Hi everyone.
Welcome to PyPodcats, the Hidden
Figures of Python podcast series.
The PyPodcats is, a podcast
highlighting voices from
the underrepresented group
members of the Python community.
So we do have a Code of Conduct
that is following
the PSF Code of Conduct.
And if you're interested
to know more, you can always,
check out our website.
Very excited to be back because
this is 2025, probably the first
guest for our 2025 podcast.
My name is Georgi and I'm one
of the PyPodcats hosts
and I'm based in Amsterdam.
Also one of the PSF Director
of the Board.
And to you, Mariatta.
Hi everybody.
I'm excited that we are
resuming the PyPodcast series.
My name is Mariatta, I'm based
in Vancouver, Canada and I'm
in the Python community.
I've been contributing for many
years as open source contributor.
I'm also one of the Python core
developers and, and I'm also
involved in the PyLadies community.
Our guest today is Tamara.
Tamara, can you introduce yourself?
Hi.
Thank you so much for having me.
I'm also really excited
to be the first guest for 2025.
So I'm Tamara.
I, am currently an Open
Source Software Engineer.
That's my title
at a company called Probabl.
Probabl is the brand ambassador
for scikit-learn.
And my job there is I work
in the open source team
so I'm sponsored to work
on open source packages.
One of my jobs, includes
maintaining Fairlearn that I
hope we're going to talk about
a bit more later about.
And I do contribute to scikit-learn
and I've contributed to another
package recently called Spops.
At the same time, some of my time
in the week goes to research.
That's going to become
my master thesis.
I am part of a program that's mainly
about computational linguistics,
but also has some cognitive science.
Yes, that's me at the moment.
I've also been involved,
I guess in the Python community
for over a decade.
So there's a lot different.
Yes, since 2012 it's been a bit.
Wow, it's been a while.
Yeah.
That's crazy.
So, yeah, you were just
talking about Fairlearn.
So what is Fairlearn about?
It sounds like a game for me.
With the name
Learning Fair.
Yeah.
So Fairlearn is a, I guess the best
way to describe it is a toolkit
is a library that it provides tools
for data scientists to work on.
Increase the AI safety
of their applications.
So I think if we don't go too
deep we can think of FairLearn
as a library that provides
metrics and also provides a
specific kind of methods for
assessment and mitigation of
unfairness in this case.
But it is based on a specific type of
fairness, which is group fairness.
And what does that mean?
I think it's important to address
is that we have tools for people to
evaluate how a model would perform
for different groups of people.
And in Fairlearn we call this
groups of people sensitive
features but they can be any kind
of subgroup that you want
to make sure the algorithm or
the model performs fair for.
We can think of any
examples like sex or race or any,
any really combination
of the subgroups as well.
This is based on something called
disaggregated evaluation.
And these are the metrics
and the other methods help with.
Do you have any like case
study of a company
or a project that uses Fairlearn?
I love this question because
it's a, it's an interesting
problem that we have.
We do really love working
on this and it's part of my
the research does, I read
papers around this method.
But one of the core problems is
that a lot of the people who do use
the library and there are plenty
because I think in December
there were half a million downloads.
Whoa, is that.
Yes, it's big and it's
interesting that it's really hard
to get any data about it.
And this is as somebody that's
just getting in this area
from an academic standpoint,
I wasn't in it before.
I learned from the senior
researchers that a lot of the
companies do not really want to
discuss the usage of these kinds
of tools and would definitely
not make any of their data sets
available.
But if you go on the website
I think this is one of the biggest.
Despite the excellent metrics
and really careful editorial,
decisions that are made and we
really do not add anything that
hasn't been mindfully vetted.
In a sense one of the biggest things
that we focus on is education.
So if you, if somebody opens the
website there is a place with
example notebooks and there in the
example notebooks there is one
white paper that was made by
Microsoft who started the library
many years ago.
But now it's community led.
Ernst and Young, if I pronounce
that right, that worked on
credit fairness in the states
between male and female applicants.
And it's a really interesting
case study to read of course,
one can assume there are
constantly this kind of scandals
coming out in the open.
You could assume that somebody
uses something somewhere.
Sometimes people from governments or
from different institutions come
to our community calls which are
by the way open for everyone.
