoT output is completed, defaulting to 4K, with a maximum of 8K. Note that the CoT output can reach up to 32K tokens, and the parameter to control the CoT length (reasoning_effort) will be available soon. [1]
I’m using it through Kagi which doesn’t use Deepseek’s official API [1]. That limitation from the docs seems to be everywhere.
In practice I don’t think anyone can economically host the whole model plus the kv cache for the entire context size of 128k (and I’m skeptical of Deepseek’s claims now anyway).
Edit: a Kagi team member just said on Discord that they’ll be increasing max tokens next release
He's just repeating a lot of disinformation that has been released about deepseek in the last few days. People who took the time to test DeepSeek models know that the results have the same or better quality for coding tasks.
Benchmarks are great to have but individual/org experiences on specific codebases still matter tremendously.
If an org consistently finds one model performs worse on their corpus than another, they aren't going to keep using it because it ranks higher in some set of benchmarks.
But you should also be very wary of these kind of anecdotes, and this thread highlights exactly why. That commenter says in another comment (https://news.ycombinator.com/item?id=42866350) that the token limitation that he is complaining about has actually nothing to do with DeepSeek's model or their API, but is a consequence of an artificial limit that Kagi imposes. In other words, his conclusion about DeepSeek is completely unwarranted.
It mashed the header and C++ file together, which is egregiously bad in the context of QT. This isn’t a new library, it’s been around for almost thirty years. Max token sizes have nothing to do with that.
I invite anyone to post a chat transcript showing a successful run of R1 against this prompt (and please tell me which API/service it came from so I can go use it too!)
I wasn't suggesting using the anecdotes of others to make a decision.
I'm talking about individuals and organizations making a decision on whether or not to use a model based on their own testing. That's what ultimately matters here.
It's not great at super-complex tasks due to limited context, but it's quite a good "junior intern that has memorized the Internet." Local deepseek-r1 on my laptop (M1 w/64GiB RAM) can answer about any question I can throw at it... as long as it's not something on China's censored list. :)
Thanks for saying this, I thought I was insane, DeepSeek is kinda bad. I guess it’s impressive all things considered but in absolute terms it’s not great.
I have run personal tests and the results are at least as good as I get from OpenAI. Smarter people have also reached the same conclusion. Of course you can find contrary datapoints, but it doesn't change the big picture.
To be fair, it's amazing by the standards of six months ago. The only models that beat it are o1, the latest gemini models and (for some things) sonnet 3.6
It’s definitely not all hype, it really is a breakthrough for open source reasoning models. I don’t mean to diminish their contribution, especially since being able to read the reasoning output is a very interesting new modality (for lack of a better word) for me as a developer.
It’s just not as impressive as people make it out to be. It might be better than o1 on Python or Javascript thats all over the training data, but o1 is overwhelmingly better at anything outside the happy path.
> An AACS encryption key (09 F9 11 02 9D 74 E3 5B D8 41 56 C5 63 56 88 C0) that came to prominence in May 2007 is an example of a number claimed to be a secret, and whose publication or inappropriate possession is claimed to be illegal in the United States.
This is a silly take for anyone in tech. Any binary sequence is a number. Any information can be, for practical purposes, rendered in binary [1].
Getting worked up about restrictions on numbers works as a meme, for the masses, because it sounds silly, but is tantamount to technically arguing against privacy, confidentiality, the concept of national secrets, IP as a whole, et cetera.
> Any piece of digital information is representable as a number; consequently, if communicating a specific set of information is illegal in some way, then the number may be illegal as well.
There is thought-stopping satire and thought-provoking satire. Much of it depends on the context. I’m not getting the latter from a “USA land of the ‘free’” comment.
> It depends on where you live. In many places, collecting rainwater is completely legal and even encouraged, but some regions have regulations or restrictions.
United States: Most states allow rainwater collection, but some have restrictions on how much you can collect or how it can be used. For example, Colorado has limits on the amount of rainwater homeowners can store.
Australia: Generally legal and encouraged, with many homes using rainwater tanks.
UK & Canada: Legal with few restrictions.
India & Many Other Countries: Often encouraged due to water scarcity.
I think so; I joined Reddit when it was in tech news as people left Digg after the big redesign. I'm not sure when the exodus started. I left Fark over the hd-dvd mess.
In both cases, legality depends entirely on repercussions, i.e. if there's someone to enforce the ban. I suspect that in the "illegal numbers" case there might be.
It's not open source. The provide the model and the weights, but not the source code and, crucially, the training data. As long as LLM makers don't provide the training data (and they never will, because then they will be admitting to stealing), LLMs are never going to be open source.
(a) You have everything you need to be able to re-create something, and at any step of the process change it.
(b) You have broad permissions how to put the result to use.
