Welcome to Community & Code,
the podcast about the
people behind the commits.
I'm here today with Brendan O'Leary.
He is a developer relations
engineer at Kilo Code.
Welcome to the show, Brendan.
Tell us about yourself.
Yeah, thanks for having me, Chris.
Glad to be here.
Yeah, I'm Brendan.
I work in developer relations.
I've been in developer
relations for a long time,
but longer than that,
just kind of been involved
in coding and shipping code
to production and kind of
everything that goes along with that.
You know, I've worked in product,
I've worked in engineering, and then,
yeah,
kind of found my passion in
developer relations and
helping other developers ship code, so...
So I've been in DevRel for
almost a full year,
although I feel like I mean,
I've said this before, too.
I was doing forms of
developer relations before
I was actually like had that as a title.
And also one of the things
that I've observed since
kind of being in the space
more and meeting other
people that are doing
DevRel is that it means a
lot of different things depending on
the organization, the team,
where it sits in the org structure.
What does developer
relations look like for you at KiloCode?
Yeah, that's a great question.
And yeah,
where it sits and all of that is
like the best part of
developer relations is bike
shedding what developer relations is,
right?
Yeah, so Aquilo Code, you know,
we're really focused on
delivering an agentic AI.
It's an open source application.
a VS Code extension that
helps folks use all the
different AI models that
are out there to try and
supplement what they're
doing as an engineer.
And so developer relations
was an early focus for us
because all of our customers,
all of our users are developers.
And I think that what I
always say about developer
relations in general is
like a lot of folks like to say, oh,
it should sit in
engineering or it should
sit in marketing.
And at the end of the day,
like some developer
relations folks get mad at me about this,
but at the end of the day,
it is developer marketing, right?
Like that is what it is like.
Like, obviously,
I think it's obvious to
everyone or people that are in, you know,
software that developers
don't like being marketed
to through traditional channels.
um but would rather be able
to try something put their
hands on it understand how
it works maybe even inspect
the code behind it right
that's why open source is
such a popular methodology
and so in order to enable
that I think you need
a human connection with
people and so that's what
we are really focused on is
how can we connect with
people and make them
successful with the tool uh
and then kind of the rest
of it is the score takes
care of itself right like
all of all of the revenue
and all the other things
can follow that uh but if
you don't execute on that
well you're just constantly
fighting uphill uh if
you're if you want to you
know try and make something
work uh with developers
So tell me about what Kilo
code is or does and about
sort of the AI integration
that you're building or
working on or sharing with other people.
Sure.
Yeah.
Yeah, that would be great.
I think, yeah,
so I'll kind of give you
like maybe a little bit of
my path to there,
which I think then answers
what we're doing and why I
think it is a way of
attacking this problem.
You know,
I think there's a lot of like AI
maximalists out there that will say,
you know, for the past two or three years,
we've said, oh,
we're six months away from
the never being an software
engineer anymore.
And then there's a lot of,
I think also skepticism from the,
you know,
like rightly earned skepticism
from developers and
software engineers who have seen either,
you know,
the slop that early AI models
produced or,
or the kind of vibe coding
methodology that has not
been super successful for
people that don't
understand how the code works.
So I would say,
I think that the reality is
somewhere in the middle.
I say,
I don't think that AI is going to
replace software engineers,
but I do think a software
engineer that uses AI is
going to replace software engineers.
I think it's a tool that
represents probably, yes, a step change,
but again,
I don't even need to be a
maximalist to say that
If you look at statistics
from Stack Overflow,
if you look at what people
are talking about in the
SEO world just in general
with the rise of models
like ChatGPT that are
making massive traction
throughout the world,
not just in software engineering.
I think the reality is that
if you're an engineer today that's saying,
yeah, but AI, it's a fad, it'll pass.
That's like somebody saying, oh,
Stack Overflow, that's a fad, it'll pass.
Or, oh, Google, yeah, that's a fad,
it'll pass at those times.
Is it overhyped and probably
the latest overhyped
venture capital bubble?
Sure.
But is it going to just go
away one day because of that?
No.
There was a time when people
thought of the Internet that way,
or they thought of the web
and dot coms that way.
And yeah,
there had to be this equilibrium
to find reality.
But the reality now is that
we all have the Internet in
our pocket and at our
fingertips every minute.
I think that's going to be
the rally for AI.
So sorry,
that's like a very philosophical
way of coming to, what does Kilo Code do?
I've got questions,
but we'll come back to them.
Yeah.
So yeah, Kilo Code lives in VS Code.
So it's a VS Code extension.
And then we just interface to the models,
right?
So like we're not inventing
any foundational model.
But we allow you to say, oh,
I want to connect to Sonnet
four or I want to connect to Gemini.
Right.
Like what model do I want to connect to?
And then our tool has a
number of agents that can
then take action.
Right.
Like an orchestrator that
that take plans out with the agents,
a architect that doesn't
just code right away.
