Everyone's obsessed with the uh model
and I think they should be more
interested in the harness, what you can
do to get the most out of the harness,
giving it the right prompts, giving it
the right skills to work with and
improving the environment in which the
model runs. As I sort of said with
Fable, like the model is useful, but I
think the harness has an equal amount of
work and you have much more control of
the harness than you do the model.
People are focused on the wrong thing.
They're looking at the big shiny new
thing when in fact just focus on the
stuff that's been working for 30 40
years, you know, and it really does
work. Like people ask me all the time,
how do you optimize for token spend?
Have a code base that's easier to make
changes in,
>> right, Matt? So what's going to be the
main difference between people who use
AI to get insanely ahead and the
majority of people who only get a small
boost from it? So in his book philosophy
of software design, John Asterout talks
about the difference between tactical
and strategic programming. So I find
this distinction so useful when thinking
about AI because tactical programming is
all about the on the ground day-to-day
stuff, the actual writing of the code,
the actual messing about with the
syntax, figuring out bugs as they come
up, and actually creating the code,
creating the commits. Strategic
programming is winning the war, not the
battle. It's longer term thinking. It's
the general sitting right at the top.
How does the codebase need to look what
um strategies can I use to improve our
velocity? And for me, strategic
programming has always been the most
interesting, the most exciting. That's
how I was thinking even when I was a
junior. How can we increase our
velocity? How can we do more with less?
And AI has basically eaten tactical
programming. It's gone, right? It's all
gone. So AI is just better at doing
tactical programming than you are
because it can do it for cheaper, right?
And so you need to be great at strategic
programming in order to get the most out
of this infinite fleet of tactical
programmers that you now have access to.
>> So does that mean knowing how to
orchestrate these agents plus some like
fundamentals of software design,
codebased architecture, like how would
you break that down into like these
specific skills that people can learn?
>> Yeah, great question. So strategic
programming really hasn't changed in AI,
right? AI is just um all we're doing is
instead of delegating to junior or
mid-level programmers, we're delegating
to AI instead. So the things that you
need to do good delegation are still the
same. You need to design the hard parts
up front. You need to make sure those
tasks are really, really well scoped.
You need to be thinking about the
interfaces between all of the modules in
your codebase. You know, you need to be
thinking about test scenes and good
tests. You need to essentially design a
codebase that's easy to work in and have
just enough documentation that can point
AI to the right places where it's going
to make those changes and make them
effectively.
>> I think everybody at this point agrees
that the AI progress is very fast if not
speeding up. So, I think a lot of people
also miss the part of upskilling
themselves, right? Because the like yeah
you can pay for subscriptions you know
you can get the latest tools but
ultimately anybody can do that but there
is still going to be people who use
these tools to massively grow their
business you know to ship more and
better software than ever before and
there's going to be people who like try
it a bit and you know maybe use the free
version or the cheaper model. So how
would you advise people to start
teaching themselves to be better?
>> Yeah. Um people ask me all the time like
because you know I sell developer
courses right? So I'm sort of, you know,
you can take my advice here with a pinch
of salt, but I personally feel that my
skills are a multiplier for AI, right? I
if I'm able to oversee a codebase and
think about how like things should be
built and just tell AI how to do it,
then a AI just has so much richer
context to work with. And I think of
this, I mean, I see this everywhere and
people uh like CTO's and like people I
talk to at conferences tell me this all
the time is that AI makes senior
developers just 10 times better. And it
sort of doesn't make sense to hire that
many juniors anymore because juniors get
a little boost from AI, but seniors just
get this ridiculous huge boost from it
and they can do so much more with it. So
your skills are the ceiling on what AI
can do. And if your skills are low, then
AI is not going to be able to go past
that, you know. So getting good with AI
is really about getting good at your
domain, getting good at what um AI is
going to be doing for you. So a better
teacher can use AI to teach people
better than uh a random can, you know.
So I think skills are more important now
than they used to be because again, you
just have this multiplier available to
you and you can delegate more. So you
recently shipped the new teach skill,
right? Can you tell us more about that?
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>> Yeah, I know a lot about teaching. I've
been a teacher for 10 years, actually.
So I was teaching uh singing and voice
when I was uh just straight out of
university. Then I became a developer
and now I teach developers. I've been
doing that for the last four years. So I
know a lot about teaching and I I
thought okay what if I take some of the
teaching principles that I know about
such as the zone of proximal development
such as the difference between knowledge
skills and wisdom encode that into a
skill and essentially use it to create a
course on the fly about any topic and
that's what I've done and it's extremely
effective. I've actually been learning
I'm teaching myself Rubik's cube from
this. I can solve a Rubik's cube now
from memory thanks to this skill. And
I've been using this for all sorts of
stuff. So yesterday I was I was messing
about with what it might look like to
ask the teach skill how to become a
senior developer. And it basically went
on this big journey looking at a bunch
of trusted resources getting a big sort
of curriculum together and just produced
something that was gorgeous. And so that
>> absolutely let's give it a go.
>> Yeah. Yeah, I think people would love to
see that.
>> Definitely. Okay, so uh David, what do
you want to learn?
>> Let's do like systems design.
>> I tell you what, I mean I I I I've got
an idea here, which is that a lot of
people come to me. I I teach courses for
engineers, really, people who already
know how to be an engineer.
>> I'm intrigued by if this skill can teach
you basically the basics of engineering,
you know what I mean? To fill in the
gaps that you might have if you're a
vibe coder, you know? So, I'm going to
pretend that I'm a vibe coder. I'm going
to invoke the teach skill. I'm inside an
empty directory here and I'm just going
to let it roll. So, I'm going to dictate
something out and let's see how it goes.
I am a vibe coder and I want to fill in
my knowledge gap so that I can ship
better software. I know some very, very
basic CLI commands and I know just about
enough to read some code and use the
terminal, but that's about it. What do
you think I should learn to um develop
my skills next?
>> So, I'm going to put that
>> a simple prompt, plain English. Anybody
can ask you this.
>> Exactly. And it's just a a very very
simple request. And I'm not really
talking about the subject that I'm
trying to learn. I'm talking about my
mission, the thing I want to get out of
this, the reason I'm coming to this
session with the teacher today. And you
can think of this really as a
collaborative effort. Basically, I am
talking to a teacher and the agent is my
teacher. and it should know how best to
teach me.
