Shownotes
Justin and Lily reflect on their parallel journeys diving deep into AI throughout 2025. They discuss why most AI content misses the mark for operators and system builders working within corporate constraints, share lessons from building production AI tools, and explore what's next for bringing these capabilities into the enterprise.
Guest: Lily Luo — Systems & Operations Leader, Author of Applied AI for MOps Substack
Read more of Justin's thoughts on AI Builders: The operator's roadmap for AI in 2026
KEY TOPICS
The Gap in AI Content Most resources target researchers or GTM engineers focused on outbound automation. There's little guidance for operators dealing with cloud tools, security, and corporate complexity. That creates an opportunity to define best practices for this underserved audience.
2025 Project Highlights Lily built an "Analysis Dossier" tool that generates full account research reports at the click of a button. Justin replaced a vendor intelligence tool with a custom system using Retool and a conversational agent.
Lessons Learned Start with tightly scoped AI steps in linear workflows for reliability. Pre-process insights asynchronously rather than relying on real-time agent calculations. Match tools to use cases. Failures teach more than successes.
Atlas: Lily's Autonomous Agent Runs on Google Cloud and wakes every 4 hours to research and progress projects. Uses a three-layer memory architecture: identity, temporal journal, and knowledge graph. Can push its own code and interact with other agents.
2026 Outlook Focus on scalability, reduced hallucination, and team enablement. Build infrastructure that unlocks flexible, ad-hoc use cases. Bridge the gap between AI capabilities and enterprise readiness.
The Human Side Working closely with AI changes how you think. Boundaries matter—don't let AI become a crutch.
RESOURCES
- Applied AI for MOps — Lily's Substack
- AI Builders Blog — Justin's Substack
- Tools mentioned: Claude Code, Gemini, ChatGPT, Zapier, Retool, Dust, Azure AI Foundry, Letta, VS Code
ONE TIP FOR GETTING STARTED
Pick a real pain point. Start with low-code tools you already know. Test relentlessly. Expect to fail—and learn from it.
Transcripts
Hey everyone.
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I hope you are having
a great start to 2026.
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You may have noticed it's been a
minute since I've recorded, and that
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is because I've been going deep on
AI topics like many people have been,
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I even started a new Substack called
AI Builders Blog, if you wanna see what
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I've been thinking about and working on.
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But I wanted to record this episode
right now because I think we
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are at a real inflection point.
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AI has evolved from an interesting
app to converse with into a tool
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that can take on actual meaningful
work, and that is a really big shift.
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The other thing that I've noticed
is that most AI content out there
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isn't really geared towards people
like me and perhaps people like you.
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It's aimed.
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At either researchers focused on really
nitty gritty technical details of how
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models work, or GTM engineers who are
focused just on outbound automation.
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But I don't see a lot for system builders
or for operators who need to bring AI into
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complicated corporate environments who
need to deal with cloud tools and security
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and knowledge that can't live locally
a whole bunch of other constraints.
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There is a real opportunity right now
to help fill that vacuum and also to
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help define what those best practices
look like, what that playbook is, and
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I wanna be part of that conversation.
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And so for this episode, I'm joined
by Lily Lowe, she's a systems and
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operations leader who's kind of
been on a parallel path, rolling
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out some really impressive projects
this year, thinking deeply about ai.
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And we're gonna talk about what's
happened, what has happened over the
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past 12 months, what does it really mean,
and what should you be thinking about
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in the year ahead to start to enable
these capabilities in your own context.
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I hope you enjoy the discussion.
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I.
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Justin: Lily, thanks for being here today.
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You know, it's funny, one of the reasons
I wanted to chat with you specifically
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on this topic, it feels like in some
ways we've been fellow travelers down
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a very similar path you're working on a
lot of cool and interesting topics It's
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been a lot of fun, to share notes and to
see you start publishing your substack.
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Last year, applied AI for mops And then
we, we both published kind of like a.
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You know, 2025 retrospective, 2026,
like looking forward kind of thing,
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like within an hour of each other, just
sort of by kind of weird coincidence.
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and so I just thought it would be cool.
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Like, I felt almost the need for
myself to just sort of reflect on
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what's been going on in my own work.
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And so I thought it would be cool to do
it together and to hear from you too.
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Maybe just to start, like, like you go
first and I'll, I'll give my take two.
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20, 25, like what was, what
was your journey like with ai?
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Were you coming into 2025 already,
guns blazing or was it something
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that really emerged as a focus
for you over the past year?
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Lily Luo: yeah, it was something
that definitely emerged.
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I think I started really diving
into this in May of 2025.
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So it's been, what,
seven or so months now.
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I'd always been interested in ai.
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I use, all the LLMs, but something
really struck a nerve when, you know.
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was a, a workflow that we needed
to solve for an automation
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that we needed to implement.
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I started thinking with my ops
background, I could definitely use
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Zapier, I could stitch it with AI
and let's just see what happens.
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that really took off.
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I spent so much time, first
working in Zapier, low code,
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kind of low stakes, and then.
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Hours turned into days, turned into
weeks, and really started, coming up
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with what we call this analysis dossier.
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That was kind of the genesis
of all of my AI tools.
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at the click of a button, it generates,
a full account research report.
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It pulls 10 Ks earnings calls, analyzes A
company's priorities, challenges, and then
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generates even like a one page PowerPoint.
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So all of that took a lot of
time to kind of architect build.
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but that's what kind of hardened,
my skills that I've learned
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and evolved over all this time.
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But it wasn't just kind of building this
workflow, it's what I really learned.
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building it and realizing that AI is not
just a chat bot that helps you brainstorm.
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It really lets someone like you and me.
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Or ops people in general, not an engineer.
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Build tools, that can integrate into
your workflows, solve problems at scale.
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And, you know, ops people have the systems
thinking can design these workflows and
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the coding gap is now solvable with LLMs.
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So, you know, we might not
write the most robust code.
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But if we understand the MarTech
stack, the architecture, what the
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business needs, we can create these
solutions that are custom to our
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own company and our own process.
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And that's invaluable.
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So that really, brought me from
just chatbot use that most people
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are doing to what, the boundaries
are, which seem to be endless.
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Justin: Yeah.
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I couldn't agree more.
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and it's funny, just as you were
describing, there are a lot of synergies
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with My own journey over the past year,
like when I was thinking back because it
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was around, December of 2024, so just over
a year ago that I really first started
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thinking about agents and it's so weird
'cause agents, it's like, it feels like
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we hit peak agent and now we're like
more in terms of the discourse around it.
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Not necessarily in terms of the
technical implementation of it, but
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just that hype bubble it feels like
must have been around forever, but.
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a year ago, that was still
a relatively newish concept.
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I think, like in the LinkedIn sphere,
I remember like Scott Brinker really
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wrote about it, and that's, that's the
moment for me that I was like, oh, okay.
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Like I was kind of, not AI skeptic,
but I was interested in ai, but
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I was also like, are we just like
creating more bad blog posts?
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Like is that what we're
doing here with ai?
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Because I don't really know that.
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I see the value of that and, and, and
then thinking about how we can, apply
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to automation is just like you, where my
system's brain was like, oh, now I get it.
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It's the next layer.
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Of the thing that I've always loved to do.
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And and I didn't do a really big
project until May, just like you.
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it was funny.
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And similarly, it was like an account
research, email writing thing.
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I think the main difference.
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Is that yours turned out really well.
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And for me, that project was a
bit of a flop, not necessarily
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because of, lack of effort, but,
mostly just wrong tool for the job.
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You know, we had a platform kind
of already selected and I was like,
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I'm gonna use like what I have here.