It plans out the architecture,
what it's going to do.
a code agent that codes
right and so it's kind of
this different world where
again agent is like an
overloaded term right now
but it's this different
world where you don't just
throw code slop at a person
from from a prompt but you
actually like pair program
with the ai that's that's
the idea behind behind kilo
So I've been using Klein in
VS Code recently,
instead of GitHub Copilot,
which is what everyone
obviously uses and talks about.
So and Klein has a
plan mode and an act mode.
And plan mode is kind of like the,
we're going to talk about
the thing and come up with a solution.
And then the act mode is like, okay,
I've done this thing.
Do you want me to do the thing?
Because if you do,
then click over to act mode,
and then I'll do the thing.
And then you can say, yeah, okay.
So it sounds like you have a
similar sort of approach,
but maybe like another
layer in between kind of
Yep.
And the fun fact there is we
actually forked a project
called root code when we
started the company,
which the fun fact there is
root code is a fork of Klein.
So so there's there's a lot
of there's a lot of
relation between those
those three packages.
yeah um and we're actually
pretty big contributors
back to rue because of that
ruin ruin klein kind of
split six months ago or so
like in terms of like kind
of the direction they're
going as projects but
they're all open source
right and so I think that's
actually why we see now vs
code reacting and saying oh
we're going to make
co-pilot chat open source
right they just recently
did that um in fact the
code was just released like last week um
I think that's a reaction to, oh,
these open source tools are
really gaining a lot of traction because,
again,
developers want to be able to
inspect what they're doing.
They don't want to
necessarily just send a
chat into the ether and not
understand what happens back.
So yes,
it is not that different in kind of
the methodology,
but the ways that we're our
thought is that it is a
very fragmented space.
And what we want to do is be
the superset of those tools, right?
Because again,
a thing that that today again,
this will be out of date by
the time someone listens to it.
Yeah, exactly.
But the thing today that
like a root code or a
client doesn't have is any
like tab completion, right?
Which is what I think.
You know,
Cursor has a big share in the
market because of so that's
something we recently added.
And so we want to build a
superset of all the features to say, like,
not have to have seventeen
different extensions to do do stuff.
But conceptually, this idea of
you know,
an agent that has kind of a
different set of rules for
it and a different kind of
what are you about is where
those three kind of family
of extensions came from.
And the beauty of like Aru
or us is like you can
actually create your own
And so like if you look at
our documentation or ruse documentation,
there's a documentation writer mode,
right?
That is very focused on like, hey,
I'm going to give you a bunch of input,
but what your output is
going to be isn't code.
It's going to be well
formatted documentation with yes,
snippet examples, but not, you know,
just code.
So, yeah.
Interesting.
I mean,
I've talked about AI quite a bit on
this show,
and there's lots of stuff happening.
I mean, it's the new shit, right?
What brought you or
interested you in this space initially?
What about AI is...
is most exciting or
interesting yeah uh I would
say I wasn't interested at
all to begin with which is
I think great that's what I
like to hear um I never
wanted to learn what
different models meant I
never wanted to learn like
I I was not I was very much
not a maximalist I I was
like one of those people
that early on was like I'm
an elder millennial so I was like
It's not AI, it's machine learning,
you know, for certain things.
And like kind of really like
an LLM is just makes you average.
Like you don't want to you
want to be better than average.
Like, you know,
I've been kind of that kind
of like naysayer for some time.
And I think that's probably
a big chunk of software engineers, again,
because they tried it early.
They're early adopters, right?
Us software engineers are early adopters.
And it was really bad.
Like even twelve
you know, fifteen, eighteen months ago.
The thing I the thing I
point to there for folks,
if I'm trying to convince them,
but then I could talk about
what convinced me is like
if you look at the classic, you know,
video generation prompt,
which is Will Smith eating spaghetti like,
yes, like twelve, eighteen months ago,
it was horrible.
It was like like fear,
like like nightmare inducing.
Um,
and then if you look at the like V VO
three or whatever, like from Google, it's,
it's pretty realistic and kind of crazy.
And I think the models have
done just as big of a jump, uh,
in what they're able to do with code, uh,
and what they're able to do
acting again as an agent.
Um,
which to me just means working with the
human, not just.
know spitting out the slop
um that they're good at and
and so what kind of
convinced me again is what
I was talking about earlier
is like I kind of got
convinced that like okay
once I started seeing what
ai really looked like again
because again I had tried
it a while ago I was using
cursor and I was hitting
tab a lot which was helpful
but was it that much
different than autocomplete
right or you know language model yeah
But what kind of convinced me is again,
I think that once I saw the
speed that you can get with,
like when you understand
the context and give it context,
how much of a great pair
program that becomes, it's incredible.
Like just even on my portfolio site,
right?
It's like,
if I have context to something and I say,
hey, like see this, I want another model.
Like this, here's an example.