>> And it's good that you you brought it up
and you created the skill recently
because literally I was thinking about
this yesterday, right? Especially when
Fable came out. I was like, how can I
upskill myself to get the most out of it
cuz like I I I know I'm not where I can
be. You know, there's people much better
than me, much more skilled than me. So
like it's it's great timing that you
have this skill now.
>> Good. Well, this um so it's basically
saying, okay, I've checked the
workspace. It's a blank slate. the teach
skill, you need to run it in a workspace
because it saves a bunch of information
in that workspace. Okay?
>> And so for a vibe coder who can read
code and use a basic terminal, the
highest leverage gap is almost never
more syntax. It's the stuff around the
code that lets you ship without fear.
That's 100% true. So git, reading
errors, debugging, how software actually
ships, testing, totally git is where we
start. Okay,
>> but first the mission. I don't want to
guess. Three quick questions. The first
thing the skill does is it basically
aligns with what you want to do. I think
of teaching and learning as not getting
information into your head but orienting
you in the world, putting you in a new
place in the world. And this is kind of
a bit abstract and a bit you know
spiritual almost but it's not really I
mean essentially you need to learn these
skills in order to do something into the
in the world and that's your mission
right so it's like what are you building
what does ships better software mean to
you right now what's a concrete project
you're working on
>> so let's imagine I answer this let's
imagine I was a I'm a voice coach
wanting to learn how to be a better
coder you know that's actually who I was
back then so I am a voice teacher and a
singing teacher I want to build a
scheduling app to help me schedule my
students to help me retain notes on my
students to teach them better and to,
you know, build something that they can
help practice with. That's the kind of
app that I'm looking at. So, probably a
full stack application with a database
with some kind of authentication, but
that's way beyond my abilities right
now. By the way, I'm using Whisper Flow
for dictation. It's really good.
>> Yeah. So, this is going to be also the
game like how fast you can output your
tokens from your brain and input them
back into your brain.
>> Totally. Yeah. I mean like dictation is
a if we do a little sidebar on
dictation, anyone who's not doing
dictation is just so much faster, right?
It's very fast for me because I'm like
quite a fluid speaker. So I can
translate my brain into words quite
effectively.
>> But it's a skill, you know, it's a skill
at the end of the day and people can can
uh learn to verbalize false better and
faster.
>> Exactly. And it's a skill that is
actually overpowered if you're if you're
a developer. It really really is. like
uh I found that being able to
communicate and being able to speak was
something that was just ridiculously
overpowered in the development world and
so it has proved um so it has created a
mission.md here. So, it's basically
saying, okay, who is this person? What
do they want to build? Why it matters?
What success looks like? Being able to
ship that app, not break it, get it
live, and trust that it works for real
students. This is now going to orient
everything about this skill and what it
does next. So, you can see it's doing
some um searches here. So, it's
searching for some trusted resources.
How does a full stack web app uh front
end back end blah blah blah blah blah.
Let me set up your resources, a learning
record, a reference cheat sheet, and
your first lesson. So it's going to
start churning out some material that's
running locally. And this is going to
the idea of this is I think of there are
skills as stateless skills that don't
need any state on the local system or
any kind of like um memory about what
was done before. And then there are
stateful skills. So skills that rely on
information running locally. And this
teach skill is a stateful skill because
if you think about working with a great
teacher, a teacher remembers what you've
done before. A teacher knows about where
your sort of
>> need to go next. Knows what your mission
is, all that stuff. And so it's saving a
bunch of states locally so it can
remember everything. And it's first of
all created a reference. So we've got a
reference cheat sheet. It's now going to
create the first lesson. And these are
created as HTML. This means we can open
it in a browser and have like a really
rich thing to look at because learning
stuff in the terminal is just brutal.
>> Yeah. So you're using uh cl code with
fable, right?
>> I'm using claw code with opus 4.8 with
medium effort. So I'm not using fable.
Not quite yet. I haven't decided whether
I want to get into fable yet or not. Um
>> really
>> I well I mean yeah I I don't I don't
really believe in all the kind of like
yesterday was it yesterday when it was
released. It's just so much unbelievable
amount of noise. People saying they've
one-shotted this, oneshotting that. And
yes, it does seem to be a step change.
It does seem to be slightly better. But
then you've got to weigh that against
the cost of the tokens and how available
it is, the latency of it. Um I prefer to
essentially not try a new model when it
comes out and wait about a month just to
see how things shake out. That's what I
did with um Opus 4.5, which was the like
the last time I really had a massive um
new feeling about a model and it worked
fine. You know, you're not you're not
losing that much by just waiting a
little while to see how things shake
out.
>> All right, so this is the file it
created.
>> This is lesson one. Get your projects
undo button. So we can see that it's
using a more rich um like actually
seeing this in the HTML is a lot richer
and
>> nicer than doing in the terminal. This
is saved locally so you can always go
back and reference this and it's giving
you actual things you can do in the
terminal giving you proper exercises to
go and do it. So you make a folder, go
into it, start getting it, create a
file, check the status, stage it, save
the snapshot, and because of course it's
running like on my system, it knows what
my setup is. It's probably already
checked whether I've got git installed,
that kind of thing. So it's, you know,
it's perfectly
>> personalized education, you know.
>> Exactly. Totally personalized. So which
command saves a snapshot of your stage
changes? David, do you reckon you're
going to answer this one for me?
uh get command say snapshot your state
changes get commit
>> get commit bam so again it's using
techniques that are well known in
education for increasing um storage
strength right so quizzes are such an
awkward thing like I sort of hate
quizzes but quizzes are just
unreasonably effective for uh increasing
the strength that something is stored in
uh what command shows what has changed
right now David
>> good status
Get status. Yeah. Oh, bugger. I pressed
the wrong thing.
Uh, what does git add do to a change?
>> Stages changes.
>> Stage changes. A commit is best pictured
as a
>> safe point.
>> Safe point. You broke a file but haven't
committed. To restore it, you run
>> um get restore.
>> Yeah, I think it's git restore, isn't
it? Yes, there it is. Very good.
>> Okay.
And so it then sends you off to read a
primary source if you fancy it. So the
um progit book
>> um and then invites you to ask your
teacher follow-up questions and create
the next lesson. And so the idea of this
is you I think of knowledge as like a
graph, right? It's like a big forest
through which you're exploring. And what
this is doing is it's creating a linear
path through that graph. It's basically
going okay, you've learned this. Now
that's I I know that you've learned it.
It's in your learning record. We can see
it's retaining a list of learning
records in the top right here, which is
your mission and your starting point.
