S1: Hey what's up? So I think the way we're thinking
S1: about harnesses is not correct. I think it was the
S1: right way to think about them before basically as like
S1: coding systems, like coding assistance and like a set of
S1: tools and stuff to do coding better and to release
S1: apps faster. I think we have to start thinking about
S1: this completely differently. The way I imagine a harness to be,
S1: the way I think about harnesses is kind of like
S1: a content management system. Okay. The problem that we're trying
S1: to solve, actually, is that it's not exactly clear what
S1: is in our brains as a kind of a feeling
S1: of what we want. And it's not easy even within
S1: our own brains to like, translate that feeling into actual words, right?
S1: Then we have to actually capture it properly, which like
S1: has been known as like prompt engineering, right? So we
S1: have a prompt engineering problem, but before that, we have
S1: a feeling in our brain problem and how that's going
S1: to translate into actual text, which we can then capture
S1: into a prompt and then which we can then give
S1: to an AI and hopefully it understands. Hopefully it's smart
S1: enough to understand the prompt, which is the whole purpose
S1: of prompt engineering. Well, I think we've confused this whole
S1: harness idea into thinking that it has to do with coding,
S1: thinking that it has to do with basically, what are
S1: the best tools we can give? What are the best
S1: prompts that we can write? What are the best like
S1: specs and prds and all of that plans, all of
S1: that stuff. Which is all true, but I think it
S1: makes way more sense. And this is the way I've
S1: been building our system called Pi, which is now being
S1: renamed to life OS. So if you see life OS,
S1: that's actually Pi. And the model that we use with
S1: life OS is essentially to have your harness be essentially
S1: your life. Your AI harness is everything that you're trying
S1: to accomplish. It's all of your intents. It's your current state,
S1: it's your desired state. It's what you're actually trying to
S1: achieve in life, right? So it's your personal goals, it's
S1: your work goals. It's your, you know, coding projects and
S1: your apps and your desires and your preferences and your challenges.
S1: Now you might think, well, okay, but that's life stuff.
S1: And over here we have coding stuff. But I think
S1: if you think about this in the way that I'm describing,
S1: you'll notice that this all blurs, this all disappears, because
S1: what we're trying to do is get to a point
S1: where we can say just the smallest thing, okay? We
S1: could say, imagine you had a twin sister or a
S1: twin brother, and they were like synced with you. You
S1: could say to them, and this is like a really
S1: important point. You could say to that person, you could say, hey,
S1: do you think this works for us? And they could
S1: just like glance at, say, an event schedule, or they
S1: could glance at like a new vehicle or they could
S1: glance at a job description. When I say, hey, do
S1: you think this will work for us? I have communicated volumes. Okay.
S1: This this is the whole magic of a harness. Okay.
S1: It's it's context engineering. It's prompt engineering. It's a whole
S1: bunch of other things that we've been talking about for years.
S1: But it's different, okay? It's different. It is compression. It
S1: is efficiency of going from a brain thought or a
S1: feeling all the way through the translation layers into the
S1: prompt engineering, all the way to the AI. The goal
S1: is to be able to say as little as possible
S1: to achieve the best possible context transfer and intent transfer
S1: and intent articulation into your AI. That is the purpose
S1: of the harness because it doesn't matter what you're talking about.
S1: It doesn't matter if you say, hey, draw me a
S1: butterfly wing. That is beautiful. Okay, that is very abstract.
S1: It's very general, it's very non-technical. But there are questions there.
S1: What do you mean, a butterfly wing? Like, how big
S1: should it be? Like 1 or 2, you know, should
S1: I use watercolors? Like, what is a pretty butterfly wing?
S1: According to you write this. These questions need to be answered.
S1: And if you have that in your harness, because guess what?
S1: You're a famous artist known for drawing butterfly wings. Well,
S1: hopefully you wouldn't be using an AI to draw your
S1: wing for you if that were the case. Let's just
S1: say you're a famous AI artist and you put out
S1: different animals or whatever. Whatever it is, you should have
S1: so much context inside of your harness of what great
S1: art looks like, what great AI art in this case
S1: looks like. I don't like this example, but we'll just
S1: go with it and essentially like, what are all the pieces?