And my portfolio sites like is public too.
So people could look.
Like, I have a portfolio site.
It's got, like, talks.
This is very DevRel-y.
It's got talks.
It's got a blog.
And then my wife and I
recently went on a trip to Europe,
and she's like,
I really wish we just have, like, one.
We were taking a journal, like,
actually an Apple's journaling app,
which you can't share easily,
it turns out, which makes sense.
But she's like, oh,
I just wish we could have
that just on a site that I
could just send people.
And I was like, oh, I could do that.
And so I said to it, you know,
Tequila or I mean, again,
to many of these agents,
it would work the same.
I was like, hey, like, see this blog,
see these talks like I want that,
but model it for like a trip diary.
And like when it has that context,
it's it's like I could have
coded one hundred percent of what it did,
but it took it literally thirty seconds.
And it would have taken me
an hour or two and it would
have probably been the same.
And so once I kind of learned how, again,
I think I was a big
naysayer about was this
concept of prompt engineering,
but it kind of is a thing.
It's like this context
engineering is what I'm
hearing a lot of people talk about now.
It's like when you can give it context,
it is really fast at the
things that I already know how to do.
And it's really good at
figuring it out too and
working with me right like it.
Now if I want to download
like an open source tool
that I'm interested in,
the first thing I do is I
go into ask mode which
doesn't write any code.
It's like I'm not allowed to
and I say like hey,
how does this project work right?
And it'll show me it'll
it'll write diagrams.
It'll write you know whole like flow.
It'll really dig in and
explain because these these
models now have enough context to take in,
you know, a massive amount of code.
It can really explain it a
lot better and works as a
pair programmer better than
I ever expected it would.
Yeah, I recently,
I noticed in one of the
most recent chat GPT updates,
they've added integrations
with like deeper
integrations with like Google Drive.
and also integrations with
GitHub so that you can
authenticate with your
Google user or your GitHub user,
and then you can feed it an
actual project repository
or a document or whatever,
and then start asking it
questions about the context within that.
I half-formed a project with
AI to build... I had this idea for...
my perfect AI-generated playlist app.
I go back to back in the
olden days when Pandora was
actually good.
The reason why Pandora was
good is because they hired
human beings with music
history and music knowledge
backgrounds to create
what they called phenomes
that they applied to all
the different songs and
stuff that were in their database,
so that when you said,
I want a playlist or a
station based on this song,
it was not just doing
genre-based connections like Spotify does,
it was doing like, no,
these are the component
parts of that song,
and here are other things
that have similar component parts.
And there's never been
anything that's even remotely,
and I'm like a music snob, right?
I used to be a DJ.
So like I was like,
I'm all about like creating
like the perfect playlist.
And that was the only thing
that really created things
that felt like a thing that
I could have done or like
made sense in the same sort of way.
I dislike how Spotify tries
to do that and fails.
Like it gets lumped into a
very specific like rut and
then that's all it ever really gives you.
So I've started going to like,
chat GPT and like asking it questions like,
Hey,
I'm kind of feeling like this and
have a conversation with it.
And then at the end, it's like, okay,
here's, here's some stuff.
And then I have to take
those songs and put it into,
into Spotify.
And it's a manual process.
So my ideal, my idea, uh,
my ideal reality is I can just do that.
And then it does the thing
for me and it creates the
playlist just so it
authenticates with Spotify
and it creates a playlist for me.
So I had this half-formed
thing with bits of code
that don't connect to anything.
There's some API stuff in there,
but it's not fully... But I
noticed that it had this feature.
I'm like, okay,
tell me what's missing and
what's wrong and the things
that don't connect.
Because I know that this
isn't going to work,
but I also... I haven't really...
I'm mostly just copying and
pasting stuff at this point.
I'm not even really thinking
about the code yet.
So just tell me the things
that are there and not there.
And it gave me a really,
really detailed report on, it's got this,
and this is good, and this stuff is bad,
and this stuff is missing,
and this stuff you would
need to do before.
It was pretty comprehensive.
I could probably then use it
to create a list of GitHub issues,
things like task lists,
things that need to be done.
So yeah,
giving it context of larger and
larger swaths of
information is really valuable.
You were talking earlier about
sort of how, you know,
in the past people were using,
would you just use Stack Overflow,
how it's impacting the way that people,
the way that developers get their,
consume their information
and also even having impacts on SEO.
I've been seeing things
about the decline of sites,
the decline of clicks on
the links in search engines,
because as search engines
integrate with AI,
then you don't need to
click on the things if AI
is just going to give you
the summary up at the top.
So it's having SEO impacts there,
but also the decline in use
in things like Stack Overflow.
And I wonder about, at a certain point,
Because we're not at a point
like at some point, maybe theoretically,
I don't know.
I don't I'm not sure.
But at some point in theory,
at least if we listen to, you know,
all the all the gurus and
CEOs of these companies.
the belief is that we will
have truly generative AI,
where it can actually come
up with its own,
like answers to questions.
