0:15

Look a Mac brands, this is

0:17

that she's log I managed to

0:19

go of yeah again. This we

0:21

were joined by Stephen. I'm a

0:23

fully the Executive Director of the

0:25

Open Source Initiative. The oh it's

0:28

I. We Open Source Initiative is

0:30

responsible for representing the idea in

0:32

the definition of Open Source globally.

0:34

Stefano shares the challenges they face

0:36

as a Us based organization with

0:38

a global impact. We discuss the

0:40

work Stefano in the Open Source

0:43

Initiative are doing to define Open.

0:45

Source A I am why we

0:47

need an accepted and shared definition.

0:49

Of course we also tell about

0:51

the potential impact if a poorly

0:53

defined Open Source A emerges from

0:56

their efforts. Also to mention that

0:58

stuff and with Philly under the

1:00

weather for this conversation but he

1:02

power through because of how important

1:04

this topic is. A massive thank

1:07

you to our friends in Are

1:09

Partners A fly.io The Home of

1:11

Change well.com It's simple launch apps,

1:13

new users, they transform. Containers into

1:15

Micro Vm that run in their

1:18

hardware in thirty plus regions on

1:20

six continents. Launch an app for

1:22

free at fly.io. With.

1:34

Up Friends This episode of that

1:37

she's always brought you Buy our

1:39

friends over at Resell and I'm

1:41

he will leave Robinson V P

1:43

a product. Liana You know the

1:45

tagline for Resell. Develop previous ship

1:47

which has been perfect but now

1:49

there's more. after the ship process

1:51

you have to worry about security

1:53

of their ability in other parts

1:55

of just running an application. Production

1:57

was the story there. What's beyond

1:59

shipping? Yeah, you know,

2:01

when I'm building my side projects or when I'm

2:04

building my personal site, it often looks like develop

2:06

preview ship. I try out some new features, I

2:08

try out a new framework, I'm just hacking around

2:10

with something on the weekends. Everything

2:12

looks good, great. I ship it, I'm done. But

2:14

as we talk to more customers, as we've grown

2:16

as a company, as we've added new products, there's

2:19

a lot more to the product portfolio

2:21

of Vercel nowadays to help pass that

2:23

experience. So when you're building larger, more

2:26

complex products and when you're

2:28

working with larger teams, you want to

2:30

have more features, more functionality. So tangibly,

2:32

what that means is features like our

2:34

Vercel firewall product to help you be

2:36

safe and to have that layer of

2:38

security. Features like our logging and observability

2:40

tools so you can understand and observe

2:42

your application and production, understand if there's

2:45

errors, understand if things are running smoothly

2:47

and get alerted on those. And

2:49

also then really an expansion of our

2:51

integration suite as well too, because you

2:53

might already be using a tool like

2:55

a Datadog or you might already be

2:58

using a tool at the end of

3:00

this software development lifecycle that you want

3:02

to integrate with to continue to scale

3:04

and secure and observe your application and

3:06

we try to fit into those as

3:08

well too. So we've kind of continued

3:10

to bolster and improve the last mile

3:12

of delivery. That sounds amazing.

3:15

So who's using the Vercel platform like that?

3:17

Can you share some names? Yeah,

3:19

I'm thrilled that we have some

3:21

amazing customers like Under Armour, Nintendo,

3:24

Washington Post, Zapier who use

3:26

Vercel's running cloud to not only help

3:28

scale their infrastructure, scale their business and

3:31

their product, but then also enable their

3:33

team of many developers to be able

3:35

to iterate on their products really quickly

3:37

and take their ideas and build the

3:40

next great thing. Very cool.

3:42

With your configuration for over 35 frameworks,

3:44

Vercel's running cloud makes it easy for

3:46

any team to deploy their apps. So

3:49

you can get started with a 14 day free

3:51

trial of Vercel Pro or get

3:53

a customized enterprise demo from their

3:55

team. Visit vercel.com/teams log

3:58

pod to get started. That's

4:01

vercel.com/Cheese Law

4:03

Pod. Well,

4:33

Stef, no, it's been a while. Actually,

4:35

never, which is a good thing, I

4:37

suppose, but now we're here. Fantastic. We

4:39

were at All Things Open recently, and

4:41

we tried to sync up with you,

4:43

but we missed the message. And

4:45

so we were like, we got to get you on the podcast. And obviously,

4:47

you know, this show, the Cheese

4:49

Law, was born around open source. And

4:52

I kind of find it strange and

4:55

sad that we've never had anybody from

4:57

the open source initiative on this podcast.

4:59

It's – I'm glad you're here to

5:02

change that, so welcome. Thank

5:04

you. Thank you for having me. It's a pleasure.

5:07

Sorry we missed it. We missed

5:09

each other in South Carolina. It

5:11

was a great event. Oh, man. We love

5:13

All Things Open. We love Todd and their

5:15

team there. We think All Things Open is

5:18

the place to be at the end

5:20

of the year. If you're a fan of open

5:22

source, you're an advocate of open

5:24

source, and just the way that it's permeating all

5:27

the software, right? It's one. Open source is one.

5:29

And now we're just living in a hopefully

5:31

mostly open source world, right? Absolutely.

5:35

Absolutely. I mean, just last week

5:37

was an article published,

5:41

estimated the value of open source

5:43

software as a whole. The

5:46

numbers are incredible. These

5:48

researchers from Harvard Business

5:51

School went and looked

5:53

at the value of open source as it

5:55

is consumed or produced.

6:00

Dollar numbers on it. I am you the

6:02

school because I I don't know how among

6:04

analysts term a purely guess someone and have

6:06

analysed trial Yemen illegal brain and from Iowa

6:08

knows you Okay Donna hired quantify the value

6:11

of oh man oh it's quite valuable center

6:13

that little the how do you value and

6:15

he quantify the vive of the source like

6:17

worried it was the metrics a. The.

6:19

Key Office? Do you know? They. Can zip

6:22

lines of code or they counted the

6:24

Our Steam estimated the hours that it

6:26

would take to rewrite from scratch or

6:28

does software that isn't news. And.

6:31

A Use Dub Dub assets that

6:33

are debatable are already and with

6:35

some with some of those count.

6:37

And. Using those two with other sits there

6:40

to meet a t w that would take

6:42

to replicate own of the open source software

6:44

deniers that is available and they put the

6:46

numbers around. Eight. Point: Eight

6:48

Trillion dollars. Ah. I would

6:51

I? she just say all the dollars. Really?

6:53

Personally I would the sailed all the.yeah why

6:55

me? it's a huge number. Hundred dollars right?

6:57

It doesn't every dollar today like really depend

6:59

on open source at some layer. So.

7:02

Like really couldn't beat us all the dollars. Well

7:04

a bright it. It's an impressive number and it's

7:06

really hard to picture it how much how big

7:08

it is. I will our winter had them to

7:10

look it up and. Allison said

7:12

it's three times as much as

7:14

Microsoft. Market. Dot. And

7:17

ah, it's larger than the whole

7:19

of Juri state's budget. Like twenty

7:21

twenty two. His budget in the

7:23

United States that includes many deaths

7:25

are debate. Medicare six point, Three

7:27

Trillion Four yeah, The Australians their

7:29

right, more mortuary and that I've

7:31

got tired of an easier thing,

7:33

right? I got strains of anything

7:36

really media. Not. Even and sense

7:38

can there's a trend since housing allow you

7:40

to keep on by Kiss almost as series

7:42

of the I returned as is a the

7:45

banks and Ceo you. Are sad

7:47

that's on a think about really why here

7:49

and I'm are like eight point eight trillion

7:51

and I started things once you're out to

7:53

nine and I realize that's like a set

7:55

of a trillion dollars is going around at

7:57

a lot of my head around that is.

8:00

A nice rounding error on your in your

8:02

favor for was your own dollars right? Oil

8:05

and my nice for sir around it

8:07

off and out as impose and to

8:10

maintain. Are you know that? Be nice?

8:12

Yeah well. I don't know

8:14

everybody listen this podcast will be. I

8:16

think a lot of them will be

8:18

both the you know in light of

8:20

recent see better now on assume that

8:22

are list of ship is super informed

8:24

of what the open source initiative is.

8:27

I can kind of recently about paid

8:29

stefano but I'd prefer that youth and

8:31

of give us a. A taste

8:33

of what. The. L A Size

8:35

Really About what is the organization as

8:37

A Five One C Three. You know

8:39

it's a public benefit operation in California.

8:41

The What exactly is the Open Source

8:43

Initiative For all that guy with talked

8:45

about what is it? Oh yeah, in

8:47

a nutshell, we are the mundane. There's

8:49

of the open Source definition. And

8:51

dance. The. Open Source Definition

8:53

is a ten points checklist.

8:56

That has been used for twenty six years.

8:58

We have. Celebrated. Twenty Four

9:01

Years last year. It's the the checklist

9:03

that has been used to evaluate licenses.

9:06

Daddy Is legal documents said com

9:08

together with software packages. To.

9:10

Make sure that the packages the

9:12

soft or comes with said Sweden's.

9:15

Said. Are. Written. Down as.

9:18

Can. Be summarized as Tor Freedoms comes from

9:20

dumfries Off to the assumption at these

9:22

the freedom to use the software. Without

9:25

having to ask for permissions.