They happen every second week
at 6pm Berlin time.
I don't know what is that
in other time zones?
Easily.
People can easily find it
on the website.
The time zone problem.
Yeah, most of the people that
contribute are coming from the U.S.
so they, they always say their
time and I always forget because I'm
in Europe, in Berlin.
Yes.
So this is a very
interesting question.
And if anybody's listening
to this that's using
Fairlearn, we have a survey,
that we just recently made.
One of our maintainers, Michael
created it for this specific purpose
because we are a bit in the dark.
We would like to serve the community
better, but we would
need the community to,
to be brave to talk to us about
how they use the tools.
Yeah, okay.
Yeah.
Share the link with us.
Maybe, we'll share it when
would be amazing.
Yeah, yeah.
So people who use Fairlearn
share how you actually use it.
Yeah, I find it, it's interesting.
Do you think people hesitant
to share like they don't want, they
don't want to tell that they are
using Fairlearn or what is,
is there a reason they are not?
I think a lot of the models
will have maybe non ideal results.
Now this is another
interesting question.
So one thing is to assess unfairness.
You would start for example
with defining your sensitive
groups and understanding what
your specific use case is.
It always has to be specific.
And then you would pick the right
metric that different metrics
like demographic parity
of equal odds, you can assume what
they mean maybe by the name
where we can discuss them.
But after that you can use some of
these to assess how the model works.
In case of demographic parity,
for example, is this you know, equal
for the demographics involved?
But the mitigation itself,
it's a, we do provide some tools
for mitigation in the library.
And there are many, many
other ways to do mitigation.
But mitigation, it's really not easy.
It's a very case to case, basis
and the answer might be in your
data, in adjusting your predictions,
in balancing some other ways.
There are other ways
of mitigating with intermediate
representations and so on.
It can get pretty complicated.
But only you can do this
in your own use.
And it's not easy to do after
you've set up everything to redo.
Because fairness has to be
from the very beginning considered.
When you create a product
or a model, it has to.
The idea of it being fair has
to be woven into the whole process.
And I think a lot of people maybe,
yes, they assess at the end and then
I don't know what they do.
Right.
What if like halfway through
they say, oh, actually
I forgot something that we
should actually add on to it.
What did they do?
I don't know.
I would like to know what people do.
I, I recently became a, part
of a very, very big Slack
channel and I was asking people,
hi, how do you use fairness?
Please tell me something.
Nobody told me anything still,
but maybe somebody will.
Okay, yeah, hopefully we get to
share your survey and so hopefully
you get responses and you get
to learn more about the user.
I do believe there are some maybe
like big companies have idea
about this in their own internal
teams and senior researchers.
But you cannot say
anything about this.
Yeah.
So let's bring it all the way
to the beginning.
Like we are here talking
about the Python community.
So I'm actually kind
of interested to know how you
actually started using Python.
Yeah.
So Python wasn't the first
language that I work with.
I started during my studies
in the late 2000s.
They didn't really teach
Python at universities.
We studied C and C++ I believe.
And then I did my first Google Summer
of Code contributing gnome.
It was in C>K if I
remember it was 2011.
And then I was also
very politically active at the time.
And I went, I was, it was time
for the second GSoC.
And then I was okay, I want to do
something that's really specific
about citizen participation and
works really, really directly
addresses, not indirectly as any
open source would, but directly
addresses a political issue.
And I found a small project
from Spain called e-cidadania.
I don't know if you pronounce
that right now, I am
in Spanish, but it was a citizen
participation platform.
Exactly.
And it was in the time it used
Django and JavaScript to create this
little kanban like discussion,
if I can describe it now.
And it was supposed to be used by
the local government, if I remember
right, to increase the participation
of the local people who live there.
And we worked on that.
It was really fun.
And then I think I just never
stopped working with Python
because I learned Django
and I felt so empowered.
I was, I can do anything now.
Yeah, because Django was, wow,
these kind of frameworks
in 2012, 2013, I was like, I can
build any website I want.
And then I, then I also had
a job and a think tank.
I worked at a think tank
as a software engineer.
I had a little team and we did
projects that were meant for,
to increase either awareness
or address social issues.
It was really still my favorite
job so many years after.
And this is.
I could directly just use my
skills there and I felt just.