S1: What does it mean to have a good one? What
S1: does it mean to have a pretty one? What do
S1: those even mean? How do you make that testable? How
S1: do you make that something that the AI that your
S1: AI harness can actually hill climb on to achieve a
S1: positive result, right? And this is where this whole concept
S1: of like bitter pill engineering comes into play. There's multiple
S1: things that are built into life OS that are oriented
S1: around rethinking what a harness is. So one of them
S1: is the concept of ideal state. We're trying to transition
S1: from current state to ideal state. In this case, the
S1: current state is I don't have a pretty butterfly wing.
S1: The ideal state is I do have a pretty butterfly wing.
S1: So you're trying to, you know, you the AI or
S1: trying to transition me from current to ideal state, right?
S1: That's the same thing. If you're building some complex application,
S1: you're trying to start a new business and you need
S1: all the paperwork. It's all about current to ideal state.
S1: The second piece of that is the document itself. That
S1: has to do with pursuing the ideal state. When we
S1: build something inside of life OS, we're actually building an
S1: ideal state artifact. That ideal state artifact is a generalized
S1: version of all the stuff you've heard about before with,
S1: you know, specs and prds and plans and, you know,
S1: prompts and all of that. This is a single unified
S1: document that captures the kind of the journal say like
S1: the what the person wants, what they say they want
S1: that's captured changes to that are captured in like a changelog.
S1: It's broken out into features and like approaches and stuff
S1: like that, which the AI comes up with for you.
S1: And then if you make adjustments to that, it's modified.
S1: There's an HTML version, there's a markdown version. And basically
S1: this is the format that we use to hill climb.
S1: So we are giving a goal to our system to
S1: hill climb using this ideal state artifact, which is in
S1: my opinion, like the future of like a PRD. Think
S1: of it as a universal PRD for any task. And
S1: that is fundamentally what life OS is based on, right?
S1: Is this concept of pursuing the ideal. So really what
S1: we're talking about here is a system that can universally
S1: pursue ideal for you, right? We are not code generators.
S1: We are not app builders, right? Not fundamentally. It's a
S1: task that we are doing. Why are we doing that?
S1: Why are we building apps? How much money do we
S1: need to make? Are we trying to transition out of
S1: our corporate job to have, you know, work for ourselves? Well,
S1: then that's the goal, right? So how much monthly recurring
S1: revenue are we making? Are we doing a good job?
S1: Are people enjoying this? These are questions that we want
S1: to ask, and I shouldn't have to say and set
S1: up every single question that I ask to the AI,
S1: or set up every single app that I want to
S1: build with the AI to describe the stack and describe
S1: the goal that I have and describe all of that. No.
S1: That is the purpose of the harness. Have all of
S1: that already built in? It's all in your context. It's
S1: in your various context files, it's in your various tools,
S1: it's in your hooks, it's in your prompts, it's in everything.
S1: It's already pre-built. It's already pre-baked into your harness itself.
S1: So you could say something like, hey, look, is this
S1: a good system? I heard there's a new memory system
S1: out for cloud code. Is this as good as ours?
S1: I literally just do this. In fact, I'm going to
S1: show you. I'm just going to go over here to
S1: my system. Hey, what do we think about this thing?
S1: This is Gary Tan, a friend of mine, cool guy.
S1: And basically we talk about a bunch of harness stuff,
S1: but he has like this Google brain thing that 24,000 stars.
S1: A lot of people are using this. It's like a
S1: got recipes. It's basically a whole harness, right? With memory
S1: and stuff like that. So my question is I want
S1: to look at this thing and say, hey, how good
S1: is this? Like, can we use it or whatever? Now,
S1: if I have a generic harness with not much in it, right.
S1: Something that's not like life OS. Well, I'm going to
S1: have to explain. Hey there. You know, there are memory systems.
S1: We have a memory system. This is a memory system.