9:27

The. Freedom to study and to make sure that.

9:30

You know in to understand. What?

9:33

It does and when supposed to be doing. And

9:35

nothing else. And then for.you

9:38

need access to the source code. And.

9:40

Then dead freedom to modify it seemed

9:42

to fix it and and increase it's

9:44

capacity. Or. Help yourselves

9:47

and the freedom to make

9:49

copies.is for yourselves or for.

9:52

Add. To help others. And then

9:54

those three them were were written down

9:56

in the. eighties. By.

9:58

the free software foundation and

10:01

the open source initiative started a couple

10:03

of decades after that, picking

10:05

up the principles and spreading them out

10:07

a little bit in a more practical

10:10

way. In

10:13

a time, at a time when a lot

10:16

of software was being deployed and

10:18

powering the internet, basically, this

10:21

definition and this is a

10:23

license, licenses, gives users

10:26

and developers clarity about the

10:28

things that they can do. Provides

10:31

the agency and independence and

10:33

control and all

10:35

of that clarity is what

10:37

has propelled and generated that huge ecosystem

10:40

that is worth 8.8 trillion.

10:45

So who formed the initiative and

10:47

then how did it sustain

10:50

and continue? Seems like the definition

10:52

is pretty set but like what is

10:54

the work that goes on continually? Yeah,

10:56

well, the work that goes on continuously

10:59

is, especially now recently,

11:01

it's the policy,

11:03

the monitoring of policy works and

11:06

everything that goes around it. The

11:09

concept of open source seems to

11:11

be set but it's constantly under

11:13

threat because evolution

11:15

of technology, changes of business models,

11:17

the rise and the rise of

11:20

importance and power of new actors

11:22

constantly shifts and

11:26

tends to push the definition itself

11:29

of open source in different directions, the

11:31

meaning of open source in different directions

11:34

and regulation also tends to

11:36

introduce hurdles that

11:38

we need to be aware of. The

11:41

organization, what we do, we

11:43

have three programs. One is

11:45

called the Legal and Licenses

11:47

Program and that's where we

11:49

maintain the definition. We review

11:51

new licenses as they get approved and

11:54

we also keep a database of

11:56

licensing information for packages

11:58

because often... developers don't

12:00

use the right words or miss

12:03

some pieces, a lot of packages don't have

12:05

the right data. And

12:07

we have, we are maintaining

12:10

the community that maintains this

12:12

machine called Clearly Defined. On

12:14

the policy front, that's another program, the

12:17

policy and standards front,

12:19

we monitor the activity of standard

12:21

setting organizations and

12:23

the activity of regulators in the United

12:25

States and Europe mostly to make

12:27

sure that all the new laws and rules and

12:31

the standards can be implemented with

12:33

open source code and the regulation

12:35

doesn't stop or doesn't block the

12:38

development distribution of open source software.

12:40

And then a third program is

12:43

on advocacy and

12:45

outreach and that's the

12:47

activities that we do with maintaining

12:49

the blog, having the communication, running

12:51

events. And in this

12:53

program, we're also hosting the conversations

12:55

around the funding open source AI,

12:58

which is a requirement that

13:01

came out especially a couple of years ago,

13:03

getting rapidly glowing

13:06

of hotness at

13:08

us. So we were

13:10

basically forced to start this

13:12

process because open AI is

13:14

a brand new system, the

13:17

brand new activities that forces us

13:20

to review the principles to see if

13:22

they still apply and how they need

13:24

to be modified or we can apply

13:26

to AI systems as a whole. And

13:29

we are a charity organization, you mentioned that.

13:32

So our sponsors are individuals

13:34

who donate to become members and

13:37

they can donate any amounts from

13:39

$50 a year up to what have you.

13:42

And we have a few hundreds of those,

13:44

almost a thousand. And then we

13:47

have corporate sponsors who give

13:49

us money also, donations to

13:52

keep this work going. It's in

13:54

their interest to have an independent

13:56

organization that maintains the definition and

13:59

having both. of all of these

14:01

donors, corporate donors, makes the

14:04

organization stronger. So we don't

14:06

depend on any

14:08

one scene individually of them. So

14:11

despite the fact that we get money from

14:13

Google or Amazon or Microsoft and

14:15

GitHub, we don't have to swear our

14:17

own agencies to them. Do

14:19

you also defend the license so far as

14:22

going to court with people who would

14:25

misuse it or no? It hasn't happened,

14:27

but we do have, I

14:29

mean, not under my watch, but

14:31

we do have experts and now

14:34

on our board and in our

14:36

circle of licensing

14:39

experts, we do have lawyers who

14:41

go to court constantly to

14:44

defend the license, to train more,

14:46

protect users. And

14:48

they're there as like expert witnesses? Exactly.

14:51

And we do provide, we

14:53

have provided briefs for courts,

14:56

opinion pieces for regulators and

14:59

responses to requests for

15:01

information in various legislation

15:03

here. How challenging is it

15:05

to be a US-based

15:08

founded idea now,

15:12

organization that represents

15:14

and defends this definition that really,

15:16

you know, going back to the trillions,

15:18

like, I mean, all the money, all the

15:21

dollars, like it's a world problem.

15:23

It's not just a United States

15:25

problem. How does this organization operate

15:28

internationally? What challenges do

15:30

you face as a

15:32

US-based nonprofit, but representative

15:34

of the idea of open source

15:36

that really impacts everyone globally?

15:39

Yeah, that's a very good question. In

15:41

fact, it is challenging. So I started

15:43

at the organization only over

15:45

two years ago, and I'm

15:48

Italian. And so I do have connections

15:50

to Europe and knowledge about Europe. We

15:52

do have board members that are based

15:54

in Europe and other board members in

15:57

the United States. And it is actually

15:59

quite a challenge. quite challenging to

16:01

be involved into this global

16:03

conversations because now, a little

16:05

bit like maybe in

16:07

the late 90s, open source

16:10

is becoming increasingly getting

16:12

at the center of geopolitical challenges

16:14

and not because of open source

16:16

per se, but because

16:19

software is so incredibly existing

16:21

everywhere and most of that software that

16:23

exists is open source. So

16:26

there have been a lot of challenges

16:29

as the relationship, the trade relationship

16:31

with other actors like

16:33

Russia, Ukraine, now with

16:35

the war in Israel and

16:38

Gaza and the trade

16:40

wars with China, between China and

16:42

United States. There are a

16:44

lot of geopolitical issues that we are at

16:46

the center of and we're

16:48

finding really complicated. In

16:51

fact, we do have, we

16:54

have raised more money to increase

16:56

our disability on the policy front.

16:58

We have right now, at

17:01

the moment, we have two people working,

17:03

one in Europe and one is more

17:05

focused in the United States. Both

17:07

of them are part-time, but we do have budget

17:10

to hire at least another

17:12

one, if not two

17:14

policy analysts to help

17:16

us review the incredible amount of legislation

17:19

that is coming. We're just talking about

17:21

United States and Europe. I

17:23

guess even one more layer than that is

17:26

that I don't know

17:28

if it's a self-profession of the defender ship

17:30

of the term of open source. I understand

17:32

where it came from to some degree, you

17:34

know, and I wonder if, how

17:36

do you all handle the

17:38

responsibility of not

17:40

so much owning the trademark term of open source,

17:42

but to defending it? So in a way, you

17:45

kind of own it by

17:47

defending it because you have to defend it. Like it's

17:49

some version of responsibility, which is maybe

17:51

a by-product of ownership, right? There's

17:54

a pushback happening out there. Like there's even

17:56

a conversation of recent where,

17:58

you know, they can't describe their software

18:00

is open source because

18:03

the term means something. And we all agree

18:05

on that, right? We understand that. And

18:07

I'm not trying to defend that,

18:09

but like how do

18:11

you operate as an organization that defends this term? Yeah,

18:14

I mean this is really funny because we

18:17

don't have a trademark on the term

18:19

open source applied to software. We have

18:21

a soft power, Q1, that

18:23

is given to us by all

18:25

the people who, just like you just said,

18:29

recognize that the term open source is

18:31

what we have designed, we have defined.

18:34

We maintain the definition. It's

18:36

kind of recursive if you want. But

18:39

corporations, individual developers,

18:42

all their institutions like academia,

18:44

researchers, they recognize that open

18:47

source means exactly those 10,

18:50

the list of licenses, those 10 points

18:52

which you want the four freedoms that

18:54

are listed. And we maintain

18:56

that. And

18:58

this has become quite visible also even

19:00

in court where they do

19:03

understand that if someone is like

19:05

there was a recent case involving

19:08

the company Neo4j and

19:11

during that litigation that

19:13

is quite complicated with entrenched.

19:16

I'm not a lawyer. I'm not going to dive

19:19

into legal things. But

19:21

the one key takeaway that is easy

19:23

for me to grasp and communicate is

19:26

that the judge recognize that the

19:29

value of open source is in the

19:31

definition that we maintain and

19:34

calling open source something that is not

19:36

a license, that is not a license

19:39

that we have better approved is

19:42

false advertising. And that held up in

19:44

court? Oh, yeah. And

19:46

so is that what you would say to people who are

19:49

perhaps maybe nonchalant isn't the

19:51

best word but unimpressed

19:54

by open source as a

19:56

definition and they think it's

19:59

stodgy and. type and the thing that

20:01

they're doing is close enough and they

20:03

like the term, they're going to use

20:05

the term and they've got open-ish code

20:08

or source available or business source.

