And, there is the issue of data poisoning from untrusted nodes. I've almost cracked that last issue with a self-healing checkpointed rollback system that doesn't have to throw out anything that follows the corrupt datum.

But, I'm just one person with an idea and I don't have infinite funds to make this happen. This isn't a small project.

Maybe there would be interest in something like this, now that entire frontier labs are being banned from making further progress.

The total power of all GPUs on the planet dwarf their capabilities, if we had a way to harness them in a distributed way efficiently. We wouldn't be able to train a Fable as fast as them, but eventually having access is better than never having access.

So we must build and adopt frameworks that allow individuals to share resources to run SOTA models in a distributed manner. That way they will also be non-censorable by governments.

Also The only way to prevent that one entity weaponizes it, is by giving EVERYONE access to it.

I have never understood the willingness to make the functioning of or development of a product so completely dependent on the secret sauce of one of two big unprofitable, inscrutable startups.

It really defies sensible engineering principles to do that. So I was never going to do it. I'm exploring AI now but because I have decided that open weights make it a good use of my time.

It's bad enough that any given business often ends up beholden to a single payment platform and the policies of two US credit card providers.

I guess it is the freelancer in me but I always feel nervous when I am asked to put so much energy into studying or learning someone's product, rather than the underlying technology. I still remember the days when Microsoft was pretty much lobbying academic departments with promises of access to the NT source code. I remember a senior figure in our own saying that Linux was a sideshow and access to NT would make us relevant.

More control over destiny is always necessary, and I remind myself and others that the "state of the art" is behind the "cutting edge". Progress is made at the cutting edge, but there is risk of damage. Engineering should focus on building on the state of the art, not on hitching a ride on someone else's progress.

What we should be saying is: We want a public, community-ran project that does pretraining and training collectively. This means working on a training corpus in public and somehow coordinating the training work.

This is a complete change of what the term means, It's like how people conflate piracy with theft. Two different things, use different words. Free weights, inference code and chat template is very different from a community-ran LLM project.

You have either VC funded models looking for a return on investment, or CCP funded models looking to solidify authoritarian "model Chinese society".

Maybe there are some university 4B models, but I doubt those will carry far.

Because of this, I think it might not be possible to have AI *only* open-weight; major players like OpenAI, Anthropic, Google will likely stay for good, with better models than open-source versions.

I think it might look something like Photoshop & GIMP, with Photoshop being a frontier lab, and GIMP being the open-weight model. GIMP is decent for many different image editing workflows, but Photoshop is just better.

I would definitely prefer to have an open-weight model better than frontier labs'. Though I don't think it's possible.

Being Open Source (tm) will not protect you from the government/others imposing controls on your silicon or what it is allowed to do, which is already happening around the world.

Even having the models be open source won't fix the regulation or economic incentives. Which is not something you can compress into a couple of paragraphs.

AI is civilizational infrastructure and it needs civilizational solutions. Not just source.

Dependents of an AI-megacorp for our "facts"? Our software? Our work?

It's possible these companies will become everyone's boss, and will dictate to everyone what everyone is allowed to work on, think, say, do, believe, etc.

Before Big Tech springs that trap, we must support and divert resources to open models.

It doesn't really matter for most use cases, because the way AI is working is capability saturation. https://www.delanceyukschoolschesschallenge.com/the-rising-t...

The only exception to this is fields that are inherently adversarial (to nature or others) and an edge relative to competition matters.

Sure, we can do research to bring improvements to open weights models, but it's the same thing: it's either open source or it won't benefit the general public nearly as much.

From what I could tell from the very little time that I had to interact with it, it's instruction following seemed more consistent

The other thing that comes to mind is a lot of people commented on how driven it was, so I'm wondering whether figuring out how to keep existing models looping on task might actually be quite a big shift in capability

Turned out both assumptions were wrong. You couldn't trust sama to turn this into open source, the Chinese did. Elon never.