Yes.
And I still do Python.
That's very cool and I love that.
I think we need to
highlight that quote.
Just now like, with Django,
you feel so empowered.
I remember thinking
I can, I wanted to.
I can do everything now.
I know I, I'm a big fan
of the Django web framework myself.
That's what I also use
professionally.
So.
And I totally agree.
Like, I mean right now I'm like
I'm trying to replace all
my spreadsheets with Django.
I'm trying.
But yes, I love it at the end.
It will be easier to edit eventually.
Yeah, yeah, I think.
Yeah, yeah.
So you know, It's Django
in 2012 that was.
I think it was like Django one point
something probably, or Django two.
I don't know that it's
a long time ago.
I wonder if you had encountered any
difficulties or other
limitations when working with
Django and Python back then.
So I think, people can.
It's archived now on GitHub, our
repository from then, but they can
go and still see the type of commits
and PRs that we did in the past.
They're not what I would do now.
But I am not embarrassed
from my beginnings.
They can check out how
we did Django then.
Yes, there were plenty of, growing
pains as somebody that only did
C and C++ and thought in a very
different way about programming.
And I never did web before
in the, in the time.
And it was all I had to start doing
to apply for the GSoC.
I really, really wanted it.
And I was part of a hacker space
for those few years already.
And it's lovely because when
you're part of a hacker space
you have so many people
that are just there to hang out
and hack and code and mentor.
And there was a person called
Vladan, which is shout out
to Vladan if he ever hears this.
And I remember that he was a great
help to just push me to learn
and get this through this.
Growing pains faster.
Yeah, I think there was a lot
in the, in the time that
it wasn't just learning Django,
which maybe I don't know.
I think a big part of learning Django
is learning how the web works.
When you're beginning with Django,
I think, I knew theoretically
from my studies because we
did networking and so on.
I have a Computer Science
degree that I did before that.
By the time I had it finished
by 2012, I was trying
to, I started the masters.
But yeah, I think, I think it
was just so lovely and it was very
different from my C>K
experience the year before that.
It was no going back,
but it wasn't, it wasn't fully
straightforward, that's for sure.
It's just, it's hard for me
to remember was 13 years ago.
I remember somewhere, somehow
in one of my high school or
polytechnic days we have basic
Computer Science and I learned C
there and I un-seen it.
Now I C and un-C.
Yeah, it was a lot.
C.
was a lot.
I think people still do it
now, I guess, but they are.
They do, they do, they do.
It's CPython.
Exactly.
Yes.
When we built Python,
it's still used.
But yeah, I also, I, I use Java and C#
before I learned Python,
but once I use Python, yeah, I'm.
This is the language I feel like
makes sense to me, you know, it's.
Also, it's so versatile.
So in back then I did web and then
I did web for a few years and
then in 2015 I got a job where I
used Python to write hardware
bindings, which was for music
instrument.
It was really fun.
And then I, then I transitioned
to doing machine learning and NLP,
which one my degree is in.
So Python is very versatile.
It is, it's not just
Django that's empowering.
It's like you have a language
that will take you through all
of this that you can
lean back as a basis on.
I love that.
Can you talk more about your
involvement in the Python community?
Like how or.
I know you said you started using
Django and Python, but that's using
the language but then you become
involved in the community.
Can you talk more about that?
Yes.
So, I guess by getting involved
in Python as a community slightly
related to my immigration because
I come from a country
that's now called North Macedonia.
Wasn't called like this
where I lived there.
But so in there wasn't any
separate Python community,
although I already contributed
open source-wise to Python.
It was we were all one
technical community so to say.
And since I was mostly I was tightly
intertwined with a Gno,me community
before went to their conferences.
I was involved in the pre-
Outreachy kind of groups.
And so on.
I did program in Python
but I never saw the community
as a separate thing.
And when I immigrated or was trying
to immigrate to Berlin in 2013,
I get to know the PyLadies.
The PyLadies.
This is one of the people,
the first people that showed their
welcome for me in the city.
So it's still my longest
standing relationship and I
really appreciate it.
I don't think I would have
moved if they did not
were so welcoming to me.
There's Meili.
I don't know if she's ever gonna
hear this but she's still my friend
and somehow I met her and she
invited me to give a talk about
all this political projects.