So, it's captured your mission, a
decision to start with Git zone of
approximal development, current
estimates, you get the idea. So, it's
great. I freaking love it. And that's
what I would recommend to anyone
starting with uh especially development
because it's sort of I mean, I'm a
developer. I know what developer
education is. And so I've sort of put
that into this teach skill. And I think
I've always thought coding was quite
easy to learn. Like I didn't have that
much trouble when I was learning it
myself. And
I I think this is a great way to do it.
>> So is this live on GitHub somewhere?
Where can people find this?
>> GitHub Matt Pokco skills. And if you
head there, you just run this uh CLI
command npx skills latest add map
skills. You can choose the teach skill
and it will just save to your local
setup. So whether you're using claw
code, whether you're using codeex, it
will work and you'll be able to then
just invoke teach inside a fresh
workspace.
>> So you have, you know, perhaps the most
at least one of the most famous and
popular skills repos. What separates a
good agent skill from a bad one?
>> It's such a deep question.
It's such a deep question because it
depends what you want. Um you can think
of there as being two types of skills.
There are skills that are procedures,
skills that you intend to uh run
yourself and then there are skills that
are more like abilities. Those are abil
like things that you intend the model to
invoke itself. And so a good ability for
instance might be uh your coding
standards let's say. So let's say your
agent is sort of doing its own thing
kind of working along and it needs to
check uh how you like your react code
written. So it's going to write some
React code you it pulls in the ability
uh great React coding standards let's
say and then it reads it and it
understands okay I shouldn't use use
effect I should use something different
a procedure is more like something uh
this is how I prefer my skills written
it's something that you invoke yourself
to get the model to behave a certain way
it's something I love is my grill me
skill that's one of my most popular
skills what it essentially does It turns
the model into an adversarial
interviewer. So this is under
productivity under grill me. It's
incredibly short and you can see it's
literally just uh four sentences I think
this skill maybe five sentences and it's
unreasonably effective because it just
turns the agent into an adversarial
interviewer asking you questions
interviewing you and popping up with
ideas that you might not have considered
until you reach a shared understanding.
I've been using this for coding first of
all just like as a replacement for plan
mode. So before you actually go and
implement some code, you go, okay,
here's my idea. Uh, interview me about
it. Let's reach a shared understanding.
Let's flush out any weirdness or any
unexpected stuff before we get in as
much as you can. And it's just
unreasonably effective. And this is a
procedure. This is not an ability.
>> I tend to prefer my skills as
procedures. I like to be the one in
control. I like to go, okay, we'll do
grill me and then we'll go, let's write
a product requirements document. So we
use two PRD for instance. Then let's
take that PRD and turn it into
individual issues so that we can work
through them. That's just personally how
I like to do it. But other skills such
as superpowers from Opera, which is
probably the most popular skills repo
out there, it takes the opposite
approach and it prefers things to be
more like the model is in control. But
I've always preferred to me personally
be in control because I know my skills,
I know my abilities. I don't want to
delegate my thinking to the model.
>> Yeah. I mean that I think is one of the
See, I'm like playing with this idea of
the list. It's like a list of
abilities, you know, knowledge.
Basically something that like if you
could take the average, you know, 100x
developer that uses AI versus a, you
know, 1x developer, whatever. What would
be the list of the differences, right?
You can say like okay some of these are
like raw intelligence you know blah blah
blah but most of them are probably
teachable most of them are some skills
some knowledge something like that so
I'm obsessed with this idea and I think
one of them is kind of knowing when to
have the AI ask you right like kind of
this grill me style of skill because
personally I found out like the biggest
difference instead of like saying
oneshot this app I describe my vision
for this app and say like list out the
10 most consequential decisions right
the software design decisions
architectural decisions
product decisions that will shape this
project and ask interview me until you
understand 98% about it. Right? So, kind
of that is like one of the things I
would put on the list. What what are
some of the things you think are on the
list?
>> Well, can we um can I challenge the idea
that this is possible? Is it right if I
take this question in a different way?
Because
>> skills are really hard to write uh
especially because every single skill
that you write it leaks a description
this description here into the context
window. Right.
>> Yeah.
>> And you can disable this. So you can uh
there are some skills in here I think in
my engineering zoom out I think which
has uh disable model invocation true. So
this one uh this skill can only be
invoked by the user and this means its
description is not leaked into context.
Every single ability uh let's say we
have the list. Let's say we have a 100
different skills. You're going to be
leaking 100 descriptions into the
context window. Right?
>> Okay. Maybe let me rephrase. I didn't
mean it for the AI. I meant the list is
the person, right? Like if you had to
say like I know it's difficult to like
it's maybe a reductionist to take
someone who's like really insanely
productive you know maybe like so some
of the top people at opening are onic
who like worth hundreds of typical
developers right what would be the list
of their abilities skills knowledge that
compare them to an average developer
>> yeah got you well this I mean the you're
kind of heading in my direction I think
which is I prefer to hide most of these
descriptions from the AI itself and keep
all of that knowledge inside the human,
right, inside the developer. And so I
prefer that's how I prefer my skills to
be used is you essentially are the
driver. You know, you take the steering
wheel. And so I do think that this is
such an exciting time to be a senior dev
and to like be able to share and like
procedurize proceduralize maybe um your
work into reusable chunks, right? Like
in in a codebase you have a function
that's repeated three times. you take
that function and you pull it out into a
shared function that is then you know um
you reduce the duplication basically and
we're able to do that now with our own
procedures with how we build software
we're able to take these like okay I've
uh you know made this plan a h 100 times
I know how to make good plans I can turn
that into a skill distribute that to my
team and everyone can be planning in the
same way contributing back to that same
skill making everyone on the team better
so you're raising the floor really on
what engineers can do. It's such an
exciting time. And what I would say
though is that skills like
there's like know I'm going to sort of
confuse our terminology a bit. I think
of there as being three things that you
need to be good at anything which is you
need knowledge. You need the fundamental
sort of uh what is that thing like
understanding it in your head. You need
the skills. You need to be able to have
done it a bunch of times to like um you
know in muscle memory. And then you need
wisdom. You need to know when to do it.