But right now,
it's just finding something
that that exists in its data set,
and matching it to
the patterns that it's being
given in the prompt.
Which means that, for coding,
it's looking at open source code,
it's looking at the data
from Stack Overflow or
whatever that existed at
the time that it was last indexed,
and using that to inform its answers.
But if developers aren't
using Stack Overflow,
if developers aren't writing this code,
and if all the code is
being written by AI now,
there's a certain...
recursive loop that happens, right?
Like what happens in the
in-between time when we
don't have a fully
generative AI out there,
but also no one's putting
new stuff out there either
because they're just using AI for it.
Yep.
Yeah.
It's a really good question.
And I don't know that I have
the answer to it either, because like,
again, there's the maximalists,
the CEOs of these companies
that are going to say, oh, well, you know,
we have, oh, we were, you know,
a few months from, you know,
actual artificial intelligence.
And it's like,
I mean, again, if the argument, yeah, yeah,
no, you're not.
And the argument against that is like,
you're still selling your LLM to people.
Like if you were really that close,
you would stop and you
would just build every company.
Right.
That's like, like that,
that's the best argument
I've ever heard against.
Like, like, like, yeah,
like obviously you're not
because you're not, you,
you would just write every other,
you would just be the only
company if you thought you
could really do that.
And I think you hit the nail
on the head with this
feedback loop that I'm very
worried about too,
which is why I think taking
a human out of the loop in my mind,
and again,
this could get dated and in
three months someone could
laugh at me for saying this,
but I just don't see the
human coming out of the
loop anytime soon.
I see the humans getting a lot more done.
um because it's like goes to
your point right like I
think I think spotify and
pandora is the perfect
example because yeah my
experience with spotify is
it gets into that feedback
loop right like if I wanted
to go if I want to shuffle
it and like have it just
kind of vibe with me it's
like I end up listening to
the same twenty songs
because it gets this
feedback loop of like yeah
brandon likes that song so
let me throw it back exactly right
versus the Pandora example,
which is the human curated
algorithm in some ways.
Pandora didn't have humans
type in our computer when
you were listening to a song and be like,
oh, Chris is listening to this,
let me give this one to him next.
But it did have humans
curating that input,
Yeah.
In such a way that it
actually was something that
like felt human to you.
Right.
Yeah.
And I just I think that
that's where my theory is.
That's where we're going to
end up for a pretty
significant period of time,
because like you said, if you give it
If you give today's LMS more
and more context,
it's really good at then.
Oh, I've got a lot of stuff to read.
Let me like distill it down.
Like I that is what I use it
for in my day to day work
is like if I have a whole
bunch of stuff that I want
to interpret and get some
help with and like kind of
know brainstorm with someone
on like it's going to be
really good at that but if
I don't give it that
context and I just opened
up claude or chat gpt and
say hey what should I do as
developer relations
engineer today like yeah
it's it's going to be very
generic ai feeling answers
it's going to be the
spotify dj which sorry
spotify friends but it's not great um
But if I give it a little
bit of love and I give it a
little bit of context,
it then gets and it feels a lot better.
I'm like, yes, okay,
now this is enhancing what
I'm able to do as a human,
not replacing it.
Again,
that's kind of my whole thesis for
why I joined Kilo because I
do think that an engineer
that can use AI well is
going to be a lot more
productive and thus replace
engineers that are kind of like,
I'm never going to use AI anymore.
And I think on the other end
of that extreme, to your point,
it's like the people that
just think that they can
just go vibe it tomorrow.
It's like that eventually
returns to the mean, you know, with LLMs,
right?
The concept of a large
language model giving you
the next most statistically likely token,
it will necessarily revert to the mean.
And they're getting around
that in a lot of ways, right?
It's using reasoning inside
to try and go back and
forth a few times to make
sure it's right.
But there just has to be a
limit on that road that
we're walking down.
And sure,
there may be something different
that comes after it that is
in a post-LLM world.
Um,
but right now I think it just requires
that human context, right?
You're talking about task lists.
It's like,
it's really good at consuming a
task list and going point
by point right now.
Um,
and it can be good given context coming
up with those tasks,
but like that's still,
those are still very human in the loop,
uh, situations,
but like if you are human
in the loop and you start
to learn how to use it,
you can go a lot faster.
Yeah.
The other conversation that
I often bring up when I'm
talking about AI with other
developers is the idea that,
especially with a lot of
companies going hard on AI
and using that to justify
laying off junior
developers or not hiring
junior developers, is the idea that, yes,
At this point, probably,
LLMs can get us to a point
where maybe you don't
necessarily need junior developers.
But the analog that I bring
up is we don't have a lot
of COBOL programmers either.
There's a lot of COBOL code
out in the world,
particularly in banks and
government institutions and whatever.
And the reason why YIIK was
a thing was because nobody
wrote COBOL anymore because
they had all aged out and retired.