I did politically related projects
that were connected
to raising awareness or.
And they were all in Python
at the time.
As I said, empowered
to do everything.
And I remember doing that talk.
I don't remember anymore.
I cannot find any of the slides.
But I went.
I had to go back to where I was
from because you cannot apply
for a visa from Germany.
And I had to figure all of that out.
And when I came back the community
was there and that was really great.
And some of those people that I've
met there are still some of my
closest friends where I'm here.
So Yeah.
I have never been truly organizing
in the community
but I've given several talks.
My most recent one was in January.
I always go back when I have
something new and what I really want
to share with with the community.
And I, I mentor in sprints.
I've mentored Django girls
and similar kind of events.
Wherever there is help
needed of the sort.
Yes.
So I guess in that way I do.
Except well separate from
my open source work I did.
It's.
It's worth mentioning that during
the pandemic and when my, When I was
a very new mom, that was more rare.
I think I do a bit more now
than I did in those years.
Wow.
But that.
That sounds a lot like you
are still pretty involved.
I mean right from the beginning till.
Till what you are at right now
and like getting involved
in code, on the contribution,
that's quite a bit of time there.
And plus the community,
plus getting, sharing your.
Your knowledge to the community
by giving talks and then
finally adding it to like providing
giving mentorship which is like wow.
From zero to all the, all
the level, from beginners all
the way to the expert
levels, you cover everything.
So thank you.
Yeah, I mean it's, it's really
amazing to see someone like you who
are so, so passionate about sharing
what you know to the community.
And I think because of people like
you, we get to learn more and we
get to understand a lot, a lot
more about how we can use the
different cases and how we can
actually learn literally when you
mentor people.
So I'm actually very, very
interested to know like
how do you work on mentorship?
Because I recently had
very interesting conversation with
Djangonauts Space people and we are
talking about how we could actually
improve on mentorship.
So I would like to hear from
your point of view, like,
how do you actually started
like mentoring people
and to get people to understand or
to get people to learn?
Because people, when they
learn they come from all sorts
of all walks of life.
I think I have to mention something.
For me, mentorship is part
of my core values and it's related
to justice for me.
I come from a working class family
that didn't have a lot of resources
and it was really hard
for me to stay and get access
to more education programs.
Like the way my hackerspace friends
helped me with those Google Summer
of Code projects, made me pay off my
student loans that I had and I
wouldn't be here without them.
And the effort I do for me is just
a way to keep this going,
taking people with me on the journey
and it's never going to stop.
It's just part of my ethos,
in a sense.
So every time I show up, I show
up with this in mind.
So every time I get an idea
to talk at the PyLadies, it's.
I learned, I think in my last talk
I said that my company also does
certification for Scikit Learn, so
maybe people should check it out.
It's, they're really doing
a great job with it.
And there was an expert topic they
they mentioned and I was like, okay,
they mention it as an expert topic
as part of the expert certification.
I'm making a talk immediately
to bring it to the PyLadies because
I want these people to live the room
feeling as experts in that night.
So this is what guides me.
What got me into this was this,
of course, this experience with
the hackerspace which predates this
immigration experience and all
the more deep Python involvement.
And also I gained some skill by being
a TA at the university. My first
degree, that was a really long time.
I think their skills were refined.
I have a very different
approach now than I had when I was
a child at university,
so to say, a young person.
Yeah.
And for the, how do
we, how do we mentor.
I think there as open source in
open source contribution sprints or
for people to get involved.
There is a specific, I do have very
specific ideas there that I'm
trying now as a first, I'm a, I've
been involved for so long, but I've
never been a maintainer and I've
even had keynotes to talk to other
people.
I never had the chance
to any, to implement anything
of what I've been saying.
I could only hope that other
people would implement it.
And now I'm trying to pour into
the project as much as I can and it
shows in the way we do our sprints
and the way we try to retain people.
Some things for me that
are really, really important are
structure and process and a focus
on continuous involvement.
And I'm really trying to work
on these aspects at the moment.
So not just getting people in, we
can do a sprint, but we want people
to stay and work with us.
We want them to feel nice and welcome
and necessary and useful and to
take this knowledge out as well
out in their other projects.
Could you share some examples of
how you plan to implement this?