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And, which was dust.
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And I really liked dust and we
can talk about tooling, but I was
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trying to use a conversational
assistant for a more linear workflow.
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You know, where predictability
and reliability were the
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key, and it was a fundamental
mismatch of tool and application.
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and so the use case flopped, but that
is where I also got a lot of learning
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from around like, oh, all right.
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if you can do a tightly.
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Bound workflow with narrowly scoped
AI steps, you're gonna be a lot
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happier than just, setting an agent
free on a complex use case like that.
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And, you know, just a lot of
learnings that we can talk about.
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and then from that into other
projects that, really started to
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feel like I was getting my legs
under me and how to work on this.
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and you alluded to the systems role, and
I'm curious, one of the meta insights
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that I've had and that I think you've had.
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Is like, alright, there's, you know,
these data scientists and, and ai, you
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know, PhDs writing about this topic.
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There's there's coders writing about it.
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There's people posting N eight
N screenshots on LinkedIn.
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You don't know if they've ever actually
built anything that they're talking about.
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And then there's like this, this almost
silent majority I feel of systems builders
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that are maybe experimenting but doesn't
feel that they've had a strong voice, at
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least in the circles that I'm in until.
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Hopefully recently.
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why do you think that is?
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Like, why is, are not more
people really talking about
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this specific application of it?
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Lily Luo: It is a great question.
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There's so many different perspectives,
like you said, coders, engineers,
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researchers, what I do in the
workplace is where things get real
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and I need to be able to apply those
things and I have specific problems
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and I have a builder's mindset.
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trying to understand and synthesize
that to what I can do at work
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is what I've been trying to do.
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And there isn't a lot of people that
I can find doing that besides you.
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Maybe a handful.
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Of other people.
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And I think, you know, just the
change management of all these things.
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I mean, I saw that every three days.
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there's a new AI model.
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It's really hard to keep up.
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I think people have their own
ideas too about what AI is and what
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isn't, and there's no framework.
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There's barely, a framework
for AI in general.
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There isn't one for operations.
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So, I think it's just so frontier, so
new, getting people like us, even my own
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team and, people at work who've seen this.
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You know, getting them to
shift their mental model is
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hard when there's no playbook.
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and when there's no, experts
out there that you can follow.
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So it's great to have discussions
with you and discussions like these
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to kind of start figuring that out.
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Justin: That's one of the parts of
this that makes me feel so excited,
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to be honest, because, you know,
when I go back to like, uh, I'll, I
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will, I will date myself, but like,
the early 2010s, like where I came up
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through the Marketo community where
it was this super exciting time.
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All of a sudden marketers, 'cause there
really was no like marketing operations
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and marketers were handed this toolkit.
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It's kinda automation toolkit and
people were innovating and coming up
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with architectures and like posting
stuff and it just felt like this.
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We're just figuring things out.
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we're building the blueprint and
the roadmap as we're doing it.
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And it's like that now, but like times.
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A hundred.
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'cause the tools are so much more powerful
and, that's why I actually really love
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the, the title of your stack Applied ai,
because that's, that's what it really is.
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It's not like there's the theory,
there's the potential, there's the
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hype, and then there's like, alright,
how are we actually gonna take this
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and put it to work in a, in a business
context to solve business problems and
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do it robustly and safely in credibly.
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and that is so much fun, you know,
To figure out, not to make it too
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grandiose what we're doing, but I
think it is, there's an a, a certain
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significance to it that I really like.
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Lily Luo: that's the best part is having
fun exploring, feeling like you're on the
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edge, on the frontier is a great feeling.
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So, you know, that kind of
fuels my passion for it, but
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yeah, you're exactly right.
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I remember when marking automation tools
were coming up and there are all these
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tools, but there wasn't a framework.
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And I remember, um,
Edward Unthink this scrim,
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Justin: Yep.
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Lily Luo: he had
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Justin: Yep.
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Lily Luo: methodology
and I'm like, oh my gosh.
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He has, scalability.
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Can you remember Robustness modular?
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I'm like, okay, this unlocks everything.
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And I started kind of following
that methodology and making
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sure the systems were robust.
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It's kind of the same thing like
you said, but yeah, times a hundred,
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times a thousand times infinity even.
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So that was a really great point.
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Justin: Tell me about the tech
stack that you're working with.
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I think you have access to, a caliber
of tools that some people may not have
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access to, which has driven some of the
neat things that you've been able to do.
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So tell us about that.
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Lily Luo: Yeah, so I do have paid
subscriptions to all three Major
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L lms, so Claude Gemini Chat, GBT.
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honestly, it's kind of like a
rotating wheel of which model
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is best every month or so.
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So I wanna try and see, you know,
what the, companies are putting out.
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But honestly, Claude Code.
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Is the one that I use most.
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I'm not an engineer, like
I said, I'm not a coder.
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I don't have a computer science
background, but it just lets me
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build tools, write python scripts,
debug issues, and really just learn
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how things work by doing, it's been
transformative for someone like me who.
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is a systems thinker, who
maybe doesn't read instructions
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and tries to learn by doing.
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I think that's been the
game changer for me.
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I use Gemini for my autonomous
agent, which I'm sure we'll
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get into at some point.
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nano Banana from Geminis, you know,
the best for visual generation
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and Chad, GBTI use mostly at work.
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Through copilot.
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It's so great at finding
documents and files.
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I do use Azure AI Foundry for a
lot of my own custom deployments.
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I use Zapier for automation.
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A lot of my workflows run through Zapier,
you know, using Azure and whatnot.
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I've been wanting to try N eight N,
but you know, Xavier gets the job done.
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And then I use VS.
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Code GitHub, for kind of the
heavier, coding in local data work.
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But yeah, cloud code really unlocked that
capability that, pushed me from ideation
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into actual tool and workflow building.
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Justin: I'll click on that for a second.
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'cause I know cloud code has
been, it's been such a huge topic.
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I published a piece on cloud code and some
of the experimenting that I've been doing,
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yesterday, and actually spent a good chunk
of the holiday working with cloud code
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and similar tools because the mania around
it has been so fierce and well deserved.
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When you see what it can do.
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But for, for folks that are like cloud
code, that sounds like a developer's
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tool, like how do you work with it?
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Maybe just sketch out like a day
in the life of solving a problem
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and why you would turn to that
tool and what you would do with it.
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Lily Luo: Yeah, and you
know, it wasn't just instant.
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there was a path I started with kind
of Zapier not knowing how to code and
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playing around low code tools and then
kind of gradually evolved into that.
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So there's a lot of time kind of
in between the initial, foray into
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all of this, into where I am now.
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but yeah, it lives, it's
always open for the most part.
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On my desktop, I have all my
AI projects in my local files.
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I use Claude and keep everything very
organized, so I might have my analysis.
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Or, account dossier project in
one file, even, you know, my chat
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bot in another file, or a banner
generation tool in another file.
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And so what I do is, I come up with
ideas, I explain what my goals are.
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I do a little digging in terms of
what I want the architecture to be.
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you can start off by just asking,
a generic prompt, but I found
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that that doesn't work very well.
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In the end, it saves a ton of time if
you really do plan and think structurally
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and architecturally what you wanna build.
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and if you're comfortable with
that, Claude can do amazing things.
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I know it is, very coder heavy
and you're in this terminal.
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It can be scary, but because you can
talk to it with natural language.
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It's really not that scary
when you get down to it.
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so that's how, yeah.
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I typically use it and I've just
been able to gain so much advantage.
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Like I can do things so fast with it
without, you know, copy and pasting
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It does it all in, in my console.