And we couldn't see demis take over as expected, probably blocked by Google buerocracy.

to me Open Source, like Free Software, is something i can run on my own computer. any AI system that runs on a computer that i do not control is by my definition not Open Source.

so how then can Open Source AI win? it can't even compete. even if we collect enough money and create a dedicated Open Source organization to build and run a community owned AI datacenter, how does that help?

so what exactly is the demand here?

If you really want specific open source {LLM, LMM, research, harness, whatever} groups to win over closed source counterparts, you may show your care by trying open source solutions first when solving problems. And if they're really capable, award them with contributions or something.

A loooooot of work to be done for the above to happen

My bet is that once cost-efficiency becomes a priority, we will figure out ways to get away from the expensive GPU infrastructure on figure out how to architect models for CPUs. I still remember that Microsoft paper about ternary weights.

To make any agent "good", there are two components: the model and the harness. Very few companies can train models, but anyone can build a harness. How much does the harness matter? Can I build a harness that's good enough that I can use open source models with opus level performance? That's the question I've been trying to answer by building better harnesses. None of the existing frameworks have the functionality I need to build a good harness. The features I need are language-level... and so I started building a language called Agency[0].

It's been six months and its going well. Some of the things Agency can do are wild:

- It can pause and serialize execution at any point, making HITL easy

- It has some neat safety capabilities such as handlers[1] and PFA[2]

- You can bundle up any agent as an HTTP or MCP server[3]

- I'm now working on a built-in optimizer to optimize agents (think DSPy).

Obviously, it's a huge undertaking, but having worked with the Agency for six months, I can't imagine going back to another framework. It makes things so easy. I'm working on its built-in agent now [4]. My goal it to get it to be as good as Claude Code, but using open source models. It's still early days, lots of rough edges, but if this sort of thing interests you, I'd love to have a few more people test it out.

[0] https://agency-lang.com

[1] https://agency-lang.com/guide/handlers.html

[2] https://agency-lang.com/guide/partial-application.html

[3] https://agency-lang.com/cli/serve.html

[4] https://github.com/egonSchiele/agency-lang/blob/main/package...

So the real solution you're looking for is technology that can't be arbitrarily gatekept by a sovereign nation.

These are still very very (and very) early days of the modern AI and there are so many changes that are gonna happen. It's possible that all the frontier labs of today won't exist in a few years.

I'm not an expert in LLMs so it's hard to understand how much are we lacking, is it just the compute and thinking strategies / parallel chains, or something specific architecturally. But I feel there's value there and I haven't seen anything like it available so far.

I always wondered if 1000 1M parameter models fine-tuned to specific tasks with a small router could perform as well as 100B models.

And I know this is roughly how MoE works, but current MoE models still require training the model as a whole, and big players don’t have an incentive to change that.

But OpenSource community does…

I feel extremely strongly that a future in which most companies depend on one or two large AI-megacorps is going to lead to excessive rent seeking sooner or later.

I remain positive that the long term steady state will consist of proprietary models, -but- with open source AI models statistically close.

If compute keeps growing the relative cost of training current frontier models will decrease. An open source Fable/Mythos model simply seems inevitable.

It is only fair, give that LLMs are enabled by human generated content from the Internet, that they give it back!

One day an open source model reaches "good enough" level. Maybe around the level the current frontier has and most people will use that

Got a bit more than 1B tokens for $10, it's exceptionally fast, it was able to fix/implement things that 5.5 xhigh struggled with, without trying to act like my best friend or do that coy "undersell the ideal end result so that it can later overshoot it and claim a great success" bullshit.

E: miss me with the "but China" BS, everything I've experienced while using this model has convinced me they are earnestly more concerned with doing the right thing than Anthropic could ever pretend to be. And if you want to ask it questions about Mao, you can go download the weights and spend mid-five-figures to fine tune that out.

What’s the world in which frontier model performance is open source? What does that look like? What’s a sensible business model that makes this sustainable? What’s a sensible regulatory framework that doesn’t hamstring AI progress?