Like I know you mentioned you have
some ideas and you felt like
because you weren't a maintainer
you couldn't implement this.
And that's an interesting topic.
Maybe I'll ask about it
a little bit later.
But now you have these ideas.
Can you share a little bit,
a snippet of what it is?
Yeah, I think for different projects
it will mean a different thing.
So for example, things that
I've worked on recently
that turned out to be great.
I just.
When was it?
It was three weeks ago.
Two weeks ago we had a sprint.
And I.
That's very recent.
but it wasn't
the only sprint recently.
So I spent two weeks before
the sprint digging up,
first issues that were.
There was a gradation there
but they were all separate,
not part of a giant one.
They didn't involve so much thinking,
but they were perfect for teaching
the process and making closing.
So making a PR that can be
also merged really quickly.
So everybody that participated in
the sprint made a PR by the end of
it and everybody was really happy.
So we don't know how many of these
people are going to stay but for
example, in our Discord channel, I
constantly post whenever there is
a new issue, how difficult it is.
I offer help for people to continue.
And we had, for example
from the PyLadiesCon,
we had a person that joined.
I was just me online, there not.
It was not in person.
Right.
It was an online conference and we
had a person that did heard about
the project, but didn't really have
time to participate and now is still
one of our most active contributors.
Yes, shout out to Neha.
Yes, she's lovely.
And for example, right now she
is interested in helping with
some of the project management
efforts that we're doing.
And it's really cool.
We have an issue together at a link.
She has been a collaborator
to redo all the documentation
for newcomers as well.
The perspective of people
new to projects,
is really, really valuable.
And right now I think
this is the biggest project
that I want to do.
We seem to be attracting
and people are really happy
at the sprints, but there is a bit
of a structure and process
lacking for them staying.
And although we have a lot
of downloads, as I mentioned,
the project is really small.
There are not a lot of maintainers,
that are very active or just.
It's just a small project.
It's okay as it is for just
the maintenance, but for growing or
for including people, we need to do
a little bit more and we want to.
So right now I'm having an initiative
to organize things in
little groups so people, when they
come into the project can be
interested in a specific group.
So to say it doesn't have.
They mean they can also build
expertise and be an expert person
in the documentation.
That is awesome.
But it needs to be presented
in a way that it shows a path.
We don't have that right now.
I think a lot of big projects
definitely have this.
That's why I say it's dependent
on the project or things like, other
types of improvements because it's.
People come to a project like,
okay, I'm going to contribute,
let's do this, a little bit of that.
But a big Python code base
doesn't have just one type of thing.
I've been.
Most of the things I've been
contributing for this last month
have been Scikit Learn, cross
compatibility because I also
work with Scikit Learn and
improving Fairlearn to be
seamlessly used in the Scikit
Learn pipelines and so on.
And this is just one type of thing.
You don't have to be an expert
in anything and I want to make this
explicit by, by categorizing thing
so people can find their own niche
and feel confident in it.
So not just oh the for example
the FairLearns methods are really
quite a bit of advanced knowledge
in maths or statistics and this
can be very intimidating.
Somebody joined the PyLadiesCon
and told me that they felt
more intimidating contributing
to the Fairlearn compares than
the Python core which then
I felt okay, we need to, yes.
Fix this.
I think some of our
methods look very cryptic.
We need.
And we are definitely
working on fixing it.
So this is one of the efforts.
Yeah, so that's what I mean.
You don't need
to understand this math.
There's so many other places
you could be essential.
There's just so much
work always to be done.
But it's not explicit and we're
not showing people gradation
how they build the skills.
There's also a lot of, volunteer
time is precious and you
know maintainers need to support
like as a maintener,
Easy for me to get lost
in my little technical hole so
to say digging endlessly.
But I think I'm more useful.
unblocking others
and supporting the project to stay.
So this is my, I'm doing the more
grunty work myself because I want
others to you know do this kinds
of things for our project.
This, we don't have any
of this but I, I've been involved
in projects like Gnome
which are my inspiration.
I have to say I don't know
how it is right now.
I have not been contributing
for such a long time and they
had such strong organization
where you could feel that
every contribution is really valued.
Every type of contribution.
That's something
that we have lacking a
little bit still the code,
a few appreciators.