20:11

Because a lot of people are kind of pushing not just

20:13

against the definition itself but like against

20:15

the idea that we need a definition or like

20:17

you guys get to have the definition. What

20:20

do you say to them? Yeah, you know

20:22

they're self-serving. They try to

20:24

be self-serving and they're trying to destroy

20:26

the commons that way quite

20:29

visibly. I think that users see

20:31

through them and it's not

20:33

even in their interest but you

20:35

know how it works sometimes corporations,

20:38

their greed goes up to

20:40

they care only about the next quarter and

20:43

who cares about what happens next. Maybe

20:46

the next CEO will have to take care meanwhile they're

20:48

just going to laugh all the way to the bank.

20:51

And that is the approach that I see

20:53

many of these people who complain

20:56

or who try to

20:58

redefine open source because it doesn't serve

21:00

their purpose. What we maintain it doesn't

21:02

fully serve their purpose. So instead of

21:05

respecting the commons and the

21:07

shared ideas, they act

21:10

like bullies and find all

21:12

sorts of excuses to redesign.

21:14

We've seen it happening. I've been

21:16

in free software and open source

21:18

post in my career since I was

21:20

in my 20s and I've seen

21:23

what was happening with the early

21:25

days with the proprietary Unix

21:28

guys that were going around telling us

21:30

that this Linux thing is

21:32

never going to work, you're joking, you're

21:36

giving away. Then they started to

21:38

be scared and started saying, hey, you're giving away

21:40

your jewels. You know why are you

21:42

doing this? You're depriving us of our life

21:45

support. The families are

21:47

going to be begging on the streets. I

21:49

remember having this conversation with a sales guy

21:51

from Moscow and

21:54

Microsoft coming up with their

21:56

program in the early 2000s. shared

22:00

source program because they just

22:02

could not get, they wrapped their head around

22:05

the fact that you could make money sharing

22:07

your source code. But they were forced by

22:09

the market to show at least a little

22:11

bit of what was happening behind the scenes.

22:13

They were losing deals. So

22:16

we've seen it already. They're

22:19

gonna keep on going like this, but

22:21

there is plenty of interest in maintaining

22:24

plenty more forces on the other side

22:26

to maintain, then to

22:28

keep the bar straight, to keep

22:30

going where we're going. Because that

22:33

clarity is, so

22:35

it's such a powerful, such a

22:37

powerful instrument to be able to

22:39

say, I'm open source, therefore, I

22:42

know what I can do, I know what I cannot do, and

22:45

have that collaboration straightened up. The

22:48

legal departments, the compliance departments,

22:50

the public tenders, they all

22:52

tend to have very clear

22:55

and speedy review of

22:58

processes that instead, everyone has

23:00

a different understanding of what open source

23:02

means. Yeah, we go back to the

23:04

brand, right? I'm in

23:07

Italy now and I'm surprised to see a

23:09

lot of Starbucks stores

23:11

opening. And I'm

23:14

absolutely baffled, like why is this happening?

23:16

This country has plenty of bar every

23:18

quarter. So there's a cafe with a

23:20

decent coffee. Why do you need

23:22

a brand? Because people have been going

23:24

around traveling the world, they see the brand, they

23:27

recognize it, they know what they can do, they

23:29

know what they're gonna get, what they're gonna get,

23:31

and they go out there. And it's the same

23:33

with open source. Gets

23:56

separate. Hit enter. A

23:59

lot of friends. This episode is brought

24:01

to you by our friends at

24:03

Sanadia. Sanadia is helping teams take

24:06

NAS to the next level via

24:08

a global multi-cloud, multi-geo, and extensible

24:10

service fully managed by Sanadia. They

24:13

take care of all the infrastructure,

24:15

management, monitoring, and maintenance for you

24:17

so you can focus on building

24:20

exceptional distributed applications. And

24:22

I'm here with VP of product and engineering, Byron Ruth.

24:24

So Byron, in the NAS

24:27

versus Kafka conversation, I hear

24:29

a couple different things. One

24:31

I hear out there, I hate Kafka with

24:33

a passion. That's quoted by the way on

24:35

Hacker News. I hear Kafka

24:37

is dead, long live Kafka. And then

24:39

I hear Kafka is the default, but

24:42

I hate it. So what's the deal

24:44

with NAS versus Kafka? Yeah,

24:46

so Kafka is an interesting one. I've

24:48

personally followed Kafka for quite some time

24:50

ever since the LinkedIn days. And I

24:52

think what they've done in terms of

24:55

transitioning the landscape to event streaming

24:57

has been wonderful. I think they

25:00

definitely were the sort of

25:02

first market for persistent data streaming.

25:04

However, over time, as people

25:07

have adopted it, they were the first

25:09

to market, they provided a solution, but

25:11

you don't know what you don't know in

25:13

terms of you need this solution, you need

25:16

this capability, but inevitably, there's also

25:18

all this operational pain and overhead

25:20

that people have come to associate

25:22

with Kafka deployments. Based on our

25:24

experience and what users and customers

25:26

have come to us with, they

25:29

would say, we are spending a

25:31

ton of money on spend on

25:33

a team to maintain our Kafka

25:35

clusters, or managed services,

25:37

or something like that. The paradigm

25:40

of how they model topics and

25:43

how you partition topics and how you

25:45

scale them is not really

25:47

in line with what they fundamentally want to

25:49

do. And that's where NAS

25:51

can provide, as we refer to

25:54

it, subject-based addressing, which has a

25:56

much more granular way of addressing

25:58

messages, sending messages. subscribing

26:00

to messages and things like that

26:02

which is very different from what

26:04

Kafka does. And the second that

26:07

we introduced persistence with our Jetstream

26:09

subsystem as we refer to it

26:11

a handful of years ago, we

26:13

literally had a flood of people

26:15

saying, can I replace my Kafka

26:17

deployments with this NATs Jetstream alternative?

26:20

And we've been getting constant inbounds,

26:22

constant customers asking, hey, can you

26:24

enlighten us with what NATs can

26:26

do? And oh, by the way,

26:28

here's all these other dependencies like

26:31

Redis and other things and some of

26:33

our services based things that we could

26:35

potentially migrate and evolve over time by

26:38

adopting NATs as a technology, as a

26:40

core technology to people's systems and platforms.

26:42

So this has been largely organic. We

26:45

never from day one, with our persistence

26:47

layer Jetstream, the intention was never to

26:49

say we're going to go after Kafka.

26:52

But because of how we

26:54

layered the persistence on top of this

26:56

really nice PubSub core NATs foundation, and

26:58

then we promoted it and we say, hey,

27:00

now we have the same semantics,

27:03

same paradigm with these new primitives

27:05

that introduce persistence in terms

27:07

of streams and consumers, the flood

27:10

gate just opened and everyone was

27:12

frankly coming to us and wanting

27:14

to simplify their architecture, reduce costs,

27:16

operational costs, get all of

27:18

these other advantages that NATs has to offer

27:21

that Kafka does not whatsoever, or any of

27:23

the other similar offerings out there. And you

27:25

get all these other advantages that NATs has

27:27

to offer. So there's someone out

27:29

there listening to this right now, they're the

27:31

Kafka cluster admin, the person in

27:33

charge of this cluster going down or not,

27:36

they manage the team, they feel the pain, all

27:38

the things, give a prescription, what should they

27:40

do? What we always recommend

27:43

is that you can go to the

27:45

NATs website, download the server, look at

27:47

the client and model a stream. There's

27:49

some guides on doing that. We also

27:51

have so native provided a basically a

27:53

packet of resources to inform

27:55

people because we get again, so many

27:57

inbound requests about how do you compare?

28:00

apps in Kafka and we're like, let's actually

28:02

just put a thing together that can inform

28:04

people how to compare and contrast them.

28:06

So we have a link on the website

28:08

that we can share and you

28:10

can basically go get those set

28:12

of resources. This includes a very

28:14

like lengthy white paper from an

28:16

outside consultant that did performance benchmarks

28:18

and stuff like that and discuss

28:20

basically the different trade-offs

28:22

that are made and they also

28:25

do a total cost of ownership

28:27

assessment between people who are organizations

28:29

running Kafka versus running NATs

28:31

for comparable workloads. Well

28:34

there you go. You have

28:36

a prescription. Check for a

28:38

link in the show notes

28:40

to those resources. Yesterday's tech

28:42

is not cutting it. NATs

28:44

powered by the global multi-cloud

28:46

multi-geo and extensible service that

28:48

is fully managed by Cineadia.