And if no one's hiring
junior developers anymore
and we have senior
developers or
mid-developers that are
reviewing the code that is written by AI,
then no one's going to be a
developer anymore because
they're just going to go up
and no one's coming in at
the entry level.
And then we're going to be like...
what like ai just writing
its own stuff and without
with like off the rails
again um and like again
with like theoretically
maybe ai gets to the point
where it can generate its
own code and like check
itself and make sure that
it's not blowing up bank
sites or you know other things but like
Yeah,
that's the other thing that I think about,
like how the rise of AI is currently now,
but even like later, like five years,
ten years,
how that impacts the roles of
engineers in the tech industry.
Again, happy to be wrong,
but I think it's just a
shift in skill set, right?
Like my current theory is
it's a shift in skill set, right?
like back in the day in automotive,
I don't,
I don't want to like oversimplify it,
but like,
if you think about an automotive
factory back in the day,
there were probably like
super highly skilled
welders at every station.
Yeah.
And like, yeah,
now there's a robot welder
that's really good at it.
And so there's probably less
super highly skilled welders.
There are lots of like,
like verticals where that not true.
Right.
Like,
I don't know if you know about like
underwater welding, but it's like very,
very,
well-paid you know skilled
labor but there's also
labor in factories around
how the robots work and and
and making them work more
efficiently and
understanding how to use
them and understanding how
to design cars that can
then be manufactured well
like I think we'll see
similar skill shifting um
in the mid to long term here because like
Yes,
I think it will take maybe less total
people to do things.
But every time that we've
seen that happen since the
Industrial Revolution,
it doesn't mean that
there's like no jobs anymore.
I don't think this will be it either.
Again,
I could my kids might be mad at me
saying this in ten years,
but when they don't have a job.
But I just think it's a shift in skills.
Right.
Like,
I think you become more of this
product focused engineer.
Right.
I think if we were to see if
If everything you just described,
which is again,
a lot of people are talking
about like it's possible and maybe it is,
but it's like,
why do we had product
product and product
managers as a as a like a
function this whole time?
Right.
Like, oh, why?
Why?
Why don't we just have
engineers like and there's
lots of companies that have
tried that and it hasn't gone well.
Why?
Because it's kind of
different specialized skills of like,
yeah,
understanding why and what we're
building versus understanding how
We're going to build it.
And so I think it'll just be
a shift in that kind of
skill set where I think it
will require a different
kind of methodology where
you still have to tell it what to build,
right?
And like, again,
those of us that have been
around software long enough
know that like saying this
is what we want the software to do.
And then like what that really means,
when it hits the real world
are like a very tough
translation and always has
been and always, I think always will be.
Because in your example of, oh,
it's a bank and they've got
zero software engineers or
zero people mining the AI
self-governing thing.
It's like,
I don't think that's going to be real.
I think if that were real today,
the banks wouldn't have
product people and they
wouldn't have business
people and they wouldn't
have... They would just
have five engineers and it would be fine.
Why is that not fine?
Well,
because it's a diversity of skill sets.
And so if AI becomes better at...
like you said maybe the
junior engineering kind of
things um or maybe kind of
more of the coding right um
and and I think it's just
an abstracted layer off
right like to your point we
don't have a lot of cobalt
engineers because there's
not a lot of cobalt we also
have like very few people
relatively that are doing
like machine coding, right?
Like assembly level coding, right?
Like why?
Why?
Because we have higher level
languages on top of them, right?
And so like in my mind, this just might be,
let's say we don't look at
JavaScript anymore.
okay maybe I could see that
world um there will still
be the english language or
whatever language were it's
just another level of
abstraction on top of it
right um and they'll need
people um to handle it and
so so to say that they're
not in my mind is kind of
like in the early days of
very very early days of
computing right this is like
talking about Admiral Grace
Hopper and the early
founders of our industry,
it was very much seen as
when computers were becoming a thing,
let's remember if you've
ever seen Hidden Figures,
computer used to mean a person.
And typically- A computer, a person.
Yeah,
a computer was a person who computes.
And it was seen as this kind
of lower level thing.
And it was
Oh, women are entering the workforce.
Oh,
we'll let women be the computers
because it's not as high skilled.
Right.
And like, obviously,
and that led to a lot of
the founders of our
software engineering industry being women,
which is fantastic, right?
Like Admiral Hopper or
Margaret Hamilton on the Apollo program.
But the reality is those folks said like,
hey,
we've got to treat this as an
engineering skill.
Right.
Like Margaret Hamilton.
I have a whole talk that I did on her.
And when she was working on
the Apollo program,
people thought of it as like, oh, yeah,
that's just the software.
We're rocket scientists.
That's the real engineering.
And her point was like,
this thing that I'm
building is the Apollo
guidance computer is going
to be all of the telemetry
for guiding us to the moon and back.
Right.