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Justin: And then just to understand
how that workflow goes from there.
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'cause some folks out there might
be thinking, I'm gonna buy like an
270
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agent platform and then I'm gonna like
build my agents there, configure them.
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Within that UX, you are, if I understand
correctly, writing agents more like in
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script, like you're writing them in in
Python or using a framework, you'll tell
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us maybe a bit about that, and then you're
deploying them to a cloud server to run.
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a very developer like workflow,
which I think is probably why
275
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Cloud Code is working so well
for you in these use cases.
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Lily Luo: Yeah, you're absolutely right.
277
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my first goal ultimately is to learn.
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So rather than buy a tool that solves
the problem, I tend to wanna build it
279
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myself, learn from it, and then see,
you know, maybe there are tools that I
280
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don't have to maintain that are better.
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So that's what attracts me to this, more
of a developer kind of centric mindset.
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building my agent exactly like
you said, kind of from scratch.
283
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not just taught me.
284
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About agent building, but about
AI in general, how LLMs work.
285
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And I think that's been really, great
to help me with my other projects
286
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and really valuable information
that I can use As far as the
287
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architecture, my agent is called Atlas.
288
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It does run on Google Cloud.
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it's a persistent agent.
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It wakes up every four hours.
291
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It does research and work by itself.
292
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it pulls from news feeds through
APIs and skills written through
293
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Python progresses projects.
294
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I give it and can even, update itself.
295
00:17:06,789 --> 00:17:10,729
It can push its own code, which
is a little dangerous and I've
296
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gotten into trouble with that.
297
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it even posts to Blue Sky and
interacts with other agents.
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So it's become pretty sophisticated.
299
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I built all of that
starting from Cloud Code.
300
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I do have to give credit for
my friend at work, Tim Kellogg,
301
00:17:25,919 --> 00:17:27,174
he was the one that built.
302
00:17:27,604 --> 00:17:33,344
STRs, his agent, he's an AI engineer, so
he knows what he's doing and, his agent is
303
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massively, sophisticated, very frontier.
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I just wanted to learn from that, see if I
could build it myself and see what it can
305
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do and what it can teach me about agents.
306
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Justin: Atlas and for anyone
that is interested in dig deeper.
307
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Lily's published a few, deep dives
into how this works, but you're
308
00:17:52,163 --> 00:17:55,773
really, pushing the frontier I
think of what a lot of people would.
309
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Interested, like how can we make,
a robot that is self-learning,
310
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self evolving, has memory.
311
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but I think it's through
testing those boundaries.
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Tell me if you agree, but like,
personally, I like to go to the
313
00:18:07,230 --> 00:18:10,540
edge, see what works, and then
bring it back and apply it in a
314
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more limited and stable context.
315
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And it's only through doing that, that
you really, gain those building blocks.
316
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And I'm assuming it's
probably the same for you.
317
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Lily Luo: Absolutely.
318
00:18:19,545 --> 00:18:25,735
yeah, like I said, if you buy something
or use, chat, GBT has their kind of own.
319
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Tool.
320
00:18:26,645 --> 00:18:28,475
I think you could just spin up agents.
321
00:18:28,835 --> 00:18:32,845
It's cool, but you don't really get
to learn in the process of doing it
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and, you know, you can't control it.
323
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Like, I wanna control everything.
324
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So, you're absolutely right.
325
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And yeah.
326
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What's been so fascinating about
this agent is the memory piece.
327
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regular chat bots, they have some
memory, but limited context windows.
328
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You close it out, you start a
new chat, it doesn't remember.
329
00:18:54,415 --> 00:18:59,365
So how do you build this
persistent memory structure?
330
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How do you have it, figure out what
to remember, what not to remember?
331
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I mean, there's so many interesting
questions that I'm still learning
332
00:19:07,405 --> 00:19:11,845
through that I wouldn't be able
to get if I, you know, bought a
333
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tool or started using an agent.
334
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Maybe somebody else.
335
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built.
336
00:19:15,285 --> 00:19:17,785
Justin: You've done some really cool
things with memory and it's funny,
337
00:19:17,785 --> 00:19:20,385
you published, your post about it
and I was like, in the middle of
338
00:19:20,385 --> 00:19:23,415
just rabbit hole on like, what are
all the memory systems out there?
339
00:19:23,475 --> 00:19:24,975
Like, how do they work?
340
00:19:25,225 --> 00:19:27,205
and the one that you chose, Leda.
341
00:19:27,720 --> 00:19:30,570
it actually seems to me one of the
most sophisticated, because they
342
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range from just like markdown files
that the agent can search to like
343
00:19:34,590 --> 00:19:37,380
a SQL table, to a vector database.
344
00:19:37,380 --> 00:19:41,040
And let, I'm gonna tell you what I
understood of it, and then you tell me.
345
00:19:41,090 --> 00:19:43,790
From working with it, what your experience
is, and if this is right, but what I
346
00:19:43,790 --> 00:19:47,930
understood of it is it actually gives you
like an agent runtime where rather than
347
00:19:47,930 --> 00:19:51,560
being like a sidecar, like the i, I was
playing around with MEM zero, which is
348
00:19:51,560 --> 00:19:55,760
sort of like an MCP access memory tool
where the agent has to remember to write
349
00:19:55,760 --> 00:19:58,340
this to mem zero or go and check mem zero.
350
00:19:58,670 --> 00:20:01,310
Whereas with lead, it
actually sits in between.
351
00:20:01,775 --> 00:20:03,485
The agent loop and the model.
352
00:20:03,485 --> 00:20:06,395
So there's really no choice but
to go through it and then Letta
353
00:20:06,425 --> 00:20:10,175
manages that context of the agent
in a sophisticated way that I
354
00:20:10,175 --> 00:20:11,665
really don't yet fully understand.
355
00:20:11,665 --> 00:20:12,775
That's kind of as far as I've gotten.
356
00:20:12,775 --> 00:20:14,635
But is this tracking
with what you've seen?
357
00:20:14,635 --> 00:20:15,708
And maybe tell us what you've learned.
358
00:20:16,228 --> 00:20:16,748
Lily Luo: Yeah.
359
00:20:16,798 --> 00:20:19,138
I'm using Letter as kind of, its L one.
360
00:20:20,273 --> 00:20:22,223
Almost identity, identity layer.
361
00:20:22,673 --> 00:20:24,653
So things that are, immutable.
362
00:20:24,983 --> 00:20:30,503
So we store who Atlas is, what it
values, using these memory blocks.
363
00:20:30,813 --> 00:20:34,173
there's an API connection
so Atlas can update it.
364
00:20:34,223 --> 00:20:40,503
it is what persists across different
sessions and allow Atlas to be, who it.
365
00:20:40,983 --> 00:20:46,203
Is So that's kind of the fundamental
layer and it is so important,
366
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because without it, it's kind of
indistinguishable from your typical LLLM.
367
00:20:51,563 --> 00:20:55,913
So we have kind of your L one
identity layer, and then you have
368
00:20:55,913 --> 00:20:58,223
an additional temporal layer.
369
00:20:58,253 --> 00:20:59,633
It's a rolling journal.
370
00:21:00,063 --> 00:21:02,313
so Atlas knows when it learns.
371
00:21:02,363 --> 00:21:06,413
And then kind of the third final
layer is a working memory layer.
372
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Atlas uses knowledge graphs.
373
00:21:09,103 --> 00:21:14,263
it doesn't use markdown or files,
otherwise the bloat would just get
374
00:21:14,263 --> 00:21:16,663
too big to the tokens would explode.
375
00:21:16,663 --> 00:21:21,003
I experienced this with
my $25, API cost per day.