Everyone is so enamored with these Chinese lab models like deepseek and qwen and GLM but they exist in a world where the top performance is still claimed by closed source models. These are not developed out of any benevolent commitment to the principles laid out in this article. A world in which OSS is the frontier and its development is controlled and funded by government subsidies of an autocratic government is not reassuring. You can inspect weights but good luck getting the cat back in the bag in terms of capabilities, safeguards, value system, bias, nerfing if it smells American business use cases.

Deepseek was such a darling but guess what, it’s now raising money — 300M at 10 billion valuation. OSS development isn’t sustainable as a business model and in a world where it costs a few hundred million to develop a frontier model, you need a strong business model, or you need strong state subsidies and incentives which introduce a billion new problems.

the most sensible economic picture of OSS models already exist. Commoditize your complement, passion projects for a hedge fund. These are unsustainable and exist at the pleasure of the business or the founder.

Just your your natural born intelligence..? It's worked for the past 10k+ years, I'm sure it will work for some time longer

if it can access private data it will necessarily have more power.

Does he mean that the _best model_ should be an open source one (eg: today, something better than Fable 5), or just that open source models should be the default choice for most task?

The former seems an impossibility, closed labs can work off of open and their own closed research. Closed source will always be better. Well, at least until some late-stage enshittification dynamics cause the providers to hobble them.

The latter, becoming a default, not so much. But considering the deep-rooted nature of (for instance) Google, it certainly won't be a walk in the park. This seems to be a similar hurdle as dethroning Chrome as the default browser.

For the average ChatGPT user, I surmise that open-source models are already capable enough. Most people I know who use it (me included) are not paying for it, they are routed to the cheaper models.

What's needed here is everything else other than the model to be in place. Which is to say there isn't a sufficiently good open source ChatGPT app, every open source option requires more fiddling than the ChatGPT app.

No precedent comes to mind for non-tech-user software that is open source and also a default choice. The limitation is rarely from the core-tech capability; core-tech is often the same as what closed source uses.

Open source AI should and will get better for sure (including better defined first), but the state will have the power over AI never the less.

If you don't like govt's AI policy or the people making those policies, go fix that, don't act like you can avoid them.

For Chinese: saying "Open source AI must win" sounds like singing "L'Internationale, sera le genre humain". The reality is Open Source AI will be over the moment US competitive pressure gone.

For rest of world: there's no real AI for you so far, go work on it or be a citizen of US&A or China.

Ever since a Chinese firm released DeepSeek I immediately came to the realization that any US tech firm "owning" AI is simply not going to happen. China will make sure of it. It's in their national security interest not to let that happen.

From the POV of geopolitics, IMHO the US shot itself in the foot by banning the export of the best chips to China. The US also somehow has the power to prevent a Dutch company (ASML) from selling to China too. That makes a little more sense to ban but the combination of banning EUV exports AND banning the best chips sowed the seeds for the destruction of all of this.

By banning chip sales, the US inadvertently created a captive market for Chinese chips with Chinese companies. If there were no chip ban, Chinese companies probably would've bought US chips. But they can't. So they can only buy from Huawei and SMEE (indirectly). The US forced China to realize it was in their national security interest to copy the best lithography and, by extension, the best AI chips.

So DeepSeek was reportedly developed on either older NVidia hardware or smuggled newer NVidia hardware but that won't last either. At some point it'll be completely Chinese made chips that are doing this.

And what's the biggest cost for a model? Training. But you do that once and the model like any software is infinitely copyable so China can under OpenAI, Anthropic and SpaceX (xAI) and that's what they're doing.

But it gets worse for the AI moat. Local models are going to get cheaper and cheaper to run. You can already run 31B models on sub-$5000 hardware. What do you think it'll cost in 5 years? Will larager parameter models keep getting better or will there be a law of diminishing returns? What is a B100 workload now, will be a Macbook Pro workload in as little as 5 years.

What if all these AI data centers are ultimately just going to be commoditized cloud hardware like AWS in the not too distant future? We already see Google renting big from SpaceX. I think the writedown on all these data center investments and the companies that are doing them is going to be extreme in the next 5 years.

information wants to be free

Anthropic just kneecapped themselves, and possibly OpenAI and Google as well, with their FUD strategy that got fable shutdown by the government.