28:51

It's the way of the

28:53

future. Learn more at cineadia.com/change

28:55

log that's synadia.com/change log. So

29:07

last year on this time, Meta released

29:11

Llama, their large

29:13

language model, and

29:15

to much fanfare and applause and they

29:18

announced it as open source. We

29:21

know a lot has transpired since then but at

29:23

the time what was your response to that

29:26

even personally or as the executive

29:28

director of the OSI? Like what were you thinking? What

29:30

were you doing in the wake of that

29:32

announcement? Well we were

29:34

already looking at open source AI in

29:37

general. We were trying to understand what

29:39

this new world meant and what the

29:41

impact was on the principles of open

29:43

source as they applied to new artifacts

29:46

that are being created in AI and

29:49

we already had come to the

29:51

conclusion that open source AI

29:53

is a different animal than open source

29:55

software. There are many

29:58

many differences. So

30:00

we meet at me two years ago, over

30:02

two years ago, that was one of the

30:04

first things that I started was to really

30:06

push the board and to push the community

30:08

to think about AI as

30:10

a new architect that acquired

30:13

and deserved also a deep

30:16

understanding and a deep

30:18

analysis to see how we could transport

30:20

the benefits of open source software into

30:22

this world. But Rene's

30:24

of LAMA2 kind of cemented that

30:27

idea. It is a

30:29

completely new artifact because they

30:31

have released, sure they have released a lot

30:34

of information, a lot of details, but

30:36

for example, we don't know exactly what

30:38

went into the training data. And

30:41

well, LAMA2 also came out

30:43

with a license that

30:46

really has a lot of restrictions

30:48

on use. So it's having restrictions

30:50

on use is one of the

30:52

things that we don't like, I

30:54

mean, the open source definition

30:56

forbids. You cannot have any restrictions on use.

30:59

And you know, a surface value, the

31:02

license for LAMA2 seems innocent, right? One

31:04

of the things says, well, you cannot

31:06

use LAMA2 for commercial applications if you

31:08

have more than a few million, I

31:11

don't remember exactly how many, a few

31:13

million active users, monthly active

31:16

users. Okay, you

31:18

know, maybe that's a fair limitation.

31:21

And in my mind, I was like, so

31:23

what does it mean that the government of

31:25

India cannot use it? The

31:27

government of Italy, maybe, you know, if

31:30

you want to embed this into, so

31:33

that's already an exclusion

31:35

and starts to have to think about it,

31:37

you know, think about, yeah, I'm a startup,

31:39

yeah, I'm a small thing. But

31:42

what happens when you get to the six million

31:44

users when, you know, all of a sudden you

31:46

have to lower up and change completely your processes.

31:48

But then there are a couple of other restrictions inside

31:51

that license that are even more

31:53

innocent on surface. But when you

31:55

start diving deeper, like you cannot do anything

31:57

illegal with it. Okay. All

32:00

right, so let me say if I

32:02

help someone decide whether

32:04

they can or they should have an

32:07

abortion or if I

32:09

want to have this tool used in

32:11

applications to help me, I don't

32:14

know, get refugees out of

32:16

war zones into a lot of places.

32:19

And maybe I'm considered a

32:21

terrorist organization by the government

32:24

that is using that. So are

32:26

I doing something illegal? And

32:28

so on whose side, you know, who

32:30

needs to be evaluating that? If

32:33

these licensing terms that the Open

32:35

Source Initiative really doesn't think they're

32:37

useful, they're valuable, and they

32:39

should not be part of a license, they

32:42

should not be part of a contract

32:44

in general, and they need to be

32:46

dealt at a separate level. So

32:48

that's what I was looking at. I

32:51

was like, oh, LAMA2, oh my God.

32:53

It's not open source because clearly this

32:55

licensing thing would never pass our approval.

32:59

And at the same time, we don't even know

33:01

exactly what open source means. Why are you polluting

33:03

this pay? So I was really

33:05

upset. Yeah. So then

33:07

do you spring into action? Like what does

33:09

the OSI do? Because you're the defenders of

33:11

the definition, and here's a misuse, a huge

33:13

public misuse. Do you write a blog post?

33:15

Do you send a letter

33:17

from a lawyer? What do you do?

33:20

Luckily, we were already into this two-year process

33:22

of defining open source AI. So

33:29

we have... Actually I

33:31

was already in conversations with Meta

33:34

to have them join the process

33:36

and support the process to

33:38

find the shared definition of open source

33:40

AI. And in fact, they're part of

33:42

this conversation, then I'm adding with

33:45

not just corporations like Google,

33:47

Microsoft, GitHub, Amazon, et cetera.

33:50

But also we invited researchers

33:53

and academia, creators of AI,

33:56

experts of ethics and

33:58

philosophy organizations. organizations that

34:00

deal with open in general,

34:03

open knowledge, open data like

34:05

Wikimedia, comments, open knowledge

34:07

foundation, Mozilla Foundation. And

34:10

we're talking also with expert

34:13

thematics, but also organizations like

34:15

Digital Rights Group, like the

34:17

ESS and other organizations around the

34:19

world who are helping into

34:22

this debate.

34:24

Like we had to first go through an

34:26

exercise to understand and come to a shared

34:29

agreement that AI is a different thing

34:31

than software. Then we

34:34

went through an exercise to find the

34:36

shared values that we want to have represented and

34:39

why we want to have the same

34:41

sort of advantages that

34:44

we have for software also posted over to the

34:46

AI system. And

34:49

then we have identified the freedoms

34:51

that we want to have exercised.

34:54

And now we're at the point where we

34:57

are trying to enlist in being

34:59

the list of components of

35:02

AI systems, which is

35:04

not as simple as binary

35:07

code, compiler, and source

35:09

code. So it's not as simple

35:11

as that. It's a lot more complicated.

35:14

So we're building this list of components for

35:16

specific systems. And

35:19

the idea is by the end

35:21

of the spring, early summer, to

35:24

have the equivalent of what we have now

35:26

as a checklist for legal documents for

35:28

software and have the equivalent for AI

35:31

systems and their components so

35:34

that we will know basically we have

35:36

a release candidate for an open source AI

35:38

definition. And you mentioned that,

35:41

and there's, I think you posted this

35:43

eight days ago, a new draft of

35:46

the open source AI definition version 0.0.5 is available.

35:50

I'm going to read from, I think, what you

35:52

might be alluding to, which is exactly what is

35:54

open source AI. And it says, linked

35:56

up to the HackMD document, it

35:59

says, what is open source AI? To be open

36:01

source, an AI system needs to be available

36:03

under legal terms that grant the freedoms to,

36:05

one, use the system for

36:07

any purpose and without having to ask for permission,

36:10

two, study how the system works and

36:13

inspect its components, three, modify

36:15

the system for any purpose, including

36:17

to change its output, and

36:19

four, share the system for others

36:21

to use with or without modifications for any purpose.

36:25

So those seem to be the four hinges that this

36:27

– what is open source AI is hinging upon,

36:29

at least in its current draft. Is that

36:32

pretty accurate, considering it's recent

36:34

eight days ago? Yeah. Those are

36:36

the four principles that we want

36:38

to have represented. Now,

36:41

the very crucial question is what comes

36:43

next, is what is – if you

36:45

are familiar with the four freedoms of

36:48

software, those set by the

36:50

Free Software Foundation in the late 80s. They

36:53

have one – those freedoms have one

36:55

little sentence attached to it, to the

36:57

freedom to study and the freedom to

37:00

modify. They both say access

37:02

to the source code is a precondition for this,

37:05

which really means to clarify, it's

37:07

that little addition. It's meant to

37:09

clarify that if you

37:11

want to study a system, if

37:13

you want to modify it, you need to

37:15

have a way to make modifications to it.

37:18

It is not just – it's

37:20

the preferred form to make modifications from the

37:22

human perspective. It's not that you give me

37:24

a binary and then I have to

37:26

decompile it or try to figure

37:28

out from reverse engineering how it works.

37:31

Give me the source code. I need the source code here

37:33

to study. For the

37:35

AI systems, we haven't

37:37

really found yet a

37:39

shared understanding or a shared

37:41

agreement on what it means

37:44

to have access to the

37:46

preferred form to make modification to an

37:48

AI system. That's the exercise

37:50

that we're running now and we – yeah. Yeah,

37:53

that's interesting. The preferred form of modification

37:55

is really interesting. Like you

37:57

said, you don't want to give a binary. Expect –

38:00

diverse engineering because that's

38:02

possible, right? That's possible maybe to a small

38:04

subset. It's not the preferred route to

38:06

get to Rome. It's just like that's not the route I

38:08

want to go down, right? I want a different way. Yeah.

38:11

And you want to have a simple way. So even

38:14

some licenses even have more

38:16

specific wording around defining

38:19

what source code actually means like

38:21

the GNU GPL is one of those

38:24

very clear description and prescriptions about

38:27

what needs to be given to

38:30

users in order to exercise those freedoms.

38:33

Their freedoms as a user. So

38:35

for AI, yeah, for AI, it's

38:37

complicated because there are a few

38:39

new things for which we

38:42

don't even have, there are no core

38:44

cases yet. You know, I keep

38:46

repeating the same story. When software came out

38:48

for the first time, started to come out

38:50

at the labs, research labs,

38:53

they started to become a commercial artifact

38:56

that people could just sell. There

38:59

was a conscious decision to apply copyright

39:01

to it. It was not a given

39:04

fact that it was going to be using

39:07

copyright, like copyright law. So

39:09

that decision was a lucky one, honestly,

39:12

and it was a well thought out,

39:14

I don't know which of the two,

39:17

because copyright as a legal

39:19

system is very similar across the

39:21

world. And building the open

39:23

source definition, the principle of the definition,

39:26

the legal documents that go with

39:28

software for open source software and

39:30

pre-software, those legal documents built

39:32

on double copyright means that they're

39:35

very, very similarly applied pretty much

39:37

everywhere around the world. The alternative

39:39

at the time was a conversation,

39:41

were conversations around treating

39:43

software as an invention and therefore

39:46

covered by patents. Patent

39:48

law is a whole different mess around

39:50

the world. The whole different applications that

39:52

have all different terms, much

39:55

more complicated to deal with. So

39:57

for AI, we're pretty much at the scene.