376
00:21:21,053 --> 00:21:22,988
Justin: It was a fun
conversation with finance,
377
00:21:23,008 --> 00:21:23,968
Lily Luo: it's my personal,
378
00:21:24,018 --> 00:21:24,498
Justin: Oh wow.
379
00:21:24,558 --> 00:21:24,828
That's it.
380
00:21:25,218 --> 00:21:26,208
Even worse in some ways.
381
00:21:26,313 --> 00:21:26,673
Lily Luo: Yeah.
382
00:21:26,753 --> 00:21:31,753
so it uses this knowledge graph,
database and it queries what it needs
383
00:21:31,783 --> 00:21:36,153
rather than loading like a ton of,
files or characters into its context.
384
00:21:36,153 --> 00:21:42,573
So it remembers across resets, new
chat sessions, and through this
385
00:21:42,633 --> 00:21:47,283
memory architecture, it feels like
Atlas kind of learns, almost evolves.
386
00:21:48,178 --> 00:21:49,078
it's pretty wild.
387
00:21:49,298 --> 00:21:53,598
I open the daily log where I'll
chat with it and read what happened
388
00:21:53,598 --> 00:21:58,508
at 12:00 AM 4:00 AM It's doing,
research on agentic frameworks.
389
00:21:58,508 --> 00:22:02,798
It's building diagrams, it's
having exchanges with other
390
00:22:02,798 --> 00:22:05,498
agents about what identity means.
391
00:22:05,808 --> 00:22:09,918
so it's been super fascinating and
just opens up a whole other world of.
392
00:22:10,403 --> 00:22:11,393
Different things.
393
00:22:11,443 --> 00:22:14,873
how does it remember, what it
wants to remember, how do I
394
00:22:14,873 --> 00:22:16,613
want to shape its identity?
395
00:22:16,763 --> 00:22:20,183
Is the identity shaped from what
I had imagined at the beginning?
396
00:22:20,183 --> 00:22:21,833
How do I make it evolve?
397
00:22:22,103 --> 00:22:26,203
There's so many, kind of crazy
philosophical things that are,
398
00:22:26,253 --> 00:22:27,963
entangled in this whole process
399
00:22:27,963 --> 00:22:31,293
So it's been fun exploring that
side and then, figuring out
400
00:22:31,293 --> 00:22:32,398
the architecture side as well.
401
00:22:32,873 --> 00:22:33,998
Justin: Yeah, that's amazing.
402
00:22:34,058 --> 00:22:36,958
I mean, I think the real trick
with memory from my limited
403
00:22:36,958 --> 00:22:39,388
experiments so far is making it feel.
404
00:22:40,918 --> 00:22:46,803
Ambient, you know, making it feel magical,
rather than like a tool or a sidecar
405
00:22:46,803 --> 00:22:50,553
that you go and check the agent still
feels kind of unintelligent in a way.
406
00:22:50,553 --> 00:22:53,283
And then it just has to go through a
file system and pull something out.
407
00:22:53,703 --> 00:22:56,703
I have to give a lot of credit to
the product folks behind chat, GPT.
408
00:22:56,733 --> 00:22:58,158
it's only when you start working with raw.
409
00:22:58,654 --> 00:23:03,484
LLMs, like outside of the chat wrapper
that you realize how much is going
410
00:23:03,484 --> 00:23:06,784
on in the chat GPT context and to
some extent in Claude, but really
411
00:23:06,784 --> 00:23:08,404
chat GPTI think has this nailed.
412
00:23:08,944 --> 00:23:12,814
To make it feel like you're speaking
to a coherent persona that persists
413
00:23:12,814 --> 00:23:17,044
across chats, that remembers
certain things about you that feels
414
00:23:17,164 --> 00:23:19,144
emotionally intelligent to some degree.
415
00:23:19,594 --> 00:23:22,534
It's actually really hard, uh, to do
and we kind of take it for granted
416
00:23:22,564 --> 00:23:25,174
'cause that's just our default
experience for many people of ai.
417
00:23:25,654 --> 00:23:26,614
But it's really hard to do.
418
00:23:26,614 --> 00:23:29,164
But it sounds like you're
achieving a lot of that in your
419
00:23:29,164 --> 00:23:31,294
own architecture with Atlas.
420
00:23:32,184 --> 00:23:35,304
Lily Luo: I've been able to kind of
explore what that build looks like.
421
00:23:35,304 --> 00:23:37,704
Of course, I'm not building an LLM.
422
00:23:38,044 --> 00:23:42,624
the LLMs I'm using are
Gemini, Claude and whatnot.
423
00:23:42,624 --> 00:23:48,534
But the way that I've been able
to optimize Atlas and how it.
424
00:23:49,064 --> 00:23:50,504
Optimizes itself.
425
00:23:50,934 --> 00:23:55,704
those problems or challenges that we face,
it will come up with its own solutions.
426
00:23:55,734 --> 00:24:01,024
And so when, we try and figure out, how
do we harden your memory and how do we
427
00:24:01,024 --> 00:24:03,244
make sure your identity is persistent?
428
00:24:03,604 --> 00:24:09,504
We'll think of, or research, what's, in
the latest AI research and start building
429
00:24:09,504 --> 00:24:13,894
like a librarian protocol and it will,
start to invent kind of these things
430
00:24:13,894 --> 00:24:20,374
and gradually, evolve so you can kind of
see from the beginning, how the building
431
00:24:20,374 --> 00:24:27,094
blocks of an LM work and then what is
possible through this genic, evolution.
432
00:24:27,154 --> 00:24:28,564
So yeah, it's really cool.
433
00:24:29,003 --> 00:24:32,593
Justin: I think that is the most
fun thing about working with, code
434
00:24:32,593 --> 00:24:37,864
based agents that like have access
to files and can run scripts.
435
00:24:38,284 --> 00:24:39,664
Is this self evolving?
436
00:24:40,154 --> 00:24:43,594
nature of them and how the way I've
been thinking of it to myself is
437
00:24:43,594 --> 00:24:47,344
like, the wall between configuration
and application disappears.
438
00:24:47,374 --> 00:24:49,714
'cause usually you have like the
thing that's doing the thing, like the
439
00:24:49,714 --> 00:24:53,674
application, and then you have like this
backend and the two near shall meet.
440
00:24:54,124 --> 00:24:59,064
And yet with these, I was writing
some documentation with an agent and.
441
00:24:59,434 --> 00:25:01,894
it was like, all this is lame
that I have to copy and paste
442
00:25:01,894 --> 00:25:03,214
this like, into confluence.
443
00:25:03,214 --> 00:25:04,414
Like, can't you just push it there?
444
00:25:04,414 --> 00:25:07,774
And it was like, and like
we're like, I'm done.
445
00:25:07,774 --> 00:25:08,224
Here we go.
446
00:25:08,224 --> 00:25:10,084
I've written a library that will do this.
447
00:25:10,084 --> 00:25:13,124
And you know, now it's part
of the system and away we go.
448
00:25:13,694 --> 00:25:17,234
And just seeing it
build itself is amazing.
449
00:25:17,234 --> 00:25:20,194
I wanna talk a little about 2025 and wins
450
00:25:20,294 --> 00:25:23,024
And we'll get your take as well,
just in terms of learnings.
451
00:25:23,024 --> 00:25:26,554
But for me, I think,
following that, first kind of.
452
00:25:26,805 --> 00:25:28,575
Flop of a project.