Yeah.
But also every type, not just
the programming and specific type
of programming being celebrated.
Yeah.
Because everything
is really valuable.
It's true, it's true.
Yeah.
I feel like the fact that
you phrase it that way.
I do feel that there is
this, how do I say it?
Like appearance like that
code contribution is
being celebrated more compared to
other kind of contribution.
Like it's hard to.
Yeah.
I don't know how to address that
I mean every contribution counts
because yeah Open source
doesn't mean it's zero effort.
Anything that anyone volunteers
to do something, it matters.
Absolutely.
But I don't think that people feel
this sometimes in the projects where
they work as somebody that has been
mostly contributing code to project
and wanted to do other things.
I could see the stark difference when
I would be in the different types
of contributing and also when I did, for
example, I did several jobs where
I had developer relations jobs.
And you could feel also
the difference there, how you're
perceived and how your
contributions are perceived.
I think this is a mindset, but it has
to come from the maintainers.
This true.
There's also a very common
discussion point that we talk
about is that a lot of people,
when they start sprints, it's
really hard, like to prepare for
the new contributors because
sometimes a lot of maintainers,
like you said, you're already
really deep into your project.
So your mindset is.
I already know that.
So some of the people who are new,
they get to like confused like
even the very basic on how to start
and how to get things going.
So like say, for example,
if there's one person coming
to join, it's easy.
What if like there's 10?
How do you prepare to get so
many different people to start?
So I wonder if you have a specific
idea how you manage that.
So for this particular sprint, I had
10 people and I was from my project.
I do had some help
from the second, people.
It was lovely.
I honestly, I think there's nothing
that fulfills me more than a group
of people, getting to know
and starting and seeing them glow by
the end because they've made a PR.
I.
That's.
It's just the most
fulfilling thing ever.
So I, So how we prepared.
There is an issue in Fairlearn, if
anybody's interested, that I started
to prepare for the sprint.
I did it with Neha.
Neha is great.
Thank you, Neha very much.
My main collaborator on the issue.
We did several points.
One of them we still
haven't fulfilled.
And for example, we went through
all the documentation and Neha had
ideas and rewrote some of it.
For example, we didn't have a step
explaining how to do a virtual
environment because this is just.
I.
Sometimes I just don't
think about it.
But it's.
Yeah, it's something
that we should do.
So we added these kinds of things.
We redid some language, added
some more links, added Code
of Conduct everywhere because we
didn't have it in enough places.
We redid the readme so it's
more easier for people to
just jump on different places.
What else?
There's more things that we did,
I don't remember.
The only thing we didn't
do was Create videos for
people to watch at home.
That is coming next.
I have a person that volunteered
to do it for Windows.
I don't have a Windows machine
and I will do it for MacOS.
And I.
And the main thing I did myself was
digging out just the right
type of issues because I did
study a bit of cognitive science
and early childhood development
and language and so on.
And one of the interests, biggest
interests that I have is learning
and learning itself for the brain.
And one of the biggest problems
with language learning for example
is the gap from B1 level
to like getting the next levels.
And this is because
the gradation of tasks disappears.
For somebody to be challenged
but not overwhelmed and quit.
You need the right type of task.
It needs to be not too hard
and not too easy.
And this is how you
go to the next level.
And this is the same type of thinking
has to be applied to an issue.
For a Sprint, for example, if it's
the documentation works,
they're not big hiccups.
There's the issue itself is teaching
you the flow does not require
an immense amount of digging.
It's just loud.
There are so many people.
So somebody can do this semi
independently after you have
unblocked them in some way.
That is a really good,
good first issue.
There could be a follow
up but the first issue
should be really easy.
It is a very hard thing
for projects too.
I hit them for good first issues.
Labels.
I put them under reserved
for Sprint for two weeks.
So they were not available
for the general public.
This is.
Yes, that will happen
every single time.
But yeah, we had some, some
for the, for the public.
I see that you're contributing
everywhere and things like that.
Like other than that, aside from
code and community, do you do any
other things like do you
have any personal interest or.
Yes, I guess I spend most
of my times when I'm not working or
doing work related things or
community things with my son.
I have a child, he's
gonna be seven soon.
Well very soon.
It's probably my favorite part
of the day most of the time.