The "open source" models from both Meta so far fail either both or one of these checks (Meta's fails both). We should resist the dilution of the term open source to the point where it means nothing useful.
Agreed, but the "connotations don't match" is mostly because the folks who chose to call it open source wanted the marketing benefits of doing so. Otherwise it'd match pretty well.
At the risk of being called rms, no, that's not what open source means. Open source just means you have access to the source code. Which you do. Code that is open source but restrictively licensed is still open source.
That's why terms like "libre" were born to describe certain kinds of software. And that's what you're describing.
This is a debate that started, like, twenty years ago or something when we started getting big code projects that were open source but encumbered by patents so that they couldn't be redistributed, but could still be read and modified for internal use.
> Open source just means you have access to the source code.
That's https://en.wikipedia.org/wiki/Source-available_software , not 'open source'. The latter was specifically coined [1] as a way to talk about "free software" (with its freedom connotations) without the price connotations:
The argument was as follows: those new to the term "free software" assume it is referring to the price. Oldtimers must then launch into an explanation, usually given as follows: "We mean free as in freedom, not free as in beer." At this point, a discussion on software has turned into one about the price of an alcoholic beverage. The problem was not that explaining the meaning is impossible—the problem was that the name for an important idea should not be so confusing to newcomers. A clearer term was needed. No political issues were raised regarding the free software term; the issue was its lack of clarity to those new to the concept.
It's common for terms to have a more specific meaning when combined with other terms. "Open source" has had a specific meaning now for decades, which goes beyond "you can see the source" to, among other things, "you're allowed to it without restriction".
I don't know why you've been downvoted. This is a 100% correct history. "Open source" was specifically coined as a synonym to "free software", and has always been used that way.
> Open source just means you have access to the source code. Which you do.
No, they also fail even that test. Neither Meta nor DeepSeek have released the source code of their training pipeline or anything like that. There's very little literal "source code" in any of these releases at all.
What you can get from them is the model weights, which for the purpose of this discussion, is very similar to compiler binary executable output you cannot easily reverse, which is what open source seeks to address. In the case of Meta, this comes with additional usage limitations on how you may put them to use.
As a sibling comment said, this is basically "freeware" (with asterisks) but has nothing to do with open source, either according to RMS or OSI.
> This is a debate that started, like, twenty years ago
For the record, I do appreciate the distinction. This isn't meant as an argument from authority at all, but I've been an active open source (and free software) developer for close to those 20 years, am on the board of one of the larger FOSS orgs, and most households have a few copies of FOSS code I've written running. It's also why I care! :-)
The weights, which are part of the source, are open. Now you are arguing it not being open source because they don't provide the source for that part of the source. If you follow that reasoning you can ad infinitum claim the absence of sources since every source originates from something.
The source is the training data and the code used to turn the training data _into_ the weights. Thus GP is correct, the weights are more akin to a binary from a traditional compiler.
To me this 'source' requirement does not make sense. It is not that you bring training data and the application together and press a train button, there's much more actions involved.
Also the training data is of a massive amount.
Additionally, what about human in the loop training, do you deliver humans as part of the source?
> they also fail even that test. Neither Meta nor DeepSeek have released the source code of the
This debate is over and makes the open source community look silly. Open model and weights is, practically speaking, open source for LLMs.
I have tremendous respect for FOSS and those who build and maintain it. But arguing for open training data means only toy models can practically exist. As a result, the practical definition will prevail. And if the only people putting forward a practical definition are Meta et al, this is what you get: source available.
I'm not arguing for open training data BTW, and the problem is exactly this sort of myopic focus on the concerns of the AI community and the benefits of open-washing marketing.
Completely, fully breaking the meaning of the term "open source" is causing collateral damage outside the AI topic, that's where it really hurts. The open source principle is still useful and necessary, and we need words to communicate about it and raise correct expectations and apply correct standards. As a dev you very likely don't want to live in a tech environment where we regress on this.
It's not "source available" either. There's no source. It's freeware.
"I can download it and run it" isn't open source.
I'm actually not too worried that people won't eventually re-discover the same needs that open source originally discovered, but it's pretty lame if we lose a whole bunch of time and effort to re-learn some lessons yet again.
> it's pretty lame if we lose a whole bunch of time and effort to re-learn some lessons yet again
We need to relearn because we need a different definition for LLMs. One that works in practice, not just at the peripheries.
Maybe we can have FOSS LLMs vs open-source ones, like we do with software licenses. The former refers to the hardcore definition. The latter the practical (and widely used) one.
Sure, I don't disagree. I fully understand the open-weights folks looking for a word to communicate their approach and its benefits, and I support them in doing so. It's just a shame they picked this one in - and that's giving folks a lot of benefit of the doubt - a snap judgement.
