And here we go.
How fun.
Thank you.
Everybody.
A bunch of work.
Can you hear me?
Yeah.
All right.
Let's do this.
Hey.
I'm Stephen Hood.
I lead open source AI projects for Mozilla.
That's my email.
That's hooded Mozilla.com.
That's me receiving instructions for my 8-bit masters.
I'm going to talk to you about Mozilla Builders today, which is a recent program we launched in the last year.
And it is one of the ways that we sponsor open source projects.
There are other ways, but this is one of them.
I'll tell you what the project is.
Why we're doing it.
I'll tell you about some of the open source projects we funded to date.
And the kind of things we're looking to fund in the coming year.
And then talk about how we can finish the work together.
If you're working on a project that fits into the mold here.
Word of warning, this is going to be a lot of QR codes.
You all love QR codes, right?
They're beautiful, right?
I hit them.
But there's so many URLs and projects I want to share with you.
There's no easier way to get that information to you.
So that's off your phone because this is cool projects I want you to check out.
Maybe become a contributor on.
So what is Builders?
Builders is a program by which we sponsor or code develop open source AI projects.
That we think will help open source compete with closed source providers like OpenAI and
Thropic Google and others.
Now, before I move any further, you've probably noted the AI word in there.
You're probably wondering why the AI part.
Why is that in there?
Why isn't just open source projects?
And so like I said, this isn't the only way that we sponsor open source.
But this project is right now about AI.
And here's the reason why.
As a company, when we look at tech right now, we have a sense of deja vu about what's going on right now.
So looking around, I'm scared to say how few of you probably remember
the NetScape days.
But some of you do.
Yeah.
There's my people.
All right.
If you remember NetScape, then you remember how close we came to Microsoft,
basically making the web a feature of Windows.
Right?
It came very, very close to happening.
And open source is a big reason why it didn't happen.
And we kind of feel like it's playing out again with AI now.
And this is like a fundamental need technology.
And whether or not we individually like it or not.
It's not going away.
I hate to break it to you.
It's not going to go away.
And it's already affecting the web pretty profoundly.
If you've been online the last year or two, you probably noticed this happening with AI
Slop, more and more bots than ever.
Like we have to pay attention to this technology because it is affecting the web.
And that's why Mozilla cares about it.
We think of the irresponsible, not to push for this to be open source,
just like the browser itself has been.
And things are already happening.
So if you look at the world of open source AI, there's a lot of stuff you can do today
that you wouldn't be able to do otherwise.
The fact that there were open models that existed all that you can run on your own computer
that's because of open source.
The fact that some of them don't require you to write a huge check to Jensen
and fund his next yacht.
You can just use the CPU already have.
That's a big deal.
That's because of open source.
The fact that you can build apps that don't have to call out to a cloud provider,
they don't have to share your personal data with some server out there in someone's farm.
That's because of open source as well.
Not to mention all the collaboration that we can all do together to improve these projects.
So you've all probably heard about deep seek.
It came out of a week or so ago.
That's an example.
All the other complexities aside.
It's an example of open source doing what it's supposed to do,
which is people building on each other's creations and innovations
and then sharing those back out.
But even so, things need to get a little simpler, I think.
Anyone who's built, I need to open this for say I in the last year or so,
you have experienced a neat grinder of choices you have to go through.
What model may you use?
What inference provider?
What vector store?
What orchestration tool?
If any am I going to use?
And on and on and on.
Meanwhile, that's hilarious.
That image did not load.
That is the picture of a Japanese bullet train.
You ought to say about word for it.
So we've got open AI just like barreling through the country side at 400 kilometers an hour.
Well, we're all like stuck in a traffic jam.
Because they've got this clean, well-defined, easy to use API.
That a lot of people just reach for by default.
It's just easier.
So we need to get open source to a place where that's actually the easiest choice to start with.
And we're just not there yet.
And of course, we can't do this ourselves.
Just like we couldn't do the browser ourselves.
So our theme when we launched this program last year was local AI.
And that means projects that make it easier to build AI powered apps that run locally.
Where the intelligence is actually running on your device.