40:00

stage where there are some new

40:02

artifacts like the model after

40:05

you train a model and that

40:07

produces weights and parameters that

40:09

go into the model. Those

40:11

models, honestly, it's not clear

40:14

what kind of legal frameworks apply to

40:16

those things. And we might be at

40:18

the same time in history where

40:20

we could have to imagine

40:22

and think and maybe suggest and recommend,

40:25

what the best course of action will be,

40:27

whether it makes sense to treat them as

40:29

copyrightable entities, artifacts,

40:32

or nothing at all, or

40:34

inventions, or any, you know,

40:37

some other rights or exclusive

40:39

rights. And the same

40:41

goes into the other big

40:44

conversation that is happening already,

40:46

but for which there is no, I don't have

40:49

a clear view of where it's going to end,

40:51

these are the conversations around the

40:54

right to data mining. And

40:57

if you follow the conversations around

40:59

charge EPT being sued by New

41:01

York Times and Getty Images, Stability

41:03

AI, and Getty Images, and

41:05

GitHub being sued by Anonymous,

41:08

etc., etc. A lot of

41:10

those lawsuits hinge on

41:13

what's happening, why are

41:15

these powerful corporations going

41:17

around and calling the internet, aggregating

41:20

all of this information and data

41:22

that we have provided, uploaded, we

41:25

society, some commercial actors,

41:27

some non-commercial actors, we have created

41:30

this wealth of data on the

41:33

internet, and they're going around creating

41:35

it and basically making it

41:37

proprietary, building models that

41:39

they have for themselves. And on top of that,

41:42

you can already start seeing like, oh my God,

41:44

they're going to be eventually making a lot of

41:46

money out of the things that

41:48

we have created. Or even more scarily,

41:50

like sometimes I think about this myself,

41:52

I've been uploading my pictures for

41:55

many years without paying too much. So

41:57

there is another base out there, I'm sure

41:59

that. I want to build another base out

42:01

there of my pictures as I was aging. And

42:04

now these pictures are being – can be used, could

42:06

be used by an evil government

42:08

or evil actor to recognize me around

42:10

the streets at any time. And

42:16

I'd allow it in a course. Is that fair?

42:18

Is that not fair? Those are

42:20

big questions and there is no easy

42:22

or simple answer. Yeah. So

42:25

did you enumerate – and I missed

42:27

it – or can we enumerate the

42:29

components that you have decided

42:31

so far are part of an

42:34

AI system, the code I heard,

42:36

the training data, etc.? Yeah.

42:39

There are three main categories. So maybe four.

42:42

Like one is the – yeah, is in

42:44

the category of data. One is

42:46

in the category of code. That

42:48

is the other category is models.

42:52

And there is a four category that

42:54

goes into other things like documentation, for example.

42:57

Instructions of how to use or

42:59

scientific papers. In the data

43:01

parts, some of the components

43:04

are the training data, the testing

43:06

data. In the code

43:08

parts go the tooling to –

43:11

like for the architecture, the inference code

43:13

to run the model. Something

43:15

that is written by human in general,

43:17

the vehicles have in there the code

43:20

to filter and set

43:22

up the data sets and prepare

43:25

them for the training. And

43:27

then in the models, you have

43:29

the model architecture, the model

43:31

parameters, including weights, other parameters,

43:33

and things like that. There

43:36

might be intermediate steps

43:39

during the training. And

43:41

the last bit is documentation,

43:43

how to use samples,

43:45

output. So there is

43:47

an initial list of all of these components

43:50

that have been – we worked with

43:53

or actually the Linux Foundation

43:55

worked on creating this list

43:58

Specifically for generative AI. Enlarge

44:00

language models. yeah, there were working

44:02

with them. I mean, we're using

44:04

deserve their list as I as

44:06

a. Backdrop. Burrow as a

44:09

starting point to move forward this conversation.

44:11

Now. The question that we need to ask

44:13

own adding displaced and it you go to

44:16

the draft Five you will see a name

44:18

to D Matrix Basically from this though components

44:20

that are of sixteen. To remember correctly seventeen.

44:23

This. A Components. And then on

44:25

a row. Next. To them. That.

44:27

Is a question Do I needed to

44:29

run it to? I needed to use

44:31

it. The I mean do anything use

44:33

it I need to hobby into and

44:35

to study to a need this component

44:37

to modify the system. And. Will

44:40

referring to the system like this is

44:42

one of the important thing is. They

44:44

open source definition of first to the program.

44:47

And a program is never define

44:49

added a program in pretty much

44:51

we know what it is a

44:53

say I ask is. And.

44:55

Again is a bit complicated. Question looks

44:58

very simple on surface by when he

45:00

studied the B D by becomes completely

45:02

because what is an Ai system. Rak.

45:05

So we started using D, a definition

45:07

that is been. It's becoming quite

45:09

popular in every regulation and around

45:11

the world. Did say it's a

45:13

work done by Deal Organisation for

45:15

Economic Cooperation and Development D O

45:18

U C D. And. Out

45:20

damn defined an ai system.

45:22

New in very. Broad

45:25

terms. And. His definition is

45:27

being used in many regulations like

45:29

from the. United States

45:31

Executive Order on a I

45:34

nest. Also uses it in

45:36

Europe d A I are uses

45:38

it is I'll go with a

45:41

slight Betty Small minor buddy Asian

45:43

It seems to be quite popular

45:45

but dora detractors and indeed it

45:48

is quite generic to it. Sometimes

45:50

when you agree that it current

45:52

carefully me. You. Didn't color

45:55

a spreadsheet. It's really bizarre. So.

45:58

Let's say that hypothetically, I'm like. a

46:00

medical company that has been working

46:02

on a large language model and

46:05

I have proprietary data. So

46:08

I have like readings and reports

46:10

and stuff that we've accumulated over

46:12

years and I create an

46:14

LLM based on that data that

46:16

ultimately can answer questions about medicine

46:19

or whatever. And I want to

46:21

open source that. I need to be

46:23

able to make it so it's usable,

46:26

studyable, modifiable, and shareable.

46:29

And it seems like the training data even

46:32

though that's the most proprietary part and

46:34

perhaps the most difficult part to actually make

46:36

available or sometimes impossible is

46:39

necessary not to use but

46:42

to study and modify. It seems like

46:45

so if I release the

46:47

model, the code,

46:50

all the parameters, everything we use to build

46:52

a model, everything except for like the source

46:54

original data under what you guys are

46:56

currently working on, that would not be open source AI, would

46:58

it? Honestly, that is

47:01

a very good case. Example

47:03

for why I think we

47:06

need to carefully reason around

47:08

what exactly do I need to

47:10

study? What kind of access, what

47:12

sort of access do I need?

47:15

Is that the original data set? Because

47:17

if it is the original data set, then

47:19

we will never going to have an open source AI. Right.

47:23

That's where I'm getting to. Like it's not going to happen. It's not

47:25

going to happen. Yeah. So

47:27

maybe, and this is my working

47:29

hypothesis that I threw out there, maybe

47:32

what we need is a very good

47:34

description of what that data is.

47:37

Maybe samples, maybe instructions

47:39

on how to replicate it. Because

47:42

for example, there might be data that

47:45

is copyrighted. You might have

47:47

the right under first use or under

47:49

different exclusions of copyright. You may have

47:51

the rights to create a copy and

47:54

create a derivative like around the training.

47:57

But not to redistribute it. Because if you redistribute

47:59

it, then... start infringing. So

48:01

I think we need to be carefully

48:03

thinking about data. And the

48:05

reason why I became more

48:08

and more convinced that we don't need

48:10

the original data set is

48:12

because I've seen wonderful

48:15

mixing, wonderful remixing

48:18

of models, even

48:21

splitting of models

48:23

and recombinations of

48:25

models, creating whole new

48:28

capabilities, new AI

48:30

capabilities, without having

48:32

to retrain a single thing. So

48:36

I'm starting to believe really

48:38

that the AI weights in

48:40

machine learning, the weights in

48:42

the architecture, has it so it's

48:44

not a binary code. It's not a binary

48:46

system that the binary code that you have

48:48

to reverse engineer. If you

48:51

have sufficiently detailed instructions on how

48:53

it's been built and

48:55

what went into it, you

48:57

should be able, you might be

49:00

able to create new systems, reassemble

49:02

it, study how it works,

49:04

and executing it, modify. So the

49:06

preferred form to make modifications is

49:09

not necessarily going through

49:11

the pipeline or rebuilding the whole system

49:13

from scratch, which for many

49:15

reasons may be impossible. I

49:18

do like the idea of a small

49:20

subset of the data set that's

49:22

anonymized or sanitized

49:24

in some way, shape, or form. It's like this is

49:26

the acceptable sample

49:29

amount required for the study

49:31

portion or the modification portion. Yeah.