You need to know how it fits in in the
real world. And wisdom is almost
impossible to obtain without actually
having done the thing in the exact
context where you need to do it. So if
you want to be like someone at
anthropic, sure you can gain the
knowledge, you can gain the skills, but
then how are you going to gain the
wisdom, right? Like you need to probably
go to anthropic to gain the wisdom to
actually understand how to do the thing,
you know? But I think it's like being
able to bundle the first two knowledge
and skills into something that's
reusable is such a fascinating
um outcome of this weird age we're
living in.
>> So currently we talked about skills.
What's your agentic engineering setup?
Like what tools do you use? What models?
How many agents?
>> Yeah. Um so my setup is um I use claude
code essentially for planning and for um
some implementation locally. So I'm
using Opus 4.8 with um medium effort is
kind of what I've landed on and it works
fine. I do most of my uh development and
a lot of my work now um AFK so with me
away from the keyboard and the way I do
that is with something I built which is
uh a tool called sand castle and sand
castle is essentially a way to run
agents inside sandboxes.
>> Okay,
>> so you can um inside like if you don't
run an agent in a sandbox then it's
going to do weird stuff. So it might you
know randomly delete your home directory
or um you know exfiltrate your um
environment variables out to um bad
sites etc. With Sand Castle, you're
essentially able to plug in things like
Docker or Podman and run agents, run
either uh this is what it looks like,
run clawed code uh inside some sandbox,
which is extremely cool, extremely
effective and it means that you can
parallelize a bunch of agents at once
either on your own machine or you can
use like Versel sandboxes for instance
to just ping up a remote agent and then
pull the commits back into your local
workspace.
I've been doing that and I've been
combining it actually with GitHub
actions. So we can see inside for
instance here inside the actions tab of
map sand castle this one this was an
agent review action which happened a
little while ago which checks out the
branch. This runs on on a PR. It uh runs
the review agent which is just a prompt
I have locally. We can see all of the um
things the agent did. It's checking
various things blah blah blah blah blah
uh type check round clean and then it
replies saying cool it all looks good to
me. So that's mostly how I've been doing
things is running uh agents using sand
castle on GitHub actions and essentially
just telling them to do things and that
has been extremely unreasonably
effective because you just get to
parallelize as much as you want. you're
not worried about constraining the
resources on your local machine and yeah
it's just very very quick to just spin
up an agent and get it to do something.
>> So in terms of models are these 5.5
extra high are these another cloth codes
what do you prefer?
>> These are I think uh again just claw
code uh opus 4.8 eight medium. I think I
don't think I've varied it too much to
be honest. I mostly don't worry about
models that much. I mostly just use
>> um like I think
yeah, this is my sort of hot take I
suppose which is that
>> everyone is obsessed with the model.
Everyone's obsessed with the engine of
the Formula 1 car whereas in fact the
engine is really only a part of the
whole system. Right? You've got the
entire chassis. You've got uh how it um
how it moves through the air. Everyone's
obsessed with the uh model and I think
they should be more interested in the
harness, what you can do to get the most
out of the harness. Uh giving it the
right prompts, giving it the right
skills to work with and improving the
environment in which the model runs,
improving the codebase and all that
stuff. So yeah, as I sort of said with
Fable, like I the model is useful, but I
think the harness has an equal amount of
work and you have much more control of
the harness than you do the model.
>> That's true. I would maybe challenge you
a bit on this because I don't see why
you cannot do both because like
obviously I I agree that you need the
right skills, you need the right setup,
all of that matters, but then if you
swap in a better engine, all of that is
instantly better. No.
>> Yeah, it totally is. Um but I think they
you need to think of them as 50/50 right
so instead of um the model being like
90% and sort of the 10% optimization of
har like everyone's so focused on the
model people are not so like intrigued
by
so okay let's go back one step there's a
famous idea in ML which is the bitter
lesson you heard of the bitter lesson
>> yes
>> yes the bitter lesson is the idea that
Um, whatever you do in machine learning
research, compute, raw compute will just
beat you every time because compute is
increasing at such a high rate that you
can just essentially trust that the
underlying thing will get better and
that will uh beat any optimizations you
put on top of it. And there's a a sort
of idea here that maybe I'm falling into
the bitter lesson that instead of like
like optimizing my setup, optimizing my
harness, I should just wait for the
models to get better, wait for the
engine to get better, and then my car
will be faster.
I don't know. I still think there's a
lot to be gained by just optimizing the
harness and focusing on creating like
good code bases that the agent can do
well in instead of hamstringing the
agent before it even gets started. I
would say probably I I I agree that you
shouldn't wait. Like that's that's that
was a very stupid idea. People just
waiting around for AGI or not doing
anything. Obviously, I completely agree
with you there. I would say I'm
somewhere in the middle. I would say
like I'm actively trying to improve my
setup every single day trying to, you
know, get faster at using these agents.
Figure out, okay, should I be using Cmax
here? Should I be using should I put
this on VPS? Should I be using Tailscale
here? trying to like actively improve
everything else except for the model but
also trying to use the best model
possible because fundamentally like you
said you might be falling into that. I
would say maybe if it's 50/50 now for
the simplicity of this argument, what if
like the model really becomes a lot
better, right? Like let's let's assume
the next generation, right? Like Opus 6,
Fable 6, GPD 6, whatever 7 like don't
you think these models will require less
steering and like less handholding as
they become more competent or or no?
>> I'm not a pundit, right? This is what I
say to every single one of these
questions. I'm trying to do the best
with what I have right now and I don't I
don't have the insight to know whether
these things will get better. I don't
really want to make predictions about
the future. I think that if I try to
keep my um workspace and my harness
agent agnostic as much as possible, if I
try to apply good software fundamentals
to what I'm doing, if I do stuff that's
always worked, then it will probably
continue to work in the future. You know
what I mean?
>> So, if I try to overoptimize around a
model, if I get too focused on the
model, I will lose focus on um the
fundamentals. That's that's that's my
point of view.
>> Yeah. So basically you're focused on
like okay what has been true for the
last 10 20 30 years you know the the the
really best principles of great software
and it's likely going to hold up with
the next model rather than people going
from the model first and like okay this
model maybe requires shorter prompts
this model you know sucks at that part
let me patch that part like building up
you know properly proper foundation
rather than like starting with the model
maybe
>> exactly people are focused on the wrong
thing they're looking at the big shiny
new when in fact just focus on the stuff
that's been working for 30 40 years, you
know, and it really does work, you know,
if you have a codebase that's easy to
change. Like people like people ask me
all the time like how do you optimize
for for token spend, right? How do you
optimize for token spend? Have a code
base that's easier to make changes in?