S1: I'm trying to figure out how good this is compared
S1: to ours. I want you to look across all these
S1: different criteria, blah, blah, blah. Right. That's a whole bunch
S1: of stuff that is intent engineering, right? That is prompt engineering.
S1: It is context engineering. It's like all these names that
S1: we've been using. It's all the same thing. It's us
S1: trying to get our ideal result right. We have a
S1: feeling in our brain of what we mean when we
S1: give this to our AI. What we really mean is
S1: how good is this? Should we use it? But in
S1: order to give a proper prompt for that, you could
S1: actually spend half an hour or an hour or two
S1: hours writing a great prompt and you would get better results.
S1: The goal of a harness, in my opinion, is to
S1: be able to do that automatically with as few words
S1: as possible. Right? All right. So what I'm going to
S1: do is I'm just going to grab this URL here. Okay,
S1: so I got that copied, and I'm going to come
S1: over here and I'm going to open a new session
S1: right here ready to go. Kai says hello and we're
S1: going to say, hey, take a look at this and
S1: see if we should use any part of it.
S2: Evaluating G brain repo.
S1: All right. So Kai is going to head out and
S1: take a look at this thing. And what's extraordinary about
S1: what Kai is about to do is what did I say?
S1: I said, hey, take a look at this and see
S1: if we should use any part of it. I didn't
S1: explain myself. I didn't write a prompt that took me
S1: two hours. Kai already knows when it sees the project,
S1: it knows okay about itself. Look at these context files.
S1: I've included my skills, my life OS system prompt projects,
S1: my identity, Kai's identity architecture summary. That's our architecture, life
S1: OS all that stuff all unified together so that Kai
S1: understands what I mean when I say very simple sentences.
S1: What we just basically did was we cheated. We basically
S1: cheated and injected.
S2: Skip the DB, steal gap analysis and hybrid retrieval, both testable.
S2: Want me to prototype the gap layer.
S1: Look at that. Look at that. Look at how much
S1: it looked at that quickly. Okay. And this is all
S1: because it has tons of context built in. I'm already
S1: at 13%. And people will say, well, if you have
S1: a harness, you're doing all this context injection, you're wasting tokens. No,
S1: you're spending tokens. You're not wasting tokens. You're doing quite
S1: the opposite. And tons of people who use Pi OS,
S1: the new name of it basically will tell you, yes,
S1: sometimes it takes a little bit longer. That didn't take
S1: long at all. That took one minute, right? 20s. But
S1: it can take a little bit longer. But you're saving
S1: spending five times more time in tokens, doing rework. I
S1: don't have to do rework very often here, and when
S1: I do, I just rebuild that into the harness so
S1: I don't ever have to do it in the future.
S1: So I really encourage you to think about a harness
S1: in this way as a much larger, zoomed out version
S1: of life management, which is why we just renamed the
S1: Pi basically to life OS, because this is about managing
S1: what you are trying to do in your life and
S1: what you're trying to do in your work overall, what
S1: you're trying to accomplish, what is your ideal state so
S1: you can get your AI harness to transition from current
S1: state to ideal state. That is the whole purpose. And
S1: inside of that, contained inside of that is this concept
S1: of intent engineering. Okay. It's intent engineering, sitting on top
S1: of context engineering, sitting on top of prompt engineering. And
S1: ultimately it comes down to the clearest possible thinking, right?
S1: The clearest possible articulation of what you're trying to accomplish,
S1: which is why we have the algorithm, which is why
S1: we have the Issa document, ideal state artifact document. We
S1: are hill climbing towards what we're trying to accomplish, whether
S1: it's drawing a butterfly wing or building a brand new
S1: application or like improving our relationships, whatever it is, think
S1: at that larger scale. Think of your AI harness as
S1: life management, essentially, right? And it doesn't matter what platform
S1: you use, doesn't matter if you use our open source
S1: thing or you're going to use some new thing that's
S1: going to come out from anthropic or OpenAI, it doesn't matter.
S1: So I just want to prompt you to think about
S1: harnesses in this much larger way, not AI harnesses, but
S1: life OS slash life management slash intent engineering. And we'll
S1: see you in the next one.