Because we figured that those sorts of projects would naturally be resistant to being captured by platforms.
Right?
And so these are seven areas.
I'm going to do them enough time.
You can take a picture and ask me about it later.
These are seven areas where we thought open source was kind of lagging behind people like Open AI.
And so we invested in projects in this area.
We did it in two ways.
We did it with an accelerator program where we invested in 14 different open source projects in these areas.
It started in September and it ended in December with a in-person demo day in San Francisco.
And it's attended by press, investors, technologists, academics, and peers.
And all 14 of these teams had the chance to pitch and demo what they're working on.
And they all left with some progress.
Maybe more funding, more users or contributors.
It was pretty cool.
And you'll notice down there by the way in this little corner.
You recognize that computer?
Yeah.
It's the same one.
Same one.
I brought it with me.
And we used as a dumb terminal.
Like a VT100 terminal that talked to an LLM.
Like it's connected to an A's bulletin board system.
So like everything is interconnected.
And that's the level we're operating.
So.
Yeah.
All right.
So let me tell you about three of these projects just really quick.
Just to give you a flavor.
Again, there were 14 of these.
So transformer lab.
If you want to train your own model.
You want to fine-tune it.
You want to evaluate it.
That requires you to set up an understanding bunch of tools and techniques.
Transformer lab turns those all into a installable, gooey application.
It's pretty cool.
So you can go to this QR code.
Go there GitHub install it.
And you can be tuning your own model learning how it works.
Pretty amazing.
Another one is, please.
This is actually, I believe this team is based in Europe.
And what they're trying to do is create truly open data sets and models.
And I don't mean open source.
The way our friends at meta define it.
I mean actually open source.
So everything is disclosed and documented and reproducible.
And for the data sets, it's all truly above board.
So there isn't like undisclosed copyrighted data stolen from somewhere.
It's all public domain, creative commons, things like that.
During the program, they released a data set called Common Corpus, which notably is trained
on a lot of non-English content and is fully open and their own first model.
Based on that data set.
And then Thia is the third example.
This is an open source IDE that has AI assistance in it.
And there's a lot of people doing this right now.
I'm sure you've probably heard a lot of them.
Their particular approach to Thia is that they are working with open models, running locally.
And they keep the human in the loop.
So it's not trying to just do the job before you.
It's trying to help you use the developer to be more productive.
And so during the builder accelerator, they launched their first version of this capability.
And we're going to integrate it with MamaFile, which I'm branded for your convenience.
We'll talk about that more in a minute.
So that's the accelerator.
The other thing we did is what we call collaborations.
So collaborations are a little more organic.
It's not like a program you apply to.
They are what?
That's really subtle.
Thanks.
All right.
I'm going to speed up.
There's so much to talk about.
So these are one-on-one relationships we have with people who are building cool projects.
And give some examples.
Lomfile.
So Lomfile.
I love this project.
I'm the project lead for it.
And Justin Toney is the brilliant independent developer who did all the work.
And what this does is it democratizes access to open models by making them ridiculously easy to use.
Because it turns them into a single file executable.
And you can download it and run it in any computer with no installation.
Any hardware, GPU or not.
And it will let you use that model right out of the box.
And we did this by combining two projects we love.
One of them is Lomfile CPP.
Which if you're in this room, there's a good chance you've heard of.
And love as much as we do.
Also Cosmopolitan, which is Justin's project.
And this adds the sort of single file multi-platform executable magic.
And when you run a long file, you get an open model.
But you also get everything else in the box.
So you get a UI for it, you get an API server,
and you get a scriptable component.
Like it works from the command line.
So you can bash and pipe to your hards content.
This is a quick video.
Just an example of the performance enhancements we're able to make.
So these are both different copies of Lomfile.
Doing the same thing, which is reading a scientific paper and generating a summary of it.
On the left is an old version.
On the right is the latest version that has all of the CPU performance enhancements.
Justin did.
And you can see it's almost done.
Yeah, it's done.
But on the left, the old version is still reading the paper.
So it's not even done ingesting the context window that we've fed it.
And again, this is running just on a CPU.