Because then you can employ a stupider
model. If your codebase architecture is
better, then you can get a cheaper model
to do the same work because it your
guard rails are better, it's easier to
explore, it needs to spend fewer tokens
banging its head against the wall. If
you're hamstringing your model from day
one, then you will need a smart model to
get the most out of it. Um, but yeah, so
I think thinking from the model first is
the wrong way to do it.
>> Yeah. So basically I would say like the
exact opposite of you is like the
quintessential vibe coder who like is
switching tools every single week right
like there's a new replet update goes to
replet agent switches to lovable
switches to this and that constantly
switching and never learning any any
programming principles anything about
software engineering nothing you're like
your approach is basically the
difference is in approach it's not like
you don't believe in AI obviously right
now you you're heavily trying to be at
the age of AI and educating people how
to use it it's more about the difference
of approach. She's like, "Listen guys,
learn the fundamentals. Learn how code
works, how good software looks like, and
this is going to be valuable no matter
what. No matter if OpenAI is ahead,
Enthropic is ahead, Gemini is ahead."
Versus the exact opposite approach,
which unfortunately I think most of the
people who are new to AI take is like
jumping on the latest trend and like
switching everything the moment, you
know, some new update or tool comes out.
>> Totally. And I think, you know, that's,
you know, you can do that and that's
exciting. Um, but you're not really
increasing your skills that way. And
it's your skills. I firmly believe that
are the ceiling to what AI can do. You
should be focused on yourself. You know,
upskilling yourself for this new world
instead of thinking um, right? How do I
delegate my thinking? How do I delegate
more? You know, you should be pulling
more into your own domain and delegating
only the tactical stuff. Keep the
strategic mindset. keep thinking about
you know the next uh months and weeks
ahead the road map of where you're going
in your code instead of just um trying
to delegate that to you know people are
obsessed by the idea that you know you
can just delegate everything to AI and
you can't you really can't and I don't
see I mean again I'm not a pundit you
know I'm just looking at what we have
right now and it doesn't
>> yeah yeah I I am the person in the real
world that's driving this stuff I need
to be the one making product decisions I
know where I'm going And I think me as a
developer, I should be in control and I
need the skills to be able to do that.
>> I agree. One note I'm going to share on
Fable is that happened yesterday which
is a bit scary and it definitely doesn't
follow security practices is that I was
setting up a new um like a new agent for
for like Twitter and basically u the
Twitter API was bugged. The developer
console was wasn't loading some buttons
and I tried it on a different browser.
It still didn't work. I disabled all
extensions. It still didn't work. So I
gave it like a few solid minutes to try
to debug it and I failed. I mean I
didn't it wasn't the main thing I needed
to get done so I didn't really try as
hard as I could but I gave it to Cursor
powered by Fable. It used the built-in
browser inside of cursor. You know I had
to log in obviously to the console but
apart from that it started clicking. It
created API keys copied them. Again I do
not recommend this for production apps.
This is just a simple thing for me. And
then it figured out when it did the
testing that those API keys were in a
different like app in the console and
they actually weren't using the credits
I charged up. So then it moved the app
again using the built-in browser inside
of cursor and like for me I really felt
like what am I doing here? Like
obviously I described what we're
building, why are we building it, some
of the you know kind of my version of
grill me at the start but then I felt
like okay I just logged in into the
console and I just charge up a few
dollars but like everything else the AI
was doing right so like I felt like my
value in this project was a lot lower
than with previous models.
So what's your thoughts on this?
I mean, if you think about the AI's
output, right, what it what it was doing
at the end there, um, it needed like how
does the AI know at the end that it's
done a good job, right? What it is is
the theory here that you can disappear
from the project completely. No, you're
still needed, right? Like all we're
doing here is we've just given the AI a
set of tools and we're it's, you know,
we've given it a scoped task and it's
performing that task, right? you know it
we've given it a goal and we said you
know do blah blah blah blah blah I don't
think of that as that particularly
magical you know that's something that
uh agents can do now you just give them
the tools and they go and do it but to
decide whether that's the right thing to
do to um uh security test that at the
end of that that's something that you're
needed for right you David are needed
for that to know whether it's done a
good job and so yeah we can delegate
more but I don't think that's a reason
to start thinking um you know or have AI
psychosis or anything. It's just yeah,
it's a reasonable thing that the AI can
do with computer use.
>> I've also seen a lot of people report
like they they were, you know, maybe
looking for optimizations or doing some
feature and then again I'm talking about
fable because it just came out. It's
topical, right? So, it's on top of my
mind. But a lot of people reported that
it found like deeper bugs that they
didn't notice at all. Whereas other
models completely miss those. And that I
I I would like again I would challenge
you slightly that that's a sign of like
AI being able to do more. I'm not saying
we need to be completely removed from
the loop but like if the AI is you know
redesigning the front end and it finds a
issue in one of the like backend API
endpoints like a major security issue I
would argue that that's like AI being
more involved.
It's not a 50/50 at that point.
>> Yeah. So you're saying that um
the better a the better the engine is
the more value you can bring to the
business just by having the engine and
those effects are emergent. You don't
know what you're going to get by
increasing the power of
>> it. It will still know the vision,
right? It will still know what you're
doing here. Like this is a educational
repository for my students in my paid
community or this is something just for
my team. It will be used by roughly five
people. The purpose of this is XY Z. It
will still know the core idea, the
initiative that comes from you. But in
terms of the actions and like what
happens, my argument would be that as
the models get more powerful, more and
more of these is going to be done by the
AI. But not only that, the AI will spot
what needs to be done, such as the
example with the, you know, deep bugs
that the user wasn't even debugging.
>> Totally. But I I think that we think
that the model is the only way to get
there, right? What you could be doing is
in your repository is you could run an a
cron job that runs every single day,
let's say, and does a security review
and every day it checks a new part of
the repo, right? And you could use a
relatively simple model for that and you
probably get some decent results. I mean
this idea that there are deep bugs that
um you know or deep sort of security
things inside your application that uh
the model could spot and others cannot
you know like sure that's like it sounds
attractive but you could probably also
uncover those bugs with cheaper models
if you just looked in the right places
you know and you gave it the right
prompt let's say for one of a better
word or the right harness so I don't
think there's something that's
necessarily special about the model that
does those things or you know and I
think that's again 50/50. If you had a
harness that sort of was looking
specifically for those things then you
would find them and I think we're
lagging behind in our practices and
expecting the model to just pick up the
slack.