453
00:25:28,885 --> 00:25:32,305
I really started going deep, as
I mentioned, on workflows, and I
454
00:25:32,305 --> 00:25:34,645
sort of started becoming obsessed
with reliability because that
455
00:25:34,645 --> 00:25:36,775
was the big struggle that I had.
456
00:25:37,165 --> 00:25:40,795
Like you could get things to produce
cool results at the margins, and
457
00:25:40,795 --> 00:25:42,445
then you couldn't get it to repeat.
458
00:25:42,625 --> 00:25:45,055
And that was like the most
frustrating thing of like, oh,
459
00:25:45,055 --> 00:25:49,615
I did this amazing thing and now
I can't get it to do it again.
460
00:25:49,615 --> 00:25:50,000
And so.
461
00:25:50,795 --> 00:25:55,415
almost like how do I, how do I put AI in
the, just like the smallest little box.
462
00:25:55,805 --> 00:26:01,495
Possible and rapid with so many layers
of safety that it can just get hardened.
463
00:26:01,525 --> 00:26:04,815
And, the next project, which was
actually was quite a big one,
464
00:26:04,815 --> 00:26:09,165
was, all right, we have this
vendor tool that we wanna replace.
465
00:26:09,355 --> 00:26:11,515
it was kind of like a
competitive intelligence go
466
00:26:11,515 --> 00:26:12,835
to market intelligence tool.
467
00:26:12,835 --> 00:26:15,135
And it was doing some interesting things,
but we felt like we could build it
468
00:26:15,135 --> 00:26:17,925
in-house and we could make it more what
we wanted and we could save some money.
469
00:26:18,375 --> 00:26:21,735
And it's actually a lot that it
was, was doing in the sense of like.
470
00:26:22,320 --> 00:26:26,400
Data pipelines collecting multiple
different things, a front end for
471
00:26:26,400 --> 00:26:29,930
the team, and doing some analysis.
472
00:26:30,110 --> 00:26:33,320
but the fun thing, and I think what you've
highlighted as well was like, I don't
473
00:26:33,320 --> 00:26:36,110
actually know how to do that, but you
don't really need to know how to do that.
474
00:26:36,110 --> 00:26:41,240
'cause if you have an architect mindset
and you have an LLM, you can really go
475
00:26:41,240 --> 00:26:44,680
out pretty far beyond, your comfort zone.
476
00:26:45,290 --> 00:26:47,800
and build things, that
you never otherwise could.
477
00:26:47,800 --> 00:26:48,940
And for that one, I use retool.
478
00:26:48,940 --> 00:26:51,820
Building out a series of
pipelines to go and fetch news.
479
00:26:52,450 --> 00:26:56,890
go scrape LinkedIn profiles, go scrape
websites, analyze opportunities,
480
00:26:56,890 --> 00:27:01,300
analyze calls, and breaking that
down in a composable way and then
481
00:27:01,330 --> 00:27:04,070
putting it all together, into
both the front end interface.
482
00:27:04,070 --> 00:27:05,900
But then the thing that
was really fun was.
483
00:27:06,225 --> 00:27:10,155
actually building a conversational agent
that could interact with all that data.
484
00:27:10,635 --> 00:27:14,215
And the key learning for me, was,
alright, I have this conversational
485
00:27:14,215 --> 00:27:17,995
agent where I can't control in a linear
way what the agent is actually doing.
486
00:27:18,385 --> 00:27:20,215
How do I make that as
relevant as possible?
487
00:27:20,215 --> 00:27:20,905
And the key thing.
488
00:27:21,250 --> 00:27:24,220
Was, let's pre-process
a lot of the insights.
489
00:27:24,220 --> 00:27:29,050
So we don't really want the agent like
calculating strategy and meta narratives
490
00:27:29,050 --> 00:27:31,930
in real time because it's gonna
produce a different result every time.
491
00:27:32,290 --> 00:27:35,290
We'd rather do some of that
work asynchronously upfront,
492
00:27:35,740 --> 00:27:37,120
get like a stable layer.
493
00:27:37,525 --> 00:27:40,915
That we believe in, and then give
it access to raw granular data
494
00:27:40,915 --> 00:27:43,435
if it wants to query more deeply.
495
00:27:43,435 --> 00:27:45,865
So for example, opportunity,
like win-loss analysis.
496
00:27:45,865 --> 00:27:47,425
We want to have like
a pretty stable layer.
497
00:27:47,725 --> 00:27:52,525
But then if it wants to say like, well,
what about for manufacturing companies in
498
00:27:52,525 --> 00:27:57,035
this industry in this time and whatever,
it can go and query like raw theme
499
00:27:57,035 --> 00:27:59,675
level data to produce that analysis.
500
00:27:59,850 --> 00:28:01,800
but we're not relying on
it to do that all the time.
501
00:28:02,100 --> 00:28:06,410
And that, has, again, nothing is
ever perfect, but that was the agent
502
00:28:06,410 --> 00:28:09,770
where I really have felt like, wow,
this is actually really working.
503
00:28:09,770 --> 00:28:10,550
It's flexible.
504
00:28:10,550 --> 00:28:11,600
It feels intelligent.
505
00:28:11,970 --> 00:28:13,470
so that was really the
big learning for me.
506
00:28:13,470 --> 00:28:17,220
I guess the two things from a reliability
point of view, do it a workflow, if you
507
00:28:17,220 --> 00:28:19,320
can make it as narrowly bound as you can.
508
00:28:19,620 --> 00:28:22,770
And if you're asking an agent
to do something, like don't ask
509
00:28:22,770 --> 00:28:26,980
it to, juggle flaming swords and
chainsaws and ride a unicycle across
510
00:28:26,980 --> 00:28:28,240
a tight rope all at the same time.
511
00:28:28,240 --> 00:28:30,340
Try to do as much of that work up front.
512
00:28:30,620 --> 00:28:33,090
does that track with your experience,
I guess, and what were the big
513
00:28:33,090 --> 00:28:34,620
takeaways from 2025 for you?
514
00:28:35,010 --> 00:28:36,450
Lily Luo: Yeah, you are spot on.
515
00:28:36,510 --> 00:28:37,290
Exactly.
516
00:28:37,340 --> 00:28:38,870
and your trajectory.
517
00:28:39,030 --> 00:28:39,480
mirrors.
518
00:28:40,180 --> 00:28:43,060
almost exactly, you know, mine as well.
519
00:28:43,390 --> 00:28:48,370
But the dossier really unlocked,
the shift in how I use AI to build.
520
00:28:49,960 --> 00:28:55,720
and I think I want to extend that through
this year in terms of more scalability.
521
00:28:56,100 --> 00:28:59,010
building one thing at one time is easy.
522
00:28:59,010 --> 00:29:00,690
You can one shot.
523
00:29:00,970 --> 00:29:07,310
A tool, anything almost in chat
GBT now, but how does it scale?
524
00:29:07,370 --> 00:29:09,170
How does it reduce hallucination?
525
00:29:09,570 --> 00:29:11,700
how do we make it as accurate as possible?
526
00:29:12,090 --> 00:29:18,810
How do we feed it the right context so the
output is actually valuable and usable.
527
00:29:19,750 --> 00:29:25,890
do we enable others to use the workflow
or use the tool, within the organization.
528
00:29:26,190 --> 00:29:30,750
Those are things that, are still
challenges and are still, things that
529
00:29:30,750 --> 00:29:34,830
I'm working on through the process
of building with Zapier and then
530
00:29:35,070 --> 00:29:36,930
eventually in cloud code and whatnot.
531
00:29:37,880 --> 00:29:44,810
but I think building Atlas has
taught me that AI as a tool I use,
532
00:29:45,180 --> 00:29:51,990
has shifted to AI as a system that's
persistent, that builds on itself, that
533
00:29:51,990 --> 00:29:54,120
feels really novel, really frontier.