And guess what?
If you miss, you know,
if anybody's seen Apollo thirteen,
if you miss the Earth's the
angle of attack on the Earth's atmosphere,
you either skip out or you burn up.
So like it's kind of a life
safety issue to the software.
Yeah.
And so she's she coined the
term software engineer in a
lot of ways because she's like,
we're going to treat this
like an engineering discipline.
way off the rails of your question.
But like,
what I think that just means to
us is like,
it's just a different skill
set that evolves.
And I think it's naive to say, again,
could be naive of me to say this,
but I think it's naive to say, oh,
that'll just go away and it
won't be a thing anymore.
That's the same naivety of,
oh computers yeah that's
just yeah let the let the
you know lower skilled
folks program because that
won't be a thing anymore
it's like obviously that
was wrong the the auto
manufacturing analogy is a
really good one um uh I was
recently looking at my uh
like college programs with
my son and he's really
interested he was
interested in the idea of
robotics and so I was
looking at a robotics
actual program and what it involved.
And most of what the
robotics program involved
was working with and
learning the software that
programs the robots.
Not doing anything with the
robots or building the robotics yourself,
but doing the stuff to tell
the robot how to do the
stuff that it does.
And it's almost an exact
parallel of the idea of, yeah,
we have this tool,
and then we have the people
that operate the tool,
and the tool does the work.
But, like, we're just one step removed.
But you need to have the human, like,
doing the stuff that tells
the tool how to do its job.
Otherwise, the tool is going to, you know,
blow up the car or something.
Yeah, yeah, yeah, exactly.
Yeah,
so I think the worst case scenario
that I see is we get to
that same place where...
Yes, there's a robot, ChatGPT,
that's a lot better at
writing export function.
But the human still has to
tell it where we're going.
Moving all the way back, what sort of
drew you into the world of
developer relations?
It seems like I was looking at your site,
and you have been doing
this for quite a bit.
What pulled you into developer relations?
Yeah.
Well, I think like you,
I kind of been doing it for a long time,
right?
So I,
I spent my early part of my career at
like not venture backed startups,
but at small companies that
were kind of like bootstrap funded, uh,
if you will.
So in medical software,
I spent ten years in medical software.
Um, and then I,
I live in on the east coast
of the US in Maryland and
everybody here is a
government contractor.
So I spent a little bit of
time doing that.
Um,
Uh,
but all of the time that I was doing that,
I was managing engineering teams.
I was managing product.
And kind of a lot of that is, you know,
developer relation, right?
Like if you're an engineering manager,
you do develop, like spoiler alert,
you do some developer
relations because you're
relating with developers.
And then I got an
opportunity to work at a startup, GitLab.
And my first job there, again,
was in engineering
management because I had,
at this government contractor,
been responsible for
GitLab's deployment at our
government customer and at
our own environment.
And so I was like, oh,
I understand GitLab.
I can build an engineering
team to deploy GitLab.
And I did.
And then I got to spend a
little bit of time in product there.
We were growing a lot.
So I was like,
I was like employee like one
hundred and fifty when I
was hired in twenty seventeen, I think.
And I was there for five
years and we grew to three
thousand people.
So.
Very fast, crazy growth.
This is like Zerp.
This is a crazy time.
And so we were growing all
the time and we were
growing the product team
and I got a chance to step
in and own the CI product
for GitLab for about a year,
which was crazy to me
because it was the thing
that made me fall in love with GitLab,
the product to begin with.
And now they're handing me keys to it.
And all this time there's this woman,
her name is Priyanka Sharma.
She actually is now the
executive director of the
CNCF where like Kubernetes sits.
By the time she was at
GitLab and she kept asking me like, hey,
come do developer relations.
I kept saying, no, no,
I don't want to like travel
that much and I don't want
to do all this.
And the story I tell here is
I was at an AWS reInvent,
which is a massive
conference for anyone that's never been.
And I was working the GitLab booth.
And in those early days of GitLab, this is,
let's say you are coming up.
You're working the booth.
You're going to work the GitLab booth.
This is the early days
working in the GitLab booth.
And I'm going to walk in.
I'm looking at you.
I'm looking at your booth.
Are you GitHub?
Right?
Like this is what happened early days,
right?
I was like, no, we're not GitHub.
So this person came up and
they were confused, similar confusion,
but even more, I realized, oh,
they don't understand like
what Git is and like source
code management maybe even,
or like definitely not what Git is.
And I was like, oh, okay.
Like forget GitLab, like whatever.
Like, let me show you.
And so I grabbed this piece
of paper and I started
drawing like local repository,
like your laptop and like
remote repository, like GitHub or GitLab,
you know?
GitLab, really, right?
And I'm like, explain all this.
And I get done, and they're like, okay,
thanks.
Now they understand a lot more.
And I turned to a friend of mine,
and I was like, man,
that's the thing I would do for free.