49:34

It could be the schema for example. It could

49:36

be the... Right. Provide your own

49:38

data in here if you can, which you

49:40

can obviously find other ways to use

49:43

artificial intelligence to generate more data. So that's

49:45

the whole thing, right? But I

49:47

feel like that's acceptable

49:50

to me to provide some

49:52

sort of sampling or as you said, the schema. I think that

49:54

makes sense to me. Yeah. Yeah.

49:57

The research is going also in the district shared

49:59

with... data cards

50:01

and model cards, lots

50:03

of that

50:23

you put up in a picture in a museum

50:26

and nobody can do anything with it.

50:28

It needs to be practical. I

50:31

keep repeating the open source definition,

50:33

how success because it

50:35

enabled something practical and it

50:37

has success because other people

50:40

have written it, other people have decided

50:42

to use it. If you

50:44

should keep on insisting from your pedestal that

50:46

you shall do this

50:49

and that, you may

50:51

not be finding enough little

50:53

crowds to follow here. If

50:57

no one is using it, what's the point? What's

51:22

up friends? I'm here with one of my

51:24

new friends, Zane Hamilton from CIQ. Zane,

51:27

we're coming up on a hard deadline with

51:29

the centa's end of life later this year

51:31

in July and there are still folks out

51:34

there considering what their next move should be.

51:36

Then last year we had a bunch of

51:38

change around Red Hat Enterprise Linux that makes

51:40

it quote less open source in the eyes

51:43

of the community with many saying, Rello is

51:45

open source but where is the source and

51:47

why can't I download and install it? Now,

51:49

Rocket Linux is fully open source and CIQ

51:52

is a founding support partner

51:54

that offers paid support for

51:57

migration, installation, configuration, training, etc.

52:00

But what exactly does an enterprise

52:02

or a Linux to get

52:05

when they choose the free and open source rocky

52:07

Linux and then ultimately the support from CIQ if

52:09

they need it? There's a lot going on in

52:11

the enterprise Linux space today. There's a lot of

52:14

end of life of CentOS. People are making decisions on

52:16

where to go next. The standard of what enterprise Linux

52:18

looks like tomorrow is kind of up in the air.

52:21

What CIQ is doing is we're trying to help

52:23

those people that are going through these different decisions

52:25

that they're having to make and how they go

52:28

about making those decisions. And that's where our expertise

52:30

really comes into play. A lot of people

52:32

who have been through very complex Linux migrations,

52:34

be it from the old days of integrating

52:36

from AI X or Solaris onto Linux and

52:38

even going from version to version because to

52:40

be honest enterprise Linux version to version is

52:42

not always been an easy conversion. It

52:45

hasn't been and you will hear that from us.

52:47

Typically the best idea is to do an in place

52:49

upgrade. Not always a real easy thing to do,

52:51

but what we've done is we have started looking at

52:53

and securing a path of how can we actually

52:55

go through that? How can we help a customer who's

52:57

moving from CentOS 7 because of the end of

52:59

life in July of this year? What

53:01

does that migration path look like and how can we help? And

53:03

that's where we're looking in ways to help automate from an admin

53:05

perspective. If you're working with

53:07

us, we've been through this. We can actually go through and

53:10

build out that new machine and do a lot of the

53:12

back end manual work for you so that all you really

53:14

have to do at the end of the day is validate

53:16

your applications up and running in the new space. And then

53:19

we automate the switch over. So we've worked through

53:21

a lot of that. There's also decisions you're making

53:23

around. I'm paying a very large bill

53:25

for something. I'm not necessarily getting the most value out

53:28

of. I don't want to continue down that path. We

53:31

can help you make that shift over to an

53:33

open source operating system, Rocky Linux and help derive

53:35

what's next. Help you be

53:37

involved in a community and help make sure

53:39

that that environment you have is stable. It's

53:41

going to be validated by the actual vendors

53:43

that you're using today. And that's really where

53:46

we want to be as a partner from

53:48

not just an end user perspective, but in

53:50

an industry perspective. We are

53:52

working with a lot of those top tier vendors out there

53:54

of certifying Rocky, making sure that it gets pushed back to

53:56

the R.E.S.F. Making sure that we

53:58

can validate that everything is there. and secure that

54:00

needs to be there and helping you on

54:03

that journey of moving. And that's where we,

54:05

CIQ, really show our value on top of

54:07

an open source operating system is we have

54:09

the expertise. We've done this before. We're in

54:12

the trenches with you and we're defining that

54:14

path of how to move forward. Okay, ops

54:16

and sys admin folks out there, what are

54:18

you choosing? Sentos is end of life soon.

54:20

You may be using it, but if you

54:23

want a support partner in the trenches with

54:25

you, in the open source trenches with you,

54:27

check out CIQ. The founding

54:29

support partner of Rocky Linux.

54:31

They've stood up the RESF,

54:34

which is the home for

54:36

open source enterprise software. The

54:38

Rocky enterprise software foundation. That

54:40

is they've helped to orchestrate

54:42

the open ELA, a collaboration

54:44

created by and upheld by

54:46

CIQ, Oracle and SUSE. Check

54:48

out Rocky Linux at rockylinux.org,

54:51

the RESF at resf.org. And

54:53

of course, if you need

54:55

support, check out our friends

54:57

at ciq at ciq.com.

55:12

Fully acknowledging that it's a work in progress

55:14

and you're not done. Given

55:16

your current mental model of

55:18

the definition as it is working, are there systems

55:20

out there today that you would rubber stamp and

55:23

say like, this is open source AI, I'm thinking

55:25

of perhaps Mistral has a bunch of stuff going

55:27

on and they're committed to open and transparent, but

55:29

I don't know exactly what that means for them.

55:32

Have you looked at anything and do you

55:34

have like things you're comparing against as you

55:36

build to make sure that there's a set

55:38

of things that exist or could exist that

55:41

are practical? Not yet. I know

55:43

that there is, we have an

55:45

affiliate organization called Ellutho

55:47

AI. They are a

55:49

group of researchers. They recently incorporated

55:52

as a file one C3, not

55:54

broken in your state. And

55:57

from the very beginning, they've been doing

55:59

a lot of. of research in the open,

56:02

the reasoning, data sets, and structure, and research

56:05

papers, models, and weights, and everything like

56:08

that. So I'm really leaning

56:10

a lot on them to shine

56:12

a light on how this can be done. But

56:15

I don't want to be too

56:17

restricted in my mind. They are

56:20

very open with an open science

56:22

and open research mentality. I

56:25

think that there is an open

56:27

AI and open source AI

56:29

that is not as equally

56:32

open necessarily, but it can

56:35

still practically have meaningful impact.

56:37

It can generate that positive

56:39

reinforcement of innovation and

56:41

pernicious collaboration, et cetera. So

56:44

yes, I need an un-looted AI,

56:47

but I'm also very open. And

56:49

I'm sure there will be other

56:51

organizations, other groups, as

56:54

we go and elaborate more on what

56:56

we actually need to what is preferred

56:58

form to make modifications to an AI

57:00

system that we're going to discover more. So

57:03

no open source AI

57:05

yet. So there's no rubber stamp for

57:08

anything out there currently. Well, I mean,

57:10

I said I could rubber stamp PTA

57:12

and the un-looted AI, but I don't want

57:15

to say that that's necessarily the only thing.

57:17

Right, there might be more stuff. And again,

57:19

those are the ones, the guys that I,

57:21

because I know how they work. Yesterday

57:24

or the other day, Altma was

57:26

released by the Allen AI Institute.

57:28

And that seems to be also

57:30

quite openly available for models with

57:33

science behind it, et cetera. I

57:35

haven't looked at their licenses and

57:37

I haven't looked at it carefully.

57:39

So I can't really tell. It

57:41

might always well be an

57:43

open source AI system. I

57:46

was trying to get to a definitive, really. Is

57:48

there not a stamped open

57:50

source AI out there yet?

57:54

Well, I can tell you what is not. Mama

57:56

2 is not. Open AI

57:58

is not. Touch it out. All right. a

58:00

denialist more than a permit list. Yes. So

58:02

I suppose one of the questions which maybe

58:05

is obvious but I got to ask it

58:08

is what is the benefit if

58:10

I'm building a model and I'm releasing a new

58:12

AI? What is the benefit to

58:15

it being open source? To

58:17

meet this open source AI definition, what is

58:19

the benefit to its originator?

58:22

And then obviously it's humanity, I kind of get

58:24

that, but like what is the

58:26

benefit? It's pretty easy to kind of clarify that with

58:29

software, right? We see how that's working

58:31

because we've got 30 years

58:33

of history or more in a lot of

58:35

cases. We've got track record there. We

58:38

don't have track record here. It's still

58:40

early pioneer days. What's

58:42

the benefit? That is a very good

58:44

question and I don't

58:47

have an answer for it. I mean I

58:49

know the benefit for humanity. I know the

58:52

benefit for the science of it and this

58:54

is what really those

58:56

benefits are what triggered the internet.

58:58

Like if software started to come

59:00

out of the labs without

59:03

the definition of true software, without

59:05

the GPN license, without the

59:07

BSD research, I

59:09

don't think we would have had such a

59:11

fast evolution of software

59:14

computer science. We would

59:16

not have the internet that we see

59:18

today if everyone had

59:20

to buy a license from Solarius,

59:22

Sun from Oracle, etc.