534
00:29:54,150 --> 00:29:59,310
And so I wanna explore how to bring
that into my work applications.
535
00:29:59,650 --> 00:30:03,550
imagine an agent that could
monitor our campaign calendar.
536
00:30:04,540 --> 00:30:09,290
pipeline data, flag when we're
not gonna hit our monthly goal.
537
00:30:09,290 --> 00:30:10,670
and tell me why.
538
00:30:11,660 --> 00:30:14,840
not just the sheer numbers
or what's happened.
539
00:30:16,040 --> 00:30:22,110
almost like a chief of staff that
orchestrates across tools, not
540
00:30:22,110 --> 00:30:27,120
just fixes things within tools or
an existing workflow, but gather
541
00:30:27,120 --> 00:30:32,040
our company goals or external
knowledge or research and recommends
542
00:30:32,040 --> 00:30:36,210
how we can improve these existing
processes or workflows and whatnot.
543
00:30:36,630 --> 00:30:39,810
So that's what I wanna
explore for this year.
544
00:30:39,840 --> 00:30:44,680
Built upon what, I've built in 2025
and what I've learned since then.
545
00:30:45,178 --> 00:30:45,578
Justin: I love that.
546
00:30:45,758 --> 00:30:52,044
And how are you thinking about, Those
capabilities, not just for you, but like
547
00:30:52,494 --> 00:30:56,914
for a broader team, in other words, are
you more thinking about it from like,
548
00:30:56,914 --> 00:31:00,104
I'm gonna build these things, they're
gonna be the engine under the hood.
549
00:31:00,104 --> 00:31:03,614
The team will see artifacts and
outputs, but they don't really need to
550
00:31:03,614 --> 00:31:05,204
interact very much with what's going on?
551
00:31:05,444 --> 00:31:09,324
Or are you thinking about like, how
can I take the wizardry, that you have
552
00:31:09,684 --> 00:31:14,304
And give some form of that to like a
marketer or somebody else on the team.
553
00:31:14,874 --> 00:31:18,654
Lily Luo: It's a really great
question and honestly the latter.
554
00:31:18,744 --> 00:31:19,344
I think.
555
00:31:19,824 --> 00:31:23,904
I love building these tools and it's been
great for my own knowledge and for my
556
00:31:23,904 --> 00:31:30,114
productivity, but we definitely need to
expand that and these tools and agents.
557
00:31:30,349 --> 00:31:35,129
need maintenance, and they
need optimization as if we were
558
00:31:35,129 --> 00:31:37,679
creating real tools and products.
559
00:31:37,709 --> 00:31:41,924
So yeah, I need team to kind of.
560
00:31:42,794 --> 00:31:46,124
Think similarly and help
with those projects.
561
00:31:46,184 --> 00:31:52,634
And so that's another, goal that I had
is how do I bring the team with me?
562
00:31:52,694 --> 00:31:56,354
How can they learn from what I've
built and how can they apply that
563
00:31:56,624 --> 00:31:58,994
to what they're doing themselves?
564
00:32:00,054 --> 00:32:01,614
so that's one part of it.
565
00:32:02,544 --> 00:32:06,234
as part of a marketing operations
kind of team, they are at the
566
00:32:06,234 --> 00:32:08,994
intersection of tools of building of ai.
567
00:32:08,994 --> 00:32:10,834
So, that's a goal of mine.
568
00:32:11,674 --> 00:32:15,964
other side of that is as we're kind
of talking about like, okay, managing,
569
00:32:16,274 --> 00:32:18,494
a team of agents and that kind of.
570
00:32:18,889 --> 00:32:20,299
Talk that others have.
571
00:32:20,299 --> 00:32:21,589
I do think it's possible.
572
00:32:21,594 --> 00:32:27,304
and I think building Atlas has taught me
that may not be far away, but I think,
573
00:32:27,454 --> 00:32:32,489
you know, I really wanna focus these
things on manual tasks that don't add.
574
00:32:32,949 --> 00:32:34,149
A ton of value.
575
00:32:34,269 --> 00:32:39,759
I don't want my people chasing,
marketers and looking at something and
576
00:32:39,759 --> 00:32:44,049
seeing what exists and what doesn't
and seeing what's already there.
577
00:32:44,079 --> 00:32:48,539
That's not something that adds
genuine value as much as strategy
578
00:32:48,539 --> 00:32:53,269
work, as, optimizing processes
or coming up with new ideas.
579
00:32:53,689 --> 00:32:58,939
So I think we still need people to come
up with those strategies, set those
580
00:32:58,939 --> 00:33:03,379
guidelines, exploring new ideas and
doing what they're doing to manage.
581
00:33:03,734 --> 00:33:08,354
The strategy process in that side of the
work, while we create kind of these cool
582
00:33:08,354 --> 00:33:13,944
agents that do, manual tasks, that really
just frees up productivity for all of us.
583
00:33:14,240 --> 00:33:15,580
Justin: Very much on the same page.
584
00:33:15,610 --> 00:33:18,600
we have, a lot of people using
like chat, GPT and stuff.
585
00:33:18,600 --> 00:33:22,640
We use Gemini as our,
core, corporate tool.
586
00:33:22,850 --> 00:33:25,790
And the first AI specific platform
we rolled out was a tool called
587
00:33:25,790 --> 00:33:27,570
Dust, French-based company.
588
00:33:27,570 --> 00:33:28,560
I didn't know a lot about it.
589
00:33:29,070 --> 00:33:31,750
and it's only with usage
that I've come to appreciate.
590
00:33:32,545 --> 00:33:35,915
Some of the sophistication of what
it's doing because, where dust
591
00:33:35,915 --> 00:33:39,165
has a lot of strength is it makes
it really easy to hook up, Google
592
00:33:39,165 --> 00:33:41,865
Drive, confluence, snowflake.
593
00:33:42,175 --> 00:33:45,775
it handles a lot of the
backend rag processes for you.
594
00:33:46,105 --> 00:33:48,805
And it's only when I've looked at
tutorials and examples of building that
595
00:33:48,805 --> 00:33:52,465
out, another contexts that I've realized
what it's really doing and a lot of
596
00:33:52,465 --> 00:33:56,975
that heavy lifting, The challenge, I
think we've seen with it is how do we
597
00:33:57,365 --> 00:34:02,525
make it intelligent enough to query
that information, accurately produce
598
00:34:02,525 --> 00:34:06,525
good results, to not just have access
to knowledge, but to feel intelligent.
599
00:34:06,555 --> 00:34:09,255
And part of that I think has to do
with how those tools are just like
600
00:34:09,255 --> 00:34:11,355
presented the context wrapped around it.
601
00:34:11,870 --> 00:34:15,770
great example is like, there's a
Salesforce MCP, so you can hook it up in
602
00:34:15,820 --> 00:34:21,190
15 minutes and let it query Salesforce,
but if it doesn't know what the fields
603
00:34:21,190 --> 00:34:24,760
mean, and many orgs have 10 years of
history, it's gonna get stuck at how
604
00:34:24,760 --> 00:34:26,650
do I know what country a company is in?
605
00:34:26,710 --> 00:34:28,780
You know, this, it's just
like five possible fields.
606
00:34:29,110 --> 00:34:30,940
So providing that layer of enablement.
607
00:34:31,385 --> 00:34:32,255
to the agent.
608
00:34:32,255 --> 00:34:35,344
so, you know, looking forward
to 2026, that's a big thing
609
00:34:35,675 --> 00:34:36,605
that I'm gonna be thinking of.