And that friend, they say to me,
they're like,
why won't you go work for Priyanka then?
And I was like, you know what?
You're not wrong.
And so that is what then I
spent most of my career at
GitLab in developer relations,
and I've been doing it ever since.
Yeah,
it's just this concept of being able
to like teach and like get
people excited and be
excited about the products.
Like I've always worked with
products that I've been
excited about for one
reason or another ever
since I worked at GitLab for sure,
if not even before that.
And so, yeah, that's what got me into it.
And now I've been doing it ever since.
And so, yeah, that was, I don't know,
that was like probably five
years ago now at this point.
So it's been a long time.
Yeah.
I feel like that's very similar.
I don't know that I had the exact,
I would do this for free,
but I was already doing it
in engineering roles even
before I spent a couple of
years doing online
developer training classes,
making them for Pluralsight.
which was really big for a
minute and then kind of got
eaten by LinkedIn Learning.
But yeah,
I was always already sort of
speaking at conferences and
meetups and stuff,
and this is the stuff that
I really like doing.
Do you, what is sort of your, what's,
I'll ask this,
what is your default presentation,
your default conference talk?
my default conference.
So I always, I started to give it,
it was my Margaret Hamilton one.
Uh, yeah, yeah, no, I, so I had a,
I did a whole thing on, uh,
I did a lot of deep talk
research and Margaret Hamilton,
she's an incredible, like again,
mother of our,
our industry coined the
term software engineer.
um, you know, really,
really pushed or like, uh, there's,
there's all, and then I have like, I like,
so I have a lot of Margaret
Hamilton stories that I can
work into anything, right?
Like if you,
if people ask you the question, like,
what are the things you could talk with,
talk about without, you know, prepping,
like,
for a long time.
And I don't know if you know
the Abraham Lincoln quote,
but like he people, he said, well,
if you want me to talk for
like forty five minutes or an hour,
I'm ready now.
If you want me to talk for
five or ten minutes,
I'm going to need a couple of weeks.
Right.
And so, yeah,
I can talk about her and like
a lot of great stories
that she had, she had her, she,
not surprisingly for the
time of the sixties was she was a mom.
And so she had to end up
bringing her daughter there
a lot to work because the
men would not have to think.
And they would just work
late into the evening.
And like, it was just expected of them.
but she would have to have
her daughter there with her.
And her daughter was like six at the time,
I want to say, and was in the like lunar,
like lander, like simulator and was,
you know,
messing with stuff and
programmed the Apollo,
the Apollo guidance
computer that she worked on
is like the one that you can think of,
like, right.
Like the green screen and
the big buttons that were made for,
And the daughter just like
is haphazardly kind of
playing with it and puts in
like the they're like mid
flight in the simulator,
like halfway to the moon.
And she puts in like the
launch like program zero zero zero,
right?
Like like restart.
And it was like a massive
problem because like
there's all this very little memory,
as you can imagine.
And and like it cleared out
all of the telemetry data.
on where they were in space.
And Margaret went up and said, oh,
we've really got to put
some protections in here.
And everyone was like, no, no, no.
These are professional astronauts.
They don't need.
And guess what happened?
Jim Lovell's first trip to the moon,
not Apollo-XIII, but Apollo-VIII.
Guess what happened?
Someone literally did that.
It took three hours of
relaying telemetry data
back up from NASA to reprogram.
And Margaret was like, see?
Told you.
Yeah.
Her daughter just invented QA.
Exactly.
Yeah, QA.
So in this talk,
I talk about how she
basically invented QA.
She basically invented
branching because...
she would show up I mean it
wasn't like really
branching like we think
about it today but like she
showed up to her office one
morning and had broken it
and there was like a line
of people that couldn't
work because like she had
broken the bill basically
the night before again I'm
using terms that are like
modern that like don't
really apply to how they
did it so like if anyone
understands how the
apologizing computer or
software works yes I'm
oversimplifying it but like
she had broken the bill the
night before and had a line
at her at her office the next day
And she was like, oh,
we should probably have a
test environment where we
don't just ship all the
code right to production.
Again, oversimplifying how it worked,
but basically invented that
concept as well.
um the thing that she spent
her whole career on after
was really like systems
programming and like this
kind of like high level
concept of error catching
um because again she she
really pushed them hard for
like hey like things could
happen in out of order the
way the cpu doesn't expect
it like things are going to
happen um and actually the
if you know from the apollo
eleven landing there was
like a program alarm
happening at twelve oh one
like if you like that right
up until the moment they landed
That was actually an alarm
that she put in that they
happened to have the radar
configured incorrectly.
Again, I don't know all the details,
but the radar configured incorrectly,
and it was bombarding the
CPU with interrupts.
But the CPU knew,
because of this hierarchy
that she put in,
knew this is less important
than the fact that we're landing,
basically, again, oversimplifying,
and was throwing this alarm like, hey,
you're giving me too much data,
but I'm going to ignore it,
which was great because it
actually allowed them to land.