There's no GPU in this machine.
So it's still thinking.
You get the impression.
Finally it's done.
It's going to generate its output now.
So Lomfile, there's a lot going on here.
We have a new API server working on.
That should be faster and more stable.
Then the one we have now, which we inherit from Lomfile CPU.
This is pushing a larger effort, which is we want to just enable developers to build local AI applications.
And we're also moving to a new governance model.
We're moving away from sort of the BDFL model.
Justin worked full time on this for a year.
Now she's switched into part time.
So if you are a C or C++ developer, if you're interested in this problem space, there's going to be opportunities to get involved in the project.
Welcome you to reach out.
Real quick, SQLiteVec.
If you've heard of this, this is a cool project also through builders.
Turn SQLite into a vector database.
So you can use it to build rag applications that run on a user's computer locally.
That's Elixir C as project.
It's awesome work.
And Web Applets is an attempt to do kind of like anthrophic artifacts or check
GPT plugins as an open standard.
So that we can make sure that open web standards continue to drive the evolution of
applications online even if AI starts to get involved.
So where are we going next?
The theme for this year is the next user agent.
And what do I mean by that?
Well, what I mean is this that computing is changing whether we like it or not.
So for 75 years, we have been building computers based around
a fact which is that they can't understand us.
So we put all the effort into making it easier for
us to understand the computer to talk to the computer.
And then like just last year that all changed.
To some definition of the word understand,
computers can not understand us.
They can make sense of our world,
our writing, our video and images now and sound,
and they can take action on our behalf.
And so what this means is things could change in some really weird ways.
We built this gengotower of technology.
These are all the things we built for 75 years
that power everything in our computing world.
And so that's kind of said it, I'll say it too.
Like browsers are pretty complex.
They're actually kind of like a computer inside a computer.
You know, you've got like process management, you've got
Wazzin, you've got JavaScript, you've got the rendering engine.
It's like a computer.
So it should be no surprise to us that browsers are going to get effected
by this new technological change.
And if we care about browsers and care about the web,
we've got to just be thinking about it.
We can't just leave that to the big for-profit companies.
Because we can wake up one day and find that everything's changed around this.
So this is what we're doing this year.
We're funding projects in these areas.
Take a picture of this if you want.
And I'll time to go through them all.
But the unifying theme is, as we develop this tech,
we want to make sure that it leads to a healthier web.
Because by default, that's not assured.
We want to make sure the web continues to be a healthy place,
even as the technology changes.
All right, that's it.
That's my email.
Reach out to me if you're interested.
This is our website.
Lots to read about lots of good projects there.
Thanks a lot.
And so if it's not finished,
it's because I wanted to have time for questions.
We have five minutes for questions.
If you want to leave the room, please do so.
Downstairs.
Remember, we have swagon on the table.
The two tables downstairs in table here.
Questions?
Hi. Thanks for this presentation.
I'm really interested in LLMs and what it's employers and how
mushy-like and leverage this.
And recently, I discovered that the web is like an experiment
in Firefox to do like AI chatbot and some integrations.
And I wanted to try it.
And I could only connect it to proprietary cloud platforms,
not to LML file, not to LML-CCP-CPP.
And I managed and or I could connect it,
but then it wouldn't work with LML-CPP
local after modifying about config.
And I'm just wondering what's going on,
because I would expect to be able to use it local first
with LML-CPP from Mozilla,
and I don't understand what's going on with you.
You can.
So if you're having troubles that might have been a bug
in nightly or something like that.
So there's a talk later about the AI framework
that we're building into Firefox.
So that speaker can maybe address it more,
but I would say just tread the leaves nightly.
There's a developer flag you have to throw that
makes local host an option.
And then if you use LML file or LML-CPP,
it should work.
They have the same endpoints in the same API signature.
So it's definitely our intention that you can use it locally.
I do all the time.
Yeah.
Talk to me afterwards.
That team.
So if you have any questions, you can take a look at it.
Thanks, Art, and more questions.
I don't see any hint.
Thank you very much.
Remember, if you want to leave the room with bell stairs.