59:26

If a data center would have to, you know,

59:28

you would have to go and

59:30

call the Sun Microsystems or IBM's

59:32

sales team to be able to

59:35

build a data center instead of

59:37

using just boxes and slapping remix

59:39

and Apache Web Server on it.

59:43

We would have had a completely different

59:45

history of digital world or the

59:47

past. I mean completely different. So I

59:49

can see the benefit for society and

59:51

science. For some of these

59:53

corporations, I'm assuming that they have made

59:56

some of their calculations on

59:58

stopping the competition. or

1:00:01

creating competitive advantages, maybe

1:00:03

in pure Silicon Valley approach,

1:00:05

like get more users, we'll figure out

1:00:08

the business model later. There

1:00:10

is some of that going on, likely,

1:00:12

most likely. But I can't,

1:00:14

I haven't had that conversation yet with

1:00:17

any smart people I

1:00:19

know thinking about the business models

1:00:21

behind this, the possible ways of privatizing,

1:00:24

or I don't know, finding revenue

1:00:26

streams and maybe a top properties

1:00:29

open source model. Do

1:00:31

you think that they're becoming commoditized

1:00:33

if we specifically talk about these

1:00:35

large language models? If

1:00:37

we call AI that for now, recognizing

1:00:39

there's an umbrella term and there's other

1:00:41

things that also that represents, do

1:00:43

you think that they are becoming commoditized

1:00:45

and will continue to enough so

1:00:48

that open source can keep up

1:00:50

with proprietary in terms of quality,

1:00:53

or even surpass just

1:00:55

because of the number of smart people releasing

1:00:57

things? I

1:00:59

don't know, that's what I'm asking honestly. What

1:01:01

are your thoughts on it? Honestly, recently I

1:01:03

saw this new system that

1:01:05

it's a text to speech system, and

1:01:08

they built it, this team of developers

1:01:11

from a company called Polabora. They

1:01:13

built this system by

1:01:15

splitting a system from OpenAI,

1:01:18

another from either an Epic

1:01:20

or, now I don't remember

1:01:22

exactly, but they split

1:01:24

an AI system. They took it

1:01:26

and they slipped it, their input

1:01:29

for outputs, and they attached another

1:01:31

model of their own training with

1:01:33

small data sets, and they built a brand new

1:01:35

thing. I think, I mean,

1:01:38

this is the kind of stuff that

1:01:40

is inspiring, like at one point, there's

1:01:42

gonna be, I'm sure that the quick

1:01:44

evolution of this discipline would make it

1:01:47

so that smaller teams with smaller amount

1:01:49

of data would be able to create

1:01:51

very powerful machines. And maybe

1:01:54

the advantages of these

1:01:56

large corporations are now

1:01:58

deploying, delivering, and

1:02:00

distributing openly accessible

1:02:03

AI models, maybe in

1:02:06

their mind, having optimized hardware, cloud

1:02:08

resources that they can sell, maybe

1:02:11

that's where they're going with one of their

1:02:13

revenue streams, they imagined that they

1:02:15

were coming, would be coming from.

1:02:18

Yeah, that is exciting. I did see, I think

1:02:20

it was like, Codeium AI

1:02:23

just recently announced a

1:02:25

model that beats

1:02:27

DeepMind on code generation, according

1:02:30

to benchmarks that I haven't looked at, as

1:02:32

well as Copilot, and that's

1:02:34

from a smaller player. I'm not sure

1:02:36

if that's open or closed or what, but it

1:02:39

is kind of pointing towards like, okay, there's

1:02:42

significant competition. And like you

1:02:44

said, remixing and the ability

1:02:46

to combine and change,

1:02:49

and even in some cases, swap out

1:02:52

and take the best results, that we

1:02:54

will have a vibrant ecosystem

1:02:56

of these things. And I think open

1:02:58

source is the best model

1:03:00

for vibrant ecosystems. So

1:03:03

that rings true with me. Doesn't

1:03:06

mean it's right, but it sounds right. Yeah,

1:03:09

this is a tough one. This is really

1:03:11

a tough nut to crack, really. I mean,

1:03:13

even at the forums you

1:03:15

have, I believe you're calling it

1:03:17

the deep dive, right? It's deep

1:03:19

dive colon AI. And this

1:03:21

is the place where you're hoping that many

1:03:24

folks can come and organize. You

1:03:26

say it's the global multi-stakeholder effort

1:03:28

to define open source AI, and

1:03:30

that you're bringing together various

1:03:33

organizations and individuals to collaboratively write a

1:03:35

new document, which is what we've been

1:03:37

talking about, directly and indirectly.

1:03:40

Who else has invited this? Like, how does

1:03:42

this get around? How do people know about

1:03:44

this? Who is invited to the

1:03:47

table to define or help define? Is

1:03:49

this an open way to define

1:03:51

it? What is happening

1:03:53

here? Who's participating? But at

1:03:56

this point, it's now public, so

1:03:58

anyone can really join. forum

1:04:00

and can join me in the

1:04:03

biweekly town hall

1:04:05

meetings. So that part

1:04:07

is public and everybody's

1:04:10

welcome to join. We're going to keep

1:04:12

on going with public reports

1:04:15

and small working groups with

1:04:17

people that we're picking but only

1:04:20

because of agility in the collaborations.

1:04:22

We want to have, we're picking

1:04:24

people that we know of or that

1:04:26

we have been in touch with coming

1:04:29

from a variety of experiences. Say

1:04:31

we're talking to creators of AI

1:04:34

in academia, large corporations,

1:04:36

small corporations, startups, lawyers,

1:04:39

people who work with regulators,

1:04:42

think tanks and lobbying organizations.

1:04:44

We're talking to experts in

1:04:47

other fields like ethics and

1:04:49

philosophy. We keep on chatting

1:04:53

with, we have identified six

1:04:56

stakeholders categories with

1:04:58

trying to have a representation, also

1:05:01

geographically distributed from North

1:05:04

America, South America, Asia,

1:05:07

Pacific, Europe, Africa.

1:05:10

Last year we had conversations

1:05:12

with about 80 people from

1:05:14

representatives of all these categories

1:05:16

in a private group just

1:05:18

to get things kick-started. And

1:05:21

we have had meetings in

1:05:23

person starting in June

1:05:25

in San Francisco and

1:05:28

July in the post land and

1:05:30

then other meetings in the bioreg

1:05:32

in Europe like we had meetings

1:05:34

in person with some of these

1:05:37

people doing different conferences. But

1:05:40

starting this year we're going to be, this first half

1:05:42

of the year we're going to be super public. We're

1:05:44

going to gather, we're going to

1:05:46

be publishing all the results of the working groups

1:05:49

and we're going to be taking comments

1:05:51

on the forums and then we're

1:05:53

going to have an in-person meeting. We're

1:05:56

aiming late May or June with

1:05:59

at least two representatives for each

1:06:01

of the stakeholder categories to

1:06:03

get in a room and produce

1:06:05

either now to

1:06:07

the last pieces in

1:06:10

the definition, moving on the comments and

1:06:12

come out with other that meaning with

1:06:14

a range candidate, something that we feel

1:06:16

like there is endorsement from a dozen

1:06:19

different organizations across the world and

1:06:21

across the experience. Then we're

1:06:23

going to use and we're raising funds for it

1:06:26

to have at least four events in

1:06:29

different parts of the world between June

1:06:31

and the end of October. One

1:06:34

of these events will definitely going to be at all

1:06:36

things open. What we're

1:06:38

going to gather more potential

1:06:40

endorsements as soon as

1:06:42

we get to five endorsement from

1:06:45

each of the different categories. I

1:06:48

think we're going to be able to say this is version one. We

1:06:51

can start working with it and see

1:06:53

where we land and maybe next year

1:06:55

we're going to have by that time, I

1:06:57

mean by October November, the board

1:06:59

will also have a process for

1:07:01

the maintenance of this definition

1:07:04

because most likely we're going to have

1:07:06

to think about how to maintain it,

1:07:08

how to respond to

1:07:11

challenges, whether there's technological

1:07:13

or regulatory challenges

1:07:16

or just we missed a mark and

1:07:19

we realize later we'll have to fix it. Yeah,

1:07:23

kind of want to backtrack slightly

1:07:25

I guess as I hear you

1:07:27

talk about this and kind of coming to a

1:07:30

version of BLAST sometime this year based

1:07:32

upon certain details. Like when I ask you and

1:07:34

I know this is your response and not so

1:07:36

much a corporate response in

1:07:38

terms of what's the benefit of

1:07:40

being an open source artificial intelligence, like what's

1:07:43

the benefit of being open source AI? Like

1:07:46

all this effort to define it and

1:07:49

then what if there's not that many people

1:07:51

who really want to be defined by it?

1:07:53

Like I guess that's an interesting consideration is

1:07:55

that all this effort to define it but

1:07:58

maybe there is no real benefit. or

1:08:00

the benefit is unclear and then folks just,

1:08:03

it's almost like saying, it's definitely

1:08:05

a line, right? It's like, okay, everything is

1:08:07

basically not and there's very few that are

1:08:09

basically, or at least initially. Maybe as iteration

1:08:12

and progress happens that more and more will

1:08:14

see a benefit and maybe that benefit permeates

1:08:17

more clearly, then we can see it now. Yeah.