But that doesn't impact Chinese providers. Then can US companies get investments for expensive model development if they can't actually sell those models-as-a-service?

In the meantime, open source will continue its march onward because while slower, it's completely open source, and the models are already good enough to improve their own work as well as build out the next gen of models.

Did open source operating systems win? No, MacOS/Windows are pretty dominant.

Does open source... cloud hosting, social media, ride sharing apps, you name it win? Not in my experience?

Another potential player could be Apple if they open sourced a frontier model, and make back some of the capex on hardware. Imagine AIserve hardware with continued expansion of MacMini’s and Mac Studios.

Hear me out, economies of scale can only be met when there is a large enough liquidity for it.

The amount of people willing to purchase multiple hardware releases year after year just to run LLM is already tiny and businesses already do use their own hardware and there is no desire for manufacturer to reduce their own margins.

How can you release this to public?!

Why else do you think Anthropic is heavily restricting Fable? You can’t just handwave safety concerns.

As long as these models require a lot of computing power, the best models open source or not will be served by corporations who can afford the infra.

Who's gonna pay to power an open source AI? Will it perform well enough to make Chat-GPT and Claude obsolete?

That’s really the only thing stopping people from training or running these models at home:

All we can do is hope we end up in the one where things are ok.

Biden's GPU controls should give you an idea. Thank you, China. Open source AI must win.

Subscribing is cuck paypig behavior.

You're not a cuck paypig now, are you?

Pass this on to your frens, it may save the future!

And people do not just lose operational freedom. They lose the freedom to think, much less act. To some extent, general intelligence has already been outsourced to a few companies. Phones and computers extend the human mind's capabilities, but most people don't have root on their phone. They don't know or control what software is running on it, or how the hardware is made. They don't control their phone, the phone controls them instead. The upstream problem is ownership of general computation, ownership of your own mind, aka self-ownership. This will become more obvious as computing devices become more personally integrated (desktop -> laptop -> smartphone -> smartglasses -> neural interface). Who owns the digital part of your mind? It's not really you at the moment.

Democracy, or any form of negotiation, can only exist among entities with similar capabilities. The gap must be very small. Orangutans may be smart enough to drive a golf cart, but there are no orangutan citizens in a human democracy. So you cannot run from this by being a luddite hermit in the mountains. When the world is full of digitally computing humans much smarter than you, you'll be at their mercy like monkeys are at the mercy of humans. We destroy their habitats and experiment on them as we please.

Now for the first time in history, organisms can increase their own information processing capability at will. We're in the middle of a speciation event where humanity splits into those who own the digital part of their mind vs those who don't, and there will be further splits based on how much compute you own. Though a future where no individual can fully own their mind is also possible.

By "own", I mean being able to command the entire technology stack. If we want sovereignty for the masses, then we must decentralize the entire technology stack for general computation. That means everything from electricity generation, to chip design and fabbing, to all layers of software from firmware to neural networks. All of it must be accessible to every individual. Everyone must be able to make a computer from scratch at home, or at least without leaving the city they live in. Anything less than that, and democratic society as we know it will continue to crumble.

The fundamental idea underlying all of this is: that which reproduces, survives.

At what level of organization can we reproduce?

The digitally computing human species cannot reproduce as individuals. We can only reproduce as a society, at least for now. You can't make a computer from scratch on your own, but you can make a brain from scratch with just one other person of the opposite sex. As the world we live in becomes more suitable for the digitally computing rather than the purely organic, the organic part of the digitally computing human becomes less likely to voluntarily reproduce. If the organic part were to survive without being disempowered in the future, then it's probably by moving the mechanisms for reproductive drive to the society level (via religion or authoritarian government incentivizing or mandating reproduction), or by ensuring that each and every individual has the means to make the digital part of their mind on their own just like how they can make the biological part on their own.

We're saved /s

Instead of doing a vanity site with a shelf-life of a few days, see where the action already is in online local LLM research and communities and contribute.

/s

Or are we still collectively brainwashed by the strategic false equivalence established by Big AI CMOs?