Yes.
I also have a little blog or
a substack, however they call this
thing newsletter these days.
I moved my blog there and I
write about language, technology
and society which
are my main interests.
I don't write very often but if
people want to hear me talk about
these things they could subscribe.
Yes, I love to read and I love
literature and everything.
Literature again ties to my love
for language I have to say.
And I do take care
of a lot of plants.
Cool.
At the moment that's like a lot of
things that you, you're organizing.
Right?
Those are the recharging things.
Those my, my child and my books
and my thoughts about
language and, and society are
just, it's where I, I, I draw energy
from and sprints as I said.
I don't know.
That sounds like de-charging for me.
It's recharging for you.
So.
So with so many things that you have
in your mind, what is your plan like
you, that you want to work next?
Because it seems like you have some,
quite a lot of things going on like
the plans that you have in future.
I think in thinking about it
a lot personally as well.
I've spent the last 15 years
really guided by curiosity
and a giant desire for technical
newness and depth and just
creativity of that kind.
And these days, especially this
year, I realized that more and more
I get fulfilled by unblocking
others and growing others and
helping fostering environments
where things can involve as many
people as possible.
So I think in the next few years I
want to think and implement
initiatives in the projects I'm
involved in or as my day to day
job where I can uplift others and
empower and support and unblock
people.
I think that is, that's my
main focus at the moment,
or I would like it to be.
I'm still a software engineer
mostly at the time.
Still the idea of giving back, giving
back, like what you learn, you're
just, it just sounds so generous.
Like whatever you learn, you
just want to share and let
everyone know what you learn.
I mean I've been loved and helped.
Right.
It's only just to
continuously give back.
Wow.
We need 200 Tamaras out
there and the community
thrives immediately.
I think when I look at my friend,
Jessica Greene for example,
you must know Jessica Greene.
There is one at least that I
know that my friend Meili and all
of these other people
that I'm in touch, they're just
really, really generous.
They've been generous with me
and I want to be generous with
the world too as, as much as I can.
You know, we have seasons
in life where we can't and seasons
in life where we can definitely.
This is one of, this is one of those.
We don't know how long they last.
So true.
Yeah.
Well we have to, I mean
to represent the community.
I want to say thank you because this
is like really something that we
need to celebrate and thank people
like you who contribute so much and
to, to put in so much time and even
plan you are even thinking like,
what am I gonna do next to make it
better?
That's like, wow, crazy.
Thank you.
There's really no need.
Definitely.
I don't know.
There are so many people that
do way more, that's for sure.
The new here.
I mean, this is, this
is a lot of work.
So if, is there anything that
you, would like to share before
we close, this podcast episode?
Like, you like to share with our
audience on something that you
would want to give a shout out to?
Before we could end this episode, I.
Think it would be nice if people
try Fairlearn, give us,
an idea what they could use it for.
And we always have good first issues.
They can come work with us.
We have a Discord community
when we're always available.
And I'm always happy to help
people come into the community.
So I guess an open invite to anyone
that wants to join the project
in some way and help with AI
safety and algorithmic fairness.
So perhaps, you could also give us,
where they could reach out
to you if they would want to
reach out to you for mentorship.
I am on the PyLadies Slack channel,
but if they're not a PyLady
I only have a LinkedIn account.
I don't have any other social, media.
Okay.
And, I don't know if Substack
has some kind of feature
to talk to people, but, I think
LinkedIn is probably better
because I don't, I don't know
the answer to that question.
Okay.
You can see my last name,
I guess in the recording
and then you can find me.
And I'm happy to accept
anyone that sends a request.
Of course.
Sounds good.
So thank you very much,
Tamara, for your time taken.
It's still like, I'm still so
amazed by so much that you
have done over the years.
You can compile
and probably it's like.
It'S been many years.
There was time.
Yes.
For all those who are listening, you
can actually find us on, pypodcats.
That's pypodcats.live
on YouTube channel
on Apple Podcasts and Spotify.
For this episode.
You can also reach out to our website
and get more information about
the projects on tomorrow and how,
how you can reach, Tamara too.
Thank you so much everyone
for joining and, we'll see
you in the next episode.
Bye Bye.
Thank you so much again
for inviting me.
Goodbye.