TRANSKRIPT 🔗
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Jennifer Reif: You are listening to the
Breaktime Tech Talks podcast, a bite-sized
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tech podcast for busy developers where
we'll briefly cover technical topics, news
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snippets, and more in short time blocks.
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I'm your host, Jennifer Reif, an
avid developer and problem solver
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with special interest in data,
learning, and all things technology.
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We have come to the part of the year
where I am spending more and more time
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preparing for fall events that are
coming up, as well as beginning to
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plan my spring 2027 event submissions.
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I guess that means I'm living
in the present and the future at
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the same time, which is exciting
and sometimes a bit chaotic.
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I try so hard to prioritize only a few
things at a time, but this season always
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tends to push my resolve for saying
yes and saying no to certain requests.
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The book deadline, in case you might not
know, I am writing an AI First Java book.
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That book deadline is almost here,
and I'm sliding into the finish line
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and making some adjustments as I go.
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I am looking forward to the next steps
in getting the book out into the world
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and available, and then tackling a few
backlog projects that I haven't had the
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bandwidth to work on, and diving headfirst
into a busy travel season as well.
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Spring and fall always tend
to be very busy for advocates.
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I am getting more and more excited
about some projects that I plan to
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work on after the book, but I have
to press the brakes on those until
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I wrap up the book on a strong note.
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So I hope to have some more details
coming up as soon as I wrap up one project
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and am able to jump into the next one.
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I hope to have some updates soon too
on what I'm seeing in the market.
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I have had a few murmurings from various
sources on what's going on, what are the
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key topics, what are people concerned
about or working on or struggling
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with, but I wanna check those out and
get a pulse on the market a bit better
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myself before I provide my perspective.
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For instance, industry, tools,
important components are changing
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so fast, especially this year.
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And with so many other items that are
vying for my time, I haven't really
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focused on sifting the market hype
from the actual usefulness in a while.
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I will get back to that hopefully
soon, and I will definitely share
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anything that surfaces for me
along on my channels as well.
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I did just give a technical virtual
workshop on generative AI and
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GraphRAG, and GraphAcademy, which
is where I ran the content for the
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workshop training, has had a complete
makeover that is really impressive.
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I think I have mentioned the updates to
GraphAcademy in previous episodes, but
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this was really my first entrance into
presenting and working in that content
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and in that environment in a while.
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And I must say, I was
really impressed with it.
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It was a really nice experience for
me as the presenter and speaker, but
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also I think for attendees and anyone
who might go back later and re-watch
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recordings or visit content later on.
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There's some really nice navigation.
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It's a much smoother integration
between spinning up databases
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and working in coding tools and
integrating with, external services
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and other things like that.
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And it's a much more seamless experience.
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You're not having to jump around
near as much as what you used to.
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There are also a lot of
AI tool integrations too.
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There's a really nice integration to work
with an AI to build your own data model in
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case you might not be familiar with graph
or how to construct that or how to put
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your use case or your project idea into
a graph or into an actual project format.
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There's an AI integration inside
GraphAcademy that will help you
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build out a lot of those details.
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There are some courses that will
auto-generate API keys to use during
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the course, which is, I think, really
nice and a much more seamless experience
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than someone, a instructor, whoever,
trying to create something that then
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they have to be careful to shut down,
and everybody's using the same key.
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So having something
Load full transcript
that is
automatically created and gets closed
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out nicely and cleanly and can't be
abused or overused is really nice.
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Also a lot of the courses come with
a browser-based code environment,
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so there's no download or local
setup, install, and steps that
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are required during that process.
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I find this really nice and
a much better experience.
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From someone who is a Java
developer, there's some courses in
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Python, or there's some that are
focusing on other technologies.
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And for me to be able to spin up a
working, ready-to-go environment that's
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entirely browser-based without having
to set up my local environment for all
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of that stuff, I think is extremely
valuable and much easier to learn.
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There's always public and accessible
content as well for these courses.
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So you can access the data sets,
you can access the course content.
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All of it's public and always on
and always available, which is
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really nice in case you wanna go
revisit something or, hey, I can't
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remember where I learned this thing.
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I need to go back to this course and
review these lessons or what have you.
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There's also some built-in data sets and
AI tools to help you build what you're
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interested in, which I find really nice.
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So if you haven't checked out Neo4j
GraphAcademy in a while, I definitely
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recommend you go out and either review
a course you've already taken to
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refresh your memory or try one of the
new courses that has been spun up.
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There's some really nice, great things
that have been added to that that will
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hopefully make your experience better
than perhaps the last time you tried it.
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Neo4j has a flagship event coming up in
just a couple of weeks in New York City.
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It's called Graph Summit NYC.
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I will be helping with that, and
I just got some of the details
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on that, and there's gonna be a
really cool workshop format there
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that I'm really excited to try.
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I'll be there to provide some support
and to help with other tasks, and
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throughout the workshop, answering
questions and helping people get
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up to speed with some things.
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And it's a brand-new workshop format that
some of the team has put together, and
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I'm really excited to help out with that.
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I hope that attendees will learn
a lot and walk away with a working
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project of their own, which I think is
really exciting and really valuable.
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I also have that Northeast JUG
Tour that I mentioned last week.
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It'll be coming up towards
the end of September.
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I just finished booking all of the little
details and travel bits and pieces that
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I needed for that, so definitely come
join us if you're in the area for that.
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I'll leave all the links.
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I also got tagged for a
RAG benchmarking webinar.
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I am gonna be working on the content.
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This is a really important topic, and
it's something that I've wanted to explore
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for a while and really haven't been able
to block the time to focus in on it.
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So I'm excited to be able to do
that and produce some content
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to provide for this webinar.
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I'll notify you about more of the
details as the dates and times get fully
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hashed out, but I am looking forward
to working on that project here soon.