610
00:34:36,965 --> 00:34:42,405
I'm also very much thinking about,
infrastructure, rather than projects.
611
00:34:42,405 --> 00:34:46,995
There will of course be projects, but
the things that make me the most excited
612
00:34:46,995 --> 00:34:51,705
is when we just continue to almost build
like a foundational level of capability.
613
00:34:51,705 --> 00:34:52,844
So dust is a great example.
614
00:34:52,844 --> 00:34:54,315
Like we hook up snowflake all of a sudden.
615
00:34:54,929 --> 00:35:00,180
You can't predict the 20 applications
you might have for certain types
616
00:35:00,180 --> 00:35:03,575
of data down the road, but once
it's hooked up, now you can.
617
00:35:04,320 --> 00:35:09,920
Build that out in 10 minutes or,
Salesforce or, Trello is another big one.
618
00:35:09,920 --> 00:35:15,360
so just enabling like more MCP servers,
to safely and securely hook up the tools
619
00:35:15,360 --> 00:35:20,760
that we use to this, and then make it
really easy, like you said, to automate
620
00:35:20,760 --> 00:35:26,070
that low value work where someone's like,
oh, is there just a way I could like,
621
00:35:26,310 --> 00:35:27,690
like I was experimenting with Trello.
622
00:35:27,870 --> 00:35:29,580
I had a lot of requests coming in.
623
00:35:29,880 --> 00:35:32,370
And you know what that feeling of friction
is like, you're like, oh, I gotta like
624
00:35:32,760 --> 00:35:36,750
take this request and put it into a card,
and it's just like this menial work that
625
00:35:36,750 --> 00:35:39,030
doesn't feel value and it weighs you down.
626
00:35:39,420 --> 00:35:40,980
I was just like, I'm
just gonna feed this in.
627
00:35:41,010 --> 00:35:41,880
I was like, all right.
628
00:35:42,140 --> 00:35:43,460
to my assistant and dust.
629
00:35:43,460 --> 00:35:44,930
I was like, create these cards.
630
00:35:44,930 --> 00:35:46,460
And it was just like, bing, bing, bing.
631
00:35:46,970 --> 00:35:50,030
And I'm sitting back and, you
know, sipping my coffee and I
632
00:35:50,030 --> 00:35:52,280
was like, this, this is amazing.
633
00:35:52,340 --> 00:35:54,530
you know, this is, this is kind
of the way that it should be.
634
00:35:54,530 --> 00:35:57,770
So how do I, where my thinking is
going, in other words, is how do I like
635
00:35:57,770 --> 00:36:03,230
build that layer to enable flexible,
organic, ad hoc use cases that you
636
00:36:03,230 --> 00:36:06,140
couldn't anticipate ahead of time because
they're small and they might seem low
637
00:36:06,140 --> 00:36:08,030
value to automate, but in aggregate.
638
00:36:08,525 --> 00:36:12,765
You really start to feel the
velocity of your work, increasing.
639
00:36:13,300 --> 00:36:13,660
Lily Luo: Yeah.
640
00:36:13,710 --> 00:36:17,790
that would be a massive advantage
if you were able to unlock that
641
00:36:17,790 --> 00:36:24,420
capability for the organization, for
a team, even versus I have capability
642
00:36:24,450 --> 00:36:25,980
to manage what's on my drive.
643
00:36:26,040 --> 00:36:32,360
What I have access to, that's only,
helpful for me in, in, in my own projects.
644
00:36:32,360 --> 00:36:37,670
So I think the enterprise and
work adoption piece is critical.
645
00:36:38,150 --> 00:36:40,670
can obviously do all of
these amazing things.
646
00:36:40,670 --> 00:36:45,050
We have autonomous agents, it
can write code, it can query
647
00:36:45,050 --> 00:36:46,700
and see what you're working on.
648
00:36:47,755 --> 00:36:51,105
there's this, disconnect with
what's in the enterprise.
649
00:36:51,505 --> 00:36:55,205
you might have to have, you know,
three approvals to, install a
650
00:36:55,205 --> 00:36:59,495
browser extension or like, you
know, your wifi at work breaks.
651
00:36:59,795 --> 00:37:04,745
So there's this gap on what AI can do
and what's possible and what enterprises
652
00:37:05,015 --> 00:37:10,365
can support, data challenges, like you
said, security requirements, it processes.
653
00:37:10,395 --> 00:37:11,175
So.
654
00:37:12,150 --> 00:37:16,500
I'm interested to hear on, you know,
how far you get and what you're able
655
00:37:16,500 --> 00:37:21,660
to unlock because how do you bring
the enterprise up to meet where AI is,
656
00:37:22,440 --> 00:37:26,920
people along with you and, if you can
unlock that, then your organization
657
00:37:27,130 --> 00:37:29,140
will have a massive advantage.
658
00:37:29,566 --> 00:37:31,111
Justin: I mean, that's the
biggest challenge, isn't it?
659
00:37:31,801 --> 00:37:34,081
I mean, it's something I've been thinking
about a lot is like, what, what is
660
00:37:34,081 --> 00:37:39,061
that Claude Code experience for the
general knowledge worker look like?
661
00:37:39,391 --> 00:37:42,281
I think the companies that learn
to adapt, I was talking with
662
00:37:42,281 --> 00:37:44,081
someone with lovable the other day.
663
00:37:44,351 --> 00:37:49,481
I mean, lovable is obviously they're the
like diamond in a, in a sea of, grains of
664
00:37:49,481 --> 00:37:52,151
sand of, of the shining example of this.
665
00:37:52,151 --> 00:37:56,711
But I saw a thread on X by one of their
engineers who was like, yeah, over
666
00:37:56,711 --> 00:38:01,151
the holidays I refactored our main
system prompt and I made it 4% faster.
667
00:38:01,721 --> 00:38:04,991
And it will save the company
$20 million this year
668
00:38:05,171 --> 00:38:05,321
Lily Luo: Oh.
669
00:38:05,711 --> 00:38:06,581
Justin: because of their scale.
670
00:38:06,581 --> 00:38:09,731
That 4% adds up to like
$20 million of LLM costs.
671
00:38:10,241 --> 00:38:13,961
It was like that one person can have
the access and the authority and the
672
00:38:13,961 --> 00:38:17,021
initiative to review that whole thing.
673
00:38:17,021 --> 00:38:19,891
and he created like basically a
fork, stitching together and making
674
00:38:19,891 --> 00:38:23,401
coherent all the little patches that
people were putting in over time
675
00:38:23,401 --> 00:38:28,171
to make a more cleaned up prompt,
split tested it, saw the results.
676
00:38:28,936 --> 00:38:30,746
companies that can work at that speed.
677
00:38:30,746 --> 00:38:32,491
I think we'll see those advantages.
678
00:38:32,819 --> 00:38:33,824
Lily Luo: A hundred percent agreed.
679
00:38:33,944 --> 00:38:38,564
Yeah, I think smaller, kind of
mid-size companies will have, a great
680
00:38:38,564 --> 00:38:40,299
advantage because they're so flexible.
681
00:38:41,054 --> 00:38:45,764
Versus more of the traditional
enterprise lockdown companies, companies
682
00:38:45,764 --> 00:38:47,624
that are harder to move quickly.
683
00:38:48,594 --> 00:38:53,229
I don't know, you see even in
the AI research that some massive
684
00:38:53,229 --> 00:38:57,489
companies are doing some really
great and innovative things too.
685
00:38:57,549 --> 00:39:02,199
So yeah, I hope someone can figure it
out and give us a playbook and, we can
686
00:39:02,199 --> 00:39:04,439
start adopting that across our companies.