>> You can absolutely just run Opus and get
it to do that stuff. You know, people
were talking about this like when Opus
4.5 came out, whoa, all these security
things that Opus, it's just like, sure,
it's found them and you can just get
that with a harness and just get it to
do it again and again. I like
>> Yeah, I I understand. I understand like
you're basically pushing against the
hype wave, you know, you're trying to
like implement some sense, some wisdom
into this. Say like guys, okay, the
models are getting better. Yes. But at
the same time, let's not lose the
obvious, you know, optimizations, the
obvious things that has always always
been true. Maybe like if you had a
better harness, you could support it
even in the previous generation model.
Or maybe you didn't have to spend $2,000
on API tokens, maybe only 200, you know,
stuff like that. So yeah, I I completely
agree with you there. You're trying to
be like
>> one thing one thing just to to finish
there, which is that
>> what is this what is this thing that
you've learned from Fable looking at
your code and spotting a security issue?
What you've actually learned? Sure,
you've learned that Fable is good
definitely, but you've also learned that
there are security issues in your code,
right? And you should probably have
something that runs and checks for more
security issues in the future. We need
to build loops into our um loops for
God's sake um into our I mean, we can
talk about that as well if I've got some
opinions there. Um, you need to build
these uh systems that just check your
like you need, what am I trying to say?
>> You need to figure out why it happened
like why it even got to this place. You
know, it's like if someone keeps
stealing your bike, maybe buy a lock.
>> Yes, exactly. Maybe we need to uh be
designing systems that are
self-improving over time, right? And
this is something that we've been doing
as software engineers for a long time.
We write test suites so that we can test
our own code. We do human reviews so
that we can make sure things are looking
the way they need to. We refactor so
that we can change code better in the
future. And sure, a model has uncovered
that we need to do a bit more of that.
So let's do a bit more of it. But we
don't need to use the fancy model in
order to get those insights. See, that's
what one of the things I would put on
the list is like the thing that really
separates the people who are going to go
super fast with the AI and build better
and more software versus people who are
not. Like most people in that situation,
they would just say, "Oh yeah, Fable is
great. Fix the bug. It fixes the bug."
But like the the people I don't know if
it's like 10x developer, it's almost
like 10x AI builder, you know, because
everybody's becoming more of a builder,
whether it's a designer background, a
developer background. It's like that
person would look at the underlying
issues. is like how did that even
happen? How did I have this bug for so
long that I didn't notice it and try to
patch the underlying issue? You know,
whether it's a new skill, a new system,
better staging process, whatever that I
think I would put as one of the things
on the list of your human capabilities
or things you should have to get the
most out of AI. Totally agree.
>> All right. So, you mentioned loops. This
was super viral on Twitter. Maybe it
still is, but like, you know, a week
ago. Um I think it started with Peter
Stanberger if I'm not mistaken. But
basically people are like obsessing over
agentic loops. Uh half of it I would say
is like the research labs selling more
tokens. You know basically you should be
running loops to pay us more endless
tokens. Stop prompting your agents.
Figure out what loops it can run forever
permanently. Half of it could be useful.
What's your thoughts?
>> So
what we're essentially talking about
here is the difference between human in
the loop work and AFK work. Right. human
in the loop work being the human you are
there with the agent um talking together
and like uh figuring out something. So
really useful for planning, really
useful for some kind of more complicated
implementations, uh really useful for
unscoped work, you know, stuff that you
just need to uh figure it out locally
with the agent. And then we're talking
about AFK stuff. So AFK away from
keyboard, you ping off the agent and it
goes and does something. Now,
I think that I mean the moment that I
discovered AFK was the moment I really
got into AI coding and the moment I was
really able to massively increase my
output because then instead of me having
to sit in the loop, handle all the
permissions requests, handle all of the,
you know, anything the agent needs to
ask me. The moment I can just remove
myself from the equation, I've paralyzed
myself. Suddenly there are two of me,
you know, or three of me, four of me,
five of me able to go and produce so
much more code that I then go and
review. This idea that loops are the
only way to do it is crazy, you know,
like we're essentially talking about the
history of this goes back to Jeffrey
Huntley. Uh where is it? G Huntley Ralph
uh goes back to Ralph. You remember
Ralph?
>> Yeah,
>> I was talking about Ralph in uh January,
I think. Um the original article comes
from 14th of July last year. And
essentially it's a loop. So this is the
idea where you have a while loop that
says okay pass this prompt to claude
code and then um eventually you'll be
done. Now it's essentially just uh
running clawed code again and again and
again. That's the idea of the Ralph loop
that I was talking about for a while.
And what I realized is I don't really
need to run this as a loop, right? The
only thing I need out of this is the AFK
agent to take on a specific task and do
that task.
>> The way I mostly think about these
things as cues, okay, cues, not loops.
The Q is really the backlog of tasks
that I need to complete. I'm looking at
the Sand Castle issues right now. These
are bug reports coming in about uh Sand
Castle feature requests, things like
that. I need to scope the item. Let's
say it's this for instance. So, I've
done a bit of triage here. It's um sort
of explored. Okay, is this trivial? Is
this possible? This uh was done AFK,
right? So, this this item has been
picked off the queue. It's been
explored, been put back on the queue. I
might then need to go and actually
implement this. Uh looks like yeah, this
looks pretty good. I'll actually add the
agent implement label and I'll go and
implement this in my GitHub action sand
castle setup that I was talking about
earlier.
>> Now, this isn't a loop really like it's
sort of just uh it's a queue that
eventually gets resolved. This will come
off the queue once it gets um uh once
the pull request gets merged. And that's
all development is really. You just have
a queue of tasks that you need to get
done. Project managers add more stuff to
the queue. You complete the tasks in the
queue. Like that's how we've always done
it. And there are multiple nodes picking
stuff off the queue, multiple
developers. And so an idea that there's
a single loop that just sort of goes and
completes all the tasks doesn't really
match with how like you developer teams
generally work when it's all sort of
inside GitHub actions like this. Uh
anyone any developer can add one of
these labels can trigger something and
can just get work going. So yeah, I I
think the idea of the loop is useful but
it's not the whole picture. And I think
an idea of a queue where you're picking
tasks off is is better. But mostly it's
just sort of nonsensical really. Like
when people talk about you need a loop
prompting your agent,
>> we're really just talking about AFK
agents.