And so this program alarm
that was going off was
actually an early concept
of being able to have
prioritization to the CPU
cycles and stuff that she
basically invented.
Yeah, it was an early page.
It was just like, and they knew,
they like got it.
They're like, oh yeah, ignore that page.
It's fine.
Just keep playing.
So yeah.
So I could talk about her for a lot of,
a long time.
Awesome.
Yeah.
Yeah, that's fun.
Well,
do you have any sort of thoughts that
you would like to leave
folks with or any links or
projects to share that
you're excited about?
Yeah, I mean, I'd love folks to like,
if you go to my website,
it's got a lot of things
that I've been thinking
about and writing about.
It's boleary.dev.
But yeah, I mean,
I think generally the thing
that I'm trying to get
folks to do is understand that, you know,
if you have tried, you know,
AI in the past,
you're a software engineer, you tried AI,
you know, six, twelve,
eighteen months ago and kind of, again,
are just tabbing in cursor
and not really leveraging it.
I'd encourage you to try it again.
Again, it doesn't have to be Kilo.
There's a lot of options out there.
I just, I think that, again,
I don't think we're going
to get replaced by AI,
but I do think people that get good at
programming and pair
programming with ai are
going to replace uh those
of us that have been around
you know since the early
days um and so I just
really encourage people to
try it there's lots of um
you know free tiers out
there it's not you know
vibe I'm trying to make
vibe engineering a term uh
to combat you know vibe
coding you know like yes
vibe coding is is really it
actually it has a lot of
purposes I think it's great
at prototyping
It's great when you just
need something that you
know you're going to throw away.
But I think vibe engineering
is going to be here to stay.
And so I would really
encourage people to think
about how can I vibe with
AI to do real engineering.
And that's kilocode.ai?
That's correct.
Yeah.
K-I-L-O.
As in like kilos of code.
idea yeah so it actually
like is yeah yeah yeah I
was trying my our ceo
doesn't love it he he uh I
was trying to make don't
code by the line code by
the kilo happen and he
doesn't love it anymore so
but that's that's like the
concept right um and he's a
really energetic guy the
last thing he did was um I
don't know if you saw that
vesuvius challenge it was
where there was the scroll
from mount vesuvius that was rolled up
and they couldn't unroll it
because it would just
disintegrate so they
x-rayed it um like kind of
cross sections of it and
then it was like can you
from a rolled up scroll and
x-rays and densities like
read it while it's still
rolled up that was that was
the thing he did before
kilo was he ran that
challenge it was like you
know one of those like oh
it's a million dollar I
can't remember what the
prize was but like oh
there's a million dollar
challenge if you can do it
um and he ran that so he's
he's a fun guy he's got a lot of energy
Is there a free tier?
Is there a paid tier?
How does that work?
Because I'm going to go
install it right after this.
Definitely install it.
It's open source.
The extension is open source
and free and going to be
free forever because it's open source.
We have with it our Kilo Code API provider,
where again,
we're just connecting you to the models,
to Claude, to Gemini.
And so there is a free tier
with that where we give you
twenty dollars to get started.
And then we don't take any
commission on any top up.
So this is a difference that, again,
once you start to dive into this world,
you start to find there's
like there's a lot of great
providers out there like
Requestly and Open Router
and these other things that
can kind of like
give you all of the models, right?
Like T three chat is a very common one.
Like we're not T three chat,
but like we're giving you
access to all the models
and we don't take any commission on it.
So that's where folks end up
staying is if they start to
get used to it and they
don't want to pay
commission to somebody else.
But that's through our provider.
When you install it,
you also see there's a
bunch of other providers.
You can use a local, right?
You can use like Olam locally.
Like I've got a max two year here.
Someday there'll be content
for me about like
completely off grid vibe engineering.
Or you can use, you know,
like if you have an
existing anthropic key or
you have Claude code already,
like you can just use those
and then just connect that to Kilo code.
Like our plan to make money
is with like enterprise and LDAP and SSO,
those kinds of things that
enterprises wants,
we want it to be free for
developers forever, so.
Awesome.
And that's the model I'm
used to from GitLab.
So I'm really excited about.
Yeah.
Well, thanks for coming on, Brendan.
And where can people find
you online if they have
questions about you or
anything you've done?
Yeah.
So I'm O'Leary Crew most places,
O-L-E-A-R-Y-C-R-E-W.
You can find me on basically
any social media there.
And then, yeah, boleary.dev,
B-O-L-E-A-R-Y.dev.
No apostrophe in any of that.
Next time I'm on,
I can tell you all about
living life in the age of
computers with an
apostrophe in your name.
You can imagine.
Bobby Tables tells you a lot
about what that looks like.
Yeah.
Awesome.
Well, thanks again.
And yeah,
thank you to everyone on the
internet who's listening to
this or watching this.
And we'll see you next time.