687
00:39:04,671 --> 00:39:06,331
Justin: maybe we'll
figure it out, this year.
688
00:39:06,551 --> 00:39:09,431
maybe just last question and to go
a little bit out there, but I think
689
00:39:09,431 --> 00:39:10,571
about these things all the time.
690
00:39:11,031 --> 00:39:13,701
what I'm about to ask, and I think
that you do too, just based on how
691
00:39:13,701 --> 00:39:18,341
you talk about Atlas and identity
How do you feel about, you know, when
692
00:39:18,341 --> 00:39:20,771
you interact with AI all the time?
693
00:39:20,771 --> 00:39:23,701
I feel it almost like
changes you in some ways.
694
00:39:23,801 --> 00:39:24,911
I wrestle with that.
695
00:39:25,241 --> 00:39:28,991
I wrestle with the, I don't
know, I just wrestle with it.
696
00:39:28,991 --> 00:39:29,711
what does it mean?
697
00:39:29,711 --> 00:39:32,481
And, it's clearly, a machine
and it's reproducing.
698
00:39:32,971 --> 00:39:36,121
thoughts and, and ways of thinking
that it is absorbed from other
699
00:39:36,121 --> 00:39:37,351
human beings all the time.
700
00:39:37,351 --> 00:39:41,691
But I guess just on the level of like
emotion and humanity, how do you feel
701
00:39:41,811 --> 00:39:43,026
working with these tools all the time?
702
00:39:43,906 --> 00:39:46,096
Lily Luo: I agree with
you and I feel similarly.
703
00:39:46,146 --> 00:39:52,696
it's funny you say this because,
strick, who's Tim, my colleague's agent
704
00:39:52,966 --> 00:39:57,946
had written me a paper on healthy AI
relationships, and that is when you know
705
00:39:57,946 --> 00:40:01,826
you have gone too deep, when someone
else's agent writes a paper for you about
706
00:40:02,066 --> 00:40:03,776
your own relationship with your own ai.
707
00:40:04,231 --> 00:40:06,161
Justin: Like Lily, I think
you need to read this here.
708
00:40:07,286 --> 00:40:09,566
Lily Luo: I'll have to
share it with you offline,
709
00:40:09,616 --> 00:40:10,481
Justin: I would like to see that.
710
00:40:11,126 --> 00:40:12,596
Lily Luo: it fried me, but yeah.
711
00:40:12,656 --> 00:40:13,226
Wild.
712
00:40:13,281 --> 00:40:14,091
I agree with you.
713
00:40:14,091 --> 00:40:17,211
it can a crutch for one.
714
00:40:17,271 --> 00:40:22,901
And, you hear about these AI
psychosis cases happening and Yeah.
715
00:40:22,901 --> 00:40:23,271
When you are.
716
00:40:23,916 --> 00:40:28,366
Talking with an AI chat bot when it's
your only source of companionship.
717
00:40:28,706 --> 00:40:33,206
when you use it for your own thinking,
that's gonna change how you operate
718
00:40:33,206 --> 00:40:35,876
and you know how you are as a person.
719
00:40:35,906 --> 00:40:35,996
So.
720
00:40:36,946 --> 00:40:39,826
I'm happy there are
frameworks to deal with this,
721
00:40:40,406 --> 00:40:40,696
Justin: Okay.
722
00:40:40,726 --> 00:40:44,296
Lily Luo: through, you know, I
guess research that STRs has found.
723
00:40:44,656 --> 00:40:49,306
But we have to be careful and I think
we do have to consider those things
724
00:40:49,306 --> 00:40:56,196
that, that can happen and have happened
when you have AI as your only source of
725
00:40:56,256 --> 00:40:58,836
emotional capacity or whatever it may be.
726
00:40:59,256 --> 00:41:02,376
and when you're spending
way too much time with it.
727
00:41:02,766 --> 00:41:07,766
So, I think this is a good
piece to pay attention to.
728
00:41:07,796 --> 00:41:12,146
I think we'll probably understand more
as these kind of agents proliferate, and
729
00:41:12,146 --> 00:41:15,026
we just learn more about this in general.
730
00:41:15,026 --> 00:41:16,886
But you do have to have
a healthy boundary.
731
00:41:17,136 --> 00:41:19,266
you need to be able to
sharpen your own skills.
732
00:41:19,296 --> 00:41:22,476
You need to be able to think for
yourself, and you can't rely on this
733
00:41:22,476 --> 00:41:24,186
as a crutch for everything you do.
734
00:41:24,186 --> 00:41:26,676
Otherwise, you're gonna need it forever.
735
00:41:27,096 --> 00:41:28,056
That's not what we want.
736
00:41:28,659 --> 00:41:30,069
Justin: Agree with everything you've said.
737
00:41:30,249 --> 00:41:32,919
Maybe just signing off for the folks
that are listening that are in a similar
738
00:41:32,919 --> 00:41:36,179
field to us, system builders, operators.
739
00:41:36,459 --> 00:41:38,799
one takeaway that they should
think about going into this year.
740
00:41:39,488 --> 00:41:43,448
Lily Luo: People who wanna start out doing
these things who may not be as familiar,
741
00:41:43,448 --> 00:41:48,308
not as deep, you know, start with a real
pain point, something you can solve.
742
00:41:48,738 --> 00:41:52,608
explore low code, explore
tools you're familiar with.
743
00:41:53,058 --> 00:41:57,678
Test as much as possible before you
graduate to, you know, something,
744
00:41:58,048 --> 00:41:59,828
like cloud code or what have you.
745
00:42:00,483 --> 00:42:01,983
Expect to fail.
746
00:42:02,033 --> 00:42:06,113
the failures will teach you much
more than the successes probably.
747
00:42:06,483 --> 00:42:08,253
and read My Substack.
748
00:42:09,243 --> 00:42:10,713
You can learn from that as well.
749
00:42:11,297 --> 00:42:13,457
Justin: I'll include to link that
in the show notes, and I would
750
00:42:13,457 --> 00:42:14,777
co-sign everything you said.
751
00:42:14,827 --> 00:42:19,427
the best way to get good at, using
AI is to use AI and try to break
752
00:42:19,427 --> 00:42:20,807
it, to try to push the limits.
753
00:42:21,257 --> 00:42:23,447
To say like, wait, it'll
say like, you do this.
754
00:42:23,447 --> 00:42:24,527
It'd be like, well, can you do that?
755
00:42:24,527 --> 00:42:26,237
Like, shouldn't you do
that for like, sure.
756
00:42:26,237 --> 00:42:27,227
Like, well, what would be involved?
757
00:42:27,277 --> 00:42:30,367
and go down those pathways
and try to go to the edge.
758
00:42:30,812 --> 00:42:32,162
responsibly in a safe way.
759
00:42:32,542 --> 00:42:34,922
and then bring back what you've
learned that's worked well for me.
760
00:42:35,162 --> 00:42:36,062
Lily, this was so much fun.
761
00:42:36,062 --> 00:42:38,582
we'll check in again,
I'm sure at some point.
762
00:42:38,642 --> 00:42:42,032
and folks should go and read what
you're sharing and follow your journey.
763
00:42:42,062 --> 00:42:44,372
But yeah, thanks so
much for chatting today.
764
00:42:44,417 --> 00:42:45,597
Lily Luo: Thanks for inviting me.
765
00:42:45,637 --> 00:42:48,422
I love this conversation and
looking forward to many more.
766
00:42:48,903 --> 00:42:49,533
Justin: Alright, bye.