>> Yeah. I guess uh when you talked, I
don't know why, but the the image that
came into my head is like a medieval
king managing a a kingdom with like some
ministers or whatever. And basically
assuming you know the king knows the
best know has most the most context not
like a king that just like randomly got
inherited empire right. So if you
deployed a minister into some region,
far region and you never heard from him,
never gave him commands, he would be
running on a loop and that could go
wrong or could go right depending on you
know how complex the issues are in that
region, how how smart the minister is,
whatever. But ultimately as as the king
in that medieval kingdom, you want to do
the Q approach. You want to have people
come to you and say like we have a
problem upcoming invasion you know or
there's a famine in this region and like
you have this queue of problems and you
are still in charge so that would be the
equivalent of a human here with you know
a bunch of agents bunch of AIs still you
would be prioritizing okay we have these
50 bug reports only three of them are
critical let's fix those first okay we
have these resources um this brand deal
this company wants to work with us check
their reputation first. Is that a good
way for to think about it?
>> Totally. And what we're doing here is
like you're still able to build tons of
automation into here. Let's say that I
had some kind of telemetry set up for
sandbox for sand castle or like an
observability tool like Sentry or
something. I could get a bug report from
a live application uh create an issue
from it immediately tag that issue as
like explore the issue. Maybe the agent
could return some structured data from
the explore saying can we fix this
immediately or does this need a human in
the loop? It goes and implements it. It
goes and reviews it. And then maybe it
has a little tag on it saying, "Can we
automatically merge this or does it
finally like um ping the user to go and
do it?" Like I see these systems as you
need human in the loop checkpoints and
you need to push those further and
further right further and further
towards the final thing as as or the
final output as you can. So you would
essentially get these like in instead of
like seeing the bug report, you would
see the bug report, you would see the um
exploration of the codebase, you would
see the fix and you see like um can we
review this?
>> Yeah. Just like that's that's what you
get as the human instead of seeing the
bug report and it's just so much richer
and it means it's one button click away
instead of a whole debugging session
away.
>> So that's I mean
>> so then the question here
>> where Yeah. Yeah. So the question in
that situation becomes because it's not
a loop right it only runs when the buck
comes there's no point for it to running
infinitely just paying open AI or
enthropic infinitely but my question was
like you know again as the AI gets more
powerful where because you mentioned you
push yourself further further to the
right to like last step is pushing to
production what are the like when does
it cross the threshold where like these
type of things whether it's like a small
UI change you know user requests a new
color scheme whatever like it could be
approved automatically, right? And then
maybe we go more and more. So, how does
that look like? Do you see what where
I'm getting at?
>> Oh, how do you remove human in the loop
checkpoints is what you
>> Yeah. Like where do you decide basically
where it's it's trivial enough for you
to not even look at, right? Like maybe
maybe all the agents you have which
again you set up the harnesses, they
have your skills, you use a good model
and all the agents are like, "Okay, this
is a small bug. It was just a misaligned
UI element. there is no, you know,
harmful intent from the user. The user
isn't trying to hack the application.
We're just going to merge it into prod
right away. That will presumably grow
like the the scope of things that could
be merged to prod right away. So, how
would you think about that?
>> Well, what I'd say is like what do you
gain from review, right?
>> Sure. You gain okay like you gain the
ability to gate things gate dangerous
things from going into production. So,
prevent um security bad stuff happening,
you Yeah,
>> prevent you know uh let's say uh claude
code source code being leaked to the
world you know you prevent that bad
stuff. Um, so but you also gain uh
insight into your own system into the
into the plumbing, right? So you're
watching the thing do its work and
you're assessing did it do a good job.
And so that second one, you don't want
to lose that because like again we're
talking about the harness, right? You
want to improve your harness over time
and you want some observability into it.
Now you could remove some human in the
loop checkpoint. So you could say, okay,
this uh this PR is just an internal
refactor. It just moves some code
around. It doesn't actually change any
behavior. And you could have an AI that
kind of says, okay, you don't really
need to review that one. But then who
reviews the AI that's doing that, right?
How do you give feedback to that over
time? You probably do need to check some
of the PRs that the agent says are fine
to review to check if they are actually
fine to review. And then you improve
that over time. And so we need to think
about this. We're not just reviewing the
code. We're also reviewing the system
that produces the code and that is
important and useful but I agree the
goal is to remove human in the loop
checkpoints where possible definitely.
So maybe the better way rather than like
okay let's say in a average day for this
application AI autonomously fixes 20
things and pushes to production right
away because they were super small at
the end of the day instead of you like
reviewing all these because that'll be
boring and slow maybe you get a custom
you know teach skill HTML file and say
like okay this is the common patterns in
the bugs that were fixed right so like
instead of you having to go through all
of the GitHub commits PRs whatever which
is not really optimized for this agenda
IC era. I mean again GitHub was created
a long time ago. It would be a custom
software, a custom HTML file, whatever
that's you know knows you, your learning
style, your common mistakes. It has a
history of the bugs in the past, you
know, whatever. And it would be more
optimized to helping you improve
yourself and the system.
>> Totally. I mean, one really cool like
what we're talking about here is in
making review seamless and taking um
taking the human effort out of review.
One thing that I've seen people do uh
which is crazy is on any front-end
change, it gets the AI to record a video
of itself walking through the code and
like the the thing that changed. It then
calls a texttospech API and overlays
some speech on top. So it's like the AI
is talking to you while it walks through
the code and you just have a video on
the PR of the thing working like that
sort of richness is something that we
should be building into everything that
we do and trying to optimize for human
review and make human review faster
because everyone's sort of moaning about
you know like oh man we've got so much
code to review but probably you could be
using AI to help you review the code
right like in in all sorts of
interesting ways that I think we're just
scratching the surface of Absolutely. So
a lot of people want to build something
with AI, right? Whether like you could
start with some personal tools, some you
know something for your team, but a lot
of people want to build a like business,
whether it's AI startup, whether it's
some other business. How would you think
about that? Like you know, a lot of
people there's there's a group of people
who say like, oh yeah, SAS, you know,
subscriptions, they're going to be more
valuable than ever because you're going
to be adding more seats for the agents.
There's a group of people who say like
SAS is dead. How are you thinking about
building a business, building software
in the age of AI?