1:08:20

I don't want to think about that. I

1:08:23

don't want to think about that. No, it's

1:08:26

one of those things. Like if you start any

1:08:28

of them or think you are the winner, you're

1:08:31

probably going to fail, right? So it's

1:08:33

not one of the outcomes that I

1:08:36

see tremendous amount of pressure. I

1:08:38

mean, it's unlikely that that's going to

1:08:40

happen. That's what I want to say. I

1:08:43

have had a lot of pressure

1:08:46

from corporations, regulators.

1:08:50

The AI act has a provision in

1:08:52

there, a text that

1:08:54

says that provide some exclusions to

1:08:57

the mandates of the law for

1:08:59

open source AI. There is

1:09:02

no definition in there. So regulators

1:09:04

need it. Largest corporations

1:09:06

need it. Researchers

1:09:08

need some clarity. I

1:09:12

hear a lot of researchers,

1:09:14

they want data. And

1:09:17

they want data. It doesn't mean that they

1:09:19

weren't necessarily the original data. Some

1:09:22

of them are not. But they do

1:09:24

want to have good deficit. And that

1:09:26

only comes if there is a clarity

1:09:29

about what are the boundaries of what

1:09:31

is allowed for them to accumulate data.

1:09:35

Because data becomes very, very messy very

1:09:37

quickly. Privacy law,

1:09:39

copyright law, trade secrets,

1:09:42

illegal content, content is illegal in

1:09:44

some parts of the country or

1:09:46

some countries and some other countries. It

1:09:50

becomes really, really

1:09:52

messy very quickly. And

1:09:54

researchers don't have a way to deal with

1:09:56

it right now. They need help.

1:10:00

I agree that you should keep doing it. I didn't mean

1:10:02

to sound like it should be a failure. Sometimes

1:10:04

I think it might be beneficial to think about failure at the

1:10:06

beginning because it's like, well, you got to consider

1:10:09

your exit before you can go in in a way. I'm not

1:10:11

saying you should do that, but I'm glad you

1:10:13

are defining it. It does need to be defined.

1:10:15

I didn't mean to be necessarily like what if,

1:10:17

but there's a lot of effort going

1:10:19

into this. I can see how a

1:10:22

lot of your attention is

1:10:24

probably spent simply on defining

1:10:26

this and working with all the folks, all

1:10:29

the stakeholders, all the opinion makers, etc.

1:10:32

that are necessary to define

1:10:34

what it is. It's a

1:10:36

lot of work. It's all work.

1:10:38

You're absolutely right. This is taking

1:10:40

most of my attention. And yes,

1:10:42

I do see a couple of

1:10:44

failure options. We can fail if

1:10:46

we're late and if we get it wrong. But

1:10:50

for getting it wrong, the

1:10:52

fact that it's defined with a version

1:10:54

number, I think we can

1:10:56

fix it over time. And we really shouldn't

1:10:59

be expecting to have a perfect first time.

1:11:03

It's changing too quickly, the

1:11:05

whole landscape. And the other getting

1:11:08

in late is also part of

1:11:10

the reason why I'm pushing to get

1:11:12

something out of the door because

1:11:15

a lot of pressure

1:11:17

exists in the market to have

1:11:19

something. Everyone is

1:11:21

calling them their models,

1:11:23

open source, AI, recognizing

1:11:26

that there is value in

1:11:28

that term implicitly. But if there is

1:11:30

no clarity, it's going to be diluted

1:11:32

very, very, very rapidly. Before

1:11:34

Jared and I got on this call, one thing we had a loose

1:11:37

discussion, and I quickly stopped talking because we have

1:11:39

a term. I think it's

1:11:41

pretty well known in broadcasting and podcasting is

1:11:43

like don't waste tape. And

1:11:46

I didn't want to share my deep sentiment, although I

1:11:48

loosely mentioned it to Jared in our pre-call just kind

1:11:51

of 10 minutes before we met up. It was

1:11:54

basically what is at stake. I

1:11:57

know we talked just loosely here about

1:11:59

failure as well. an option and what

1:12:01

is failure and is it iterative on the

1:12:03

version numbers you just mentioned, but is there

1:12:05

a bigger concern at stake if the

1:12:08

definition that you come up with collectively

1:12:11

is not perfectly suited? Like does the

1:12:13

term open source in software

1:12:15

now, is the term now fractured because

1:12:18

the arbiter of the

1:12:20

term open source has

1:12:22

not been able to carefully and

1:12:24

accurately define open source AI?

1:12:27

Is there a bigger loss that could

1:12:29

happen? And I'm sorry to have to ask that

1:12:31

question, but I have to. Yeah,

1:12:34

you don't want me to sleep tonight, huh? Sorry

1:12:38

about that. I think

1:12:40

so far we've been able to win,

1:12:43

in quotes, win in

1:12:45

the public when we push back

1:12:47

on the term of open source

1:12:49

because it's pretty well accepted, right?

1:12:53

And whether, and I'm going

1:12:55

to say this, but whether we like it or not, OSI

1:12:57

has been the guardian,

1:12:59

so to speak, of that term. Some

1:13:01

say you've taken that right. I

1:13:04

think you've been given that right over decades

1:13:07

of trust. And then

1:13:09

some cases there's some mistrust and that's not so

1:13:11

much me. It's just out there and not

1:13:13

everybody's been happy with every decision you come up with

1:13:16

and that's going to be the case, right? If you're

1:13:18

not making some enemies, you're not doing some things right,

1:13:20

I suppose, in the world because nobody's going to like

1:13:22

your choices, right? But I

1:13:24

think, I wonder that. I

1:13:26

personally wonder if you can't define this well,

1:13:29

does the term open source change or

1:13:32

is becoming open to change? There

1:13:35

is that raise cover where, but that's one of

1:13:37

the reasons why I'm being extra

1:13:39

careful to make sure that everyone's

1:13:42

involved and have a voice

1:13:44

and has a chance to voice their opinion

1:13:46

and all of these opinions are recorded

1:13:48

publicly so we can go back

1:13:50

and point out the

1:13:52

place where we made a bad choice and

1:13:55

be able to correct or not. Yeah.

1:13:59

Stefano, real quick. What's the number one

1:14:01

place people should go if they were to

1:14:03

get involved like the URL? Here's

1:14:06

how you can be part of that discussion

1:14:08

discuss the open source org. There we go

1:14:10

Yes, well, we're gonna be having you know

1:14:12

all of our conversations. All right,

1:14:14

you heard it. That'll be in the show notes so

1:14:17

if you are interested in this even if you just want to

1:14:19

listen and be Lurking and watching

1:14:21

as it makes progress definitely hit that up

1:14:23

if you want your voice heard and you

1:14:25

want to help Stefano and his team Make

1:14:28

this definition awesome and encompassing and

1:14:30

successful. Yes I think the

1:14:32

more voices the better the earlier on the better So

1:14:35

that we can have a great open

1:14:37

source AI definition. Thank you. Thanks

1:14:39

Stefano. Appreciate your time. Thank you so much

1:14:41

Thank you It's

1:14:46

a big question mark with the future

1:14:49

of the open source AI Definition will

1:14:51

be well the first draft of the

1:14:53

open source AI definition is linked in

1:14:55

the show notes I highly encourage you

1:14:58

to check this out dig in learn

1:15:00

about what's happening here voice your opinion

1:15:02

If you have a strong opinion, but

1:15:04

definitely pay attention as you can hear

1:15:06

with some of the uncomfortability With

1:15:09

the questions we asked about what

1:15:11

happens if the open source AI

1:15:13

definition Falls a little

1:15:16

short or what the ramifications are

1:15:18

or potential impact might be? I

1:15:20

think we all need to pay

1:15:22

close attention to how this definition

1:15:24

evolves and lands links are

1:15:26

in the show notes So check them out

1:15:28

and again Thank you to Stefano because he

1:15:31

did have a cold during

1:15:33

this conversation And he

1:15:35

powered through because he knew this was an important

1:15:37

conversation to have here on this podcast and to

1:15:39

share with you So thank you Stefano

1:15:42

up next on the pod is our friendly-turned-friend

1:15:45

Jamie Tana coming up on friends and

1:15:48

next week. It's about making your shell

1:15:50

magical with Ellie Huxtable Talking about a

1:15:52

tune check it out at a tune

1:15:55

sh Okay, once again a

1:15:57

big thank you to our friends and our partners

1:16:00

at fly.io, our friends

1:16:02

at typesense.org, and

1:16:04

of course, our friends at

1:16:06

century.io. Use the code

1:16:08

changelove to get $100 off the team

1:16:11

plan. You can do so at century.io.

1:16:14

Okay, BMC, those beats are banging. We have

1:16:16

that album out there, Dance Party. I don't

1:16:19

know about you, but I've been dancing a

1:16:21

lot more because that album has been on

1:16:23

repeat on all my places that I

1:16:25

listen to music. So I've been dancing a lot. Dance

1:16:28

Party is out there. Check it out

1:16:30

at changelove.com/beats. That's

1:16:33

it, the show's done. Thank you for tuning in. We'll

1:16:35

see you soon.