> Maybe we can have FOSS LLMs vs open-source ones, like we do with software licenses.
Why not just call them freeware LLMs, which would be much more accurate?
There's nothing "hardcore" or "zealot" about not calling these open source LLMs because there's just ... absolutely nothing there that you call open source in any way. We don't call any other freeware "open source" for being a free download with a limited use license.
This is just "we chose a word to communicate we are different from the other guys". In games, they chose to call it "free to play (f2p)" when addressing a similar issue (but it's also not a great fit since f2p games usually have a server dependency).
> Why not just call them freeware LLMs, which would be much more accurate?
Most of the public is unfamiliar with the term. And with some of the FOSS community arguing for open training data, it was easy to overrule them and take the term.
Most of the public is also unfamiliar with the term open source, and I'm not sure they did themselves any favors by picking one that invites far more questions and needs for explanation. In that sense, it may have accomplished little but its harmful effects.
I get your overall take is "this is just how things go in language", but you can escalate that non-caring perspective all the way to entropy and the heat death of the universe, and I guess I prefer being an element that creates some structure in things, however fleeting.
The only practical and widely used definition of open source is the one known as the Open Source Definition published by the OSI.
The set of free/libre licenses (as defined by the FSF) is almost identical to the set of open sources licenses (as defined by the OSI).
The debate within FOSS communities has been between copyleft licenses like the GPL, and permissive licenses like the MIT licence. Both copyleft and permissive licenses are considered free/libre by the FSF, and both of them are considered open source by the OSI.
People say this, but when it comes to AI models, the training data is not owned by these companies/groups, so it cannot be "open sourced" in any sense. And the training code is basically accessing that training data that cannot be open sourced, therefore it also cannot be shared. So the full open source model you wish to have can only provide subpar results.
They could easily list the data used though.
These datasets are mostly known and floating around.
When they are constructed, instructions for replication could be provided too
But I think my argument still stands though? Users can run Deepseek locally, so unless the US Gov't wants to reach for book burning levels or idiocy, there is not really a feasible way to ban the American public of running DeepSeek, no?
Yes, your argument still stands. But I think it's important to stand firm that the term "open source" is not a good label for what these "freeware" LLMs are.
There was an executive order passed by the previous administration that make using anything with more than 10 billion parameters illegal and punishable by government force if done without authorization. Of course like most government regulations (even though this is not a regulation, it is an executive action) the point is not to stop the behavior but instead to create a system where everyone breaks the regulation constantly so that if anyone rocks the boat they can be indicted/charged and dealt with.
>(k) The term “dual-use foundation model” means an AI model that is trained on broad data; generally uses self-supervision; contains at least tens of billions of parameters; is applicable across a wide range of contexts; and that exhibits, or could be easily modified to exhibit, high levels of performance at tasks that pose a serious risk to security, national economic security, national public health or safety, or any combination of those matters, such as by: ...
That order does not "make using anything with more than 10 billion parameters illegal and punishable by government force if done without authorization".
It orders the Secretary of Commerce to "solicit input from the private sector, academia, civil society, and other stakeholders through a public consultation process on potential risks, benefits, other implications, and appropriate policy and regulatory approaches related to dual-use foundation models for which the model weights are widely available".
Many regulations are created by executive action, without input from Congress. The Council on Environmental Quality, created by the National Environmental Policy Act, has the power to issue it's own regulations. Executive Orders can function similarly and the executive can order rulemaking bodies to create and remove regulations, though there is a judicial effort to restrict this kind of policymaking and return regulatory power back to Congress.
There’s an effort to restrict certain regulatory rule-making where it’s ideologically convenient, but it isn’t “returning” regulatory power. That rulemaking authority isn’t derived by some bullshit executive order, but by Federal law, as implemented by congress.
Congress has never ceded power to anyone. They wield legislative authority and power of the purse, and wield it as they see fit. The special interests campaigning about this are extreme reactionaries whose stated purpose is to make government ineffective.
If I'm no wrong wasn't PGP encryption once illegal to export ?
Not quite the same but the government has a nice habit of feeling like they can bad the export of research.
Add PS1 too. The US government banned sale of PlayStation to China because the PLA would apparently have access to cutting edge chips for their missiles
But that's not the goal, the goal is to protect the "intelectual property" only to American companies. Countries not in the "friends list" cannot sell products in that area without suffering repercussions. That's how the US has maintained technological dominance in some areas by restricting what other countries can do.
People in the country very much dislike the status-quo, hence the dislike of the Democratic Party and voters going for ape-shit change over Biden.
I sincerely doubt working people will see their lives improves this term and change their mind on Trump; there wasn't a liberal shift to the right and we'll likely see a major reaction of everyone going to the left when they realize dismantling everything hurt everyone.