>> Well, I I don't think that much has
changed about it to be honest. Um, like
again, I'm not a pundit. I don't really
watch markets. I don't really like care
whether SAS dies or thrives. Um, like if
you're building a business, what you
need to do is the fundamental stuff. You
need to go and talk to customers. You
need to figure out what they need. And
then you need to build stuff um like you
need to build prototypes that look like
what they need and solve their actual
problem. I don't think anything has
changed there and I think you can learn
to do that and be better with it but I
don't think AI gives you any particular
advantage there because what you need to
do is go out in the real world and have
conversations and figure out what it
actually is people need. So I think all
of the classic product design books will
still make sense here. It's just you
have a massive leg up when it comes to
actually implementing it and the
procedures they talk about you can start
delegating them to AI too. So mostly
though, it's just about having the right
idea and building the right thing. And
that's not something that AI can help
with. If you're not also talking to
actual people and figuring out what they
want, as soon as you figure out what
people want, um, you're good to go.
>> Yeah, I think that's actually the thing
that AI is notoriously bad at is like
the original ideas out of the box. And
uh yeah, like that would be probably one
of the main pieces of advice I would
give to people is like you need to be
choosing the features that get added,
right? If you see somebody who's like
delegating all of that, it's like what's
the next big thing we should add? It's
like no, you should be in charge of the
product. You could Yeah, obviously you
don't have to like learn the exact
syntax or whatever. You don't have to
read every file, but like you cannot be
asking the AI to build your app. Like
you need to have the vision. You need to
know why you're building it and like
what problem it's solving.
>> Absolutely. You should be asking AI what
thing you can remove from your app.
Basically, you should be asking how do I
make this simpler? How do I improve the
UX? How do I actually focus in on what
people want instead of ending up like
you know uh one of those dreadful uh VC
funded apps that we've all seen where
there's a thousand features and uh you
can't find the thing that you want to
do. So again, this is just product
design fundamentals.
>> We mentioned that senior devs get like
10x improvement and you know speed up.
How do you because from my experience
that's true but only if they actually
used the AI tools. There's a group of
there still developers still that are
kind of refusing to believe it or you
know AI is not that good. They tried it
a year ago two years ago they were you
know disappointed but obviously tools
harnesses models are much better. But my
counterargument or maybe it's not a
counter argument is like what about just
hiring the true if you were hiring
hiring young people who are true
believers in AI who like know these
tools inside and out. They use them all
the time. They know what's the best
model, what's the best skill, what's the
best, you know, agent in each situation.
And obviously, they need to have some
technical fundamentals. But like, how
would you reconcile this tension of like
these are seniors who have 10, 15, 20
years of experience and they get a 10x
versus these are like true AI believers
who might not have as much experience as
the seniors, but like are better
operators at using the AI? Well, hiring
great juniors has always been the goal
of any company basically because if you
find a great junior then anyone who's
enthusiastic will do a better job than
someone who's more experienced basically
like enthusiasm beats experience just in
pure output and because they develop so
much faster and they learn so much
faster. And so people who are really uh
excited about this new age and know a
lot about this stuff, if you can just
pair that with a little bit of software
fundamentals with because what we're
talking about here is I think of there
as being a difference between DX
developer experience and AX, right?
Agent experience. And so agent
experience is the experience that the
agent has working in the codebase. And
anything you can do whether that's um um
better skills um you know increasing the
power of the model works of course um
you know improving the harness and
improving the codebase as well is like
that's amazing often people forget about
improving the codebase actually for
better AX you know improve about uh they
forget about all the edges you can get
with like good software fundamentals and
so that's where the senior will be
useful because the senior knows how to
build good DX right they know how do if
they're a good senior, they know how to
build a codebase that can work well with
humans. And there's a huge overlap
between good DX and good AX, but they're
just coming at it like the junior who's
great at AI is just coming at the
problem from a different point of view
from the um senior. Um what was your
original question? How do they get hired
or like how do you or like you hire out
of both of them?
>> Sure. But not not like who would you
hire but like who will maybe get more
alpha you know who be more valuable like
is it like the senior who has a lot of
these experiences you know the right way
of thinking about software but maybe
isn't as true of AI believer and versus
somebody who's like fully embracing AI
to the maximum and knows how to use it
to the fullest.
>> I think if you have an experimental
mindset and you're excited about AI then
you're going to get a ton out of it
whether you're junior or senior. And I
think again if you're intrigued by the
harness first of all and intrigued by
improving AX everywhere that you can
then you're gonna you're gonna thrive
and love it. Now there's obviously a lot
of like good reasons that people have
for not wanting to get on the AI train.
You know they might um just be a bit you
know squeamish with the ethical stuff.
You know like anthropic stealing
everyone's um novels and just sort of
pumping them into Claude. Um, but
like it is here and it's that's how the
job is now. You know, if you're just a
tactical programmer just plumbing away
doing your work, you're gone, right?
Like that's out. You know, you can't be
a code monkey anymore. You need to think
strategically. And so seniors can
absolutely make the most of that. But
juniors can learn that, too.
>> All right. My closing question is going
to be practical for the people watching.
If you could take the average AI
enthusiast and give him like one or two
action steps to do today to either
improve his setup, improve his harness,
learn something, what would those one or
two things be?
>> First thing I would do is I would delete
every single skill, every single plugin,
every single MCP server. I would go
back. I' delete your claw.md, delete
your agents.mmd, go back to absolutely
nothing, and then observe the agent. See
what it does. In my experience, everyone
bloats up their context window with too
much stuff, with too many instructions.
Go back to a blank slate and see what
the agent does. Once you're seeing what
the agent does in that basic um sort of
mode, then layer things on top of it and
make sure those things are procedures,
procedure skills, not um ability skills.
Layer things on that you yourself
decide. And my skills repo is is a great
place to start there. If you really miss
something, if you really miss like
brainstorming from superpowers, then
bring that back. If you miss this, if
you miss that and make sure that you
install them in a way that you can
customize them, you can play around with
them and experiment, you know. Um, if
you're noticing problems, then try to
find solutions to fix those problems and
try as much as you can to delegate the
implementation to an AFK agent. Um, AFK
is just incredible way to work. It's
just takes a little bit of setup, but
once it's set up, it's just goes crazy.
>> Right, V? Appreciate your time. Where
should people find you?
>> Uh, find me on Twitter, find me at
aihero.dev, and I've got a newsletter
where I post about all this stuff. So,
aihero.dev, especially if you want to
learn about my skills and learn about
updates to them, then go to
aihero.dev/skills.
>> All right, I'm going to link all of that
below. Once again, thank you for your
time, Matt, and have a great day. Now
where is David?