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And then, of course, I'm doing
some fall travel booking and
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spring 2027 conference abstracts.
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I mentioned last week that I
took a day for researching and
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coming up with new session ideas.
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A bunch of tech conferences have opened
or will be opening their CFPs, or call
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for proposals, for conference abstract
submissions, and they are starting
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to open those now for next spring.
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So I will be submitting a lot of
the abstracts I put together last
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week to those conferences, and
we'll see where things turn up.
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Now, the content for this week.
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I came across an article I thought was
really valuable and really interesting
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right now, and it's called Building
Software is Learning by Thorsten Ball.
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You guys know probably if you've
been around my podcast for a while
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that I love a good learning topic.
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I enjoy learning, I enjoy
technology,
and combining the two, I think, is
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always really interesting and fun for me.
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Now this article, the author posted
a Slack thread, and his colleagues
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really encouraged him to create
some blog post content from it,
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so that's exactly what he did.
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This blog post talks about the age-old
problem in software development is that
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someone asks you to build something,
and you go and you build it and you
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give it back to them, and they're
like, "This isn't what I wanted."
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Or, "Eh, let me… I wanted to make a few
changes," or, "I don't like it at all."
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And that, that requirement mismatch or
the miscommunication that happens during
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design and discussions, the author says
there is honestly no possible way to
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avoid that, that reality won't, at least
at some point, mismatch the expectations.
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However, one way to go about solving
this problem is that you can reduce
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the time from the initial building
of the thing to the uncertainty
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of those mismatched results.
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So you can't necessarily avoid some
sort of miscommunication or misalignment
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in the thing that you build and
the original request, but you can
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reduce that feedback loop and shrink
that down so that what you build is
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much more closely aligned with the
requirements and the ask as you go along.
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So you can build in very
small incremental things.
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You can build kind of a, a prototype
to at least get a general idea of,
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is this what you're looking for?
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You can do some other strategies that
the author walks through in the blog post
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to help combat some of these problems.
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So instead of jumping into building
the thing, we need to do some
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learning upfront to gather more
details and more information.
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Now, I think this is really valuable
in the AI space right now because AI
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requires a lot more context, right?
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You can't just tell them, "Go
build- this ABC project." It doesn't
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really handle that very well.
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You have to be very specific about
what you're looking for, provide
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it a lot of
context and a lot of
details, lots of boundaries and
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requirements, and all this sort of stuff.
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And so the same should work
even outside of AI coding loops.
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It should be much more of an iterative,
short loop process during development.
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I have actually come up against this
exact problem during the last two
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chapters of the book that I'm writing.
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I had a project in mind, the last
two chapters, just to recap you and
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refresh your memory, or if you're new.
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The last two chapters of the book
are working on a capstone project.
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It's a larger project that pulls together
all of the concepts and the things that
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the reader has been practicing through
the rest of the book and brings them
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all together into one larger project.
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You end up building something bigger
with both AI and Java together.
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The first segment of that project, when I
was putting it together, was really easy.
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I explained what I wanted and a
few key features and just gave
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it to Claude and let it run.
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The second segment of the project,
though, in this last chapter,
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has been much more challenging.
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I had a few specific features
that I knew I wanted to build.
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Even before I started the last two
chapters, I knew, hey, I'm gonna
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build this much in this first segment
of the capstone project chapter, and
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then I'm gonna build these additional
features in the, the second chapter
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of this capstone project set.
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And when I actually started putting the
features that I wanted to build for that
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last chapter into Claude, I realized what
I actually wanted meant that I had to go
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back and refine a few things on some of
the earlier features that I had built.
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So really, I didn't fully understand
exactly what I wanted, and even when I
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kind of knew what I wanted, I ended up
having to go back and make changes to
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earlier requirements and adjust those
in order to support the thing that I
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truly wanted in a later requirement.
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It is very hard to know exactly
what you need or what little details
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might be involved until you're up to
the moment of working through that.
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This is just one example that I worked
through live, and this was an example
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of working with an AI coding tool.
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This wasn't dealing with business
folks or other technical teams
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that all were trying to have a say
in a larger production system and
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application inside an organization.
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But this was just an example that I
was putting through for a book that
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hopefully readers will work through
and learn some of these concepts.
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And I even felt this
process at a smaller scale.
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So the goal then that the author talks
about in this blog post is how do you
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get that feedback as soon as possible?
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How do you shorten that feedback
loop so that you're getting that
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process much more iteratively?
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I think this actually works
well in the current industry.
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AI lets us prototype quickly and adjust
or redesign things with fewer setbacks.
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You don't get too far into the mix
before you start making adjustments
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or telling Claude, "Wait, that's not
what I wanted. Let me fix that," or
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whatever AI tool you happen to be using.
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On the flip side, though, you have
to be as specific as possible and
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outline more boundaries than what we
probably sometimes did beforehand,
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at least, knowing that things won't
work in a single-shot approach.
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It's very hard to get that
accomplished, and we are really
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feeling that pain with AI coding tools.
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You can't give a single-shot approach
and expect the AI coding tool to produce
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something that you actually wanted.
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We're having to reduce and shorten that
feedback loop and provide more context
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upfront, which I think is valuable
not only in the AI industry space, but
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also in our day-to-day work with other
technical and business folks as well.
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From wrapping up a long-term project,
preparing for travel and content in the
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near term, and prepping for long-term
coming next spring, it can feel like
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I'm living in multiple realities.
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I'm just along for the ride, though,
striving for high quality returns
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from everything I possibly can.
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This coincides with the blog post that
I covered today, where the author talked
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about how software development is an
iterative learning process, and the
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shorter your feedback loop the better
your results and solutions can be.
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Thanks for listening, and happy coding.