Building AI Agents (Clearly Explained)

I sit down with Ras Mic to break down how AI agents actually work and why most people are using them wrong. Ras Mic explains the mechanics of context windows, makes the case that agent md files are largely unnecessary, and shares his step-by-step methodology for building custom skills that make agents dramatically more productive. Whether you're coding with Claude Code or automating workflows with OpenClaw, this episode gives you the foundational knowledge to stop wasting tokens and start getting real results from your AI tools. Timestamps 00:00 – Intro 00:42 – The Models Are Good Now 01:20 – How Context Windows Actually Work 04:55 – The Power of Skills 09:17 – How to create Skills 16:35 – Skill Maxxing 19:05 – What you need too build a project 20:40 – Recursively Building and Improving Skills 29:23 – Context Window Management and Token Efficiency 33:02 – Closing Thoughts Key Points The models (Opus 4.6, GPT 5.4) are exceptionally good now — the differentiator is the context and harness you build around them. Agent md and claude md files get loaded into context on every single turn, burning tokens and degrading performance as the context window fills up. 95% of users can skip them entirely. Skills use progressive disclosure: only the name and description sit in context until the agent determines it needs the full file, saving thousands of tokens per conversation. The best way to create a skill is to walk through the workflow with the agent step by step, achieve a successful run, and then have the agent write the skill based on that real context. Recursively refine skills by feeding failures back into the agent and having it update the skill file so the same mistake is avoided going forward. Scale for productivity by starting with one agent and building up workflows before adding sub-agents — start simple, then expand. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND MIC ON SOCIAL X/Twitter: https://x.com/Rasmic Youtube: https://www.youtube.com/@rasmic

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Greg Isenberg?

  Ross, Mike, welcome back to the pod. By the end of this episode, what are people gonna learn?

• 0:04Speaker 01

  I hope I'm gonna share some wisdom on how you can use the agents better. There's a lot of information going on right now. I disagree with most of it, and that's what we're gonna talk about. So at the end, whether you're building something, using an agent for some sort of work, you have the best output possible.

• 0:20Greg Isenberg?

  And is this gonna be a technical dive, or, you know, a non-technical person can?

• 0:25Speaker 01

  Anyone can watch this. There's gonna be a lot of diagrams. That's all.

• 0:29Greg Isenberg?

  You're gonna make it clear to understand the concepts, right?

• 0:31Speaker 01

  Easy.

• 0:32Greg Isenberg?

  Okay.

• 0:32Speaker 01

  Basics.

• 0:33Greg Isenberg?

  Let's go.

• 0:33Speaker 01

  So.

• 0:42Speaker 01

  The first thing that I want to announce, previous episodes, we probably disagree with this point, but now, what's true is the models are good. The models are exceptionally good. Opus 4.6 is amazing. GPT-5.4 is amazing. I know there's like two sets of camp where, especially when it comes to programming, people are like, oh, Opus is the better UI designer. GPT-5.4 is a better backend. Generally speaking, we've reached a point, we're not at AGI yet, where we've reached a point where the models are good. But context still matters, and you have the power to steer the models in a direction where you can get quality, or you can get slop. And that's what I really want to talk about. But before we get into all that, and feel free to cut me off, because this topic excites me.

• 1:25Speaker 01

  We need to learn how context works. And context is the model assembling information that it needs to execute an action. And the way the context is assembled, let's say in a coding agent, but really in any sort of agent, is there's this general system prompt, usually by the model provider. So for example, Claude code leaked recently, and one of the cool things that, especially as a developer, I got to do is I got to read the system prompt. So they have this general system prompt that guides the model on how to act, what to do, what not to do. The system prompt is very important. And then you have, a lot of people have agent.md files or claude.md files. Now, I'm just gonna say off rip,

• 2:04Speaker 01

  95% of people don't need this. The reason being is, again, you have to assume that the models are already good, right? Now, imagine I told you, Greg, every time we're about to shoot a podcast, Greg, you need a microphone.

• 2:19Speaker 01

  You know you need a microphone, right? You've done this plenty of times, right? So if I'm building, like, let's say, a website with Claude code, and I'm telling Claude code, "This code base uses React", I don't need to, because it has the code base in context. It can check the code, right? So there is this disparity where a lot of people are putting a lot of onus on the harness and the context building, and I'm low-key starting to strip things off. Like, I'm going super, super minimal, because, again, not to sound like an Anthropic or OpenAI shill. Unfortunately, I have not been acquired. None of them are paying me. But the models are really, really good.

• 2:57Greg Isenberg?

  Wait, so 95% of the time, I don't even need to bother with an agent.md file?

• 3:02Speaker 01

  You don't. Like, unless this is some sort of proprietary information.

• 3:05Greg Isenberg?

  Yeah, what is the 5% of the time I should care about it?

• 3:08Speaker 01

  Proprietary information that, like, may be specific to your company or some methodology that is specific to you that has to be referenced in every single conversation. Because the annoying part with an agent.md file is every time you go back and forth with the agent, it's added in the context, right? The cool thing about skills, and I'm going to talk about skills in a second, the way skills are designed, the skills are used in a way that's called progressive disclosure. Meaning, when you have a skill file, the entire thing isn't added to context. It's just the title and the description. So the agent has the title and description in the context. And when you, let's say, you have a Notion report skill, right? And you tell your agent, "Hey, I want you to create a Notion report",

• 3:51Speaker 01

  it's then going to check its context and be like, "Oh, I have this skill. Let me check out the entire document." So it's not in the context. What's in the context is the name and the description, but that's enough for the agent to be like, "Oh, this is a skill I need. Let me go use it", which is fantastic. I'm a skills maxi. And I'm going to show later in the episode, like, how you craft the perfect skills. But with agent.md and Claude.md files, it's context being added at every turn, right? So let's say you have, like, a thousand line file, Claude.md. And let's say that's like 7,000 tokens. You're spending 7,000 tokens on every Run now, do you need to? Most likely not. It probably should be a skill. But if you have some sort of company proprietary information,

• 4:37Speaker 01

  or like there's something specific that you do that the model needs to know at every single turn, then you use it. The thing is 95% of people don't have that, right? So I'm not a fan unless that's the case. So and and the reason being is we're wasting tokens, right? It's in every single turn. But this is where the beauty of skills come. Um, I'll show my screen here. The your skill, again, this is not like word for word how it looks, but a skill basically looks like this. There is a name, there is a description, and then underneath is a bunch of information. I'm going to put bunch of info. What, when you create a skill dot M D file,

• 5:19Speaker 01

  what gets added into the context is actually just the name and the description, right? The bunch of info doesn't get added. So imagine you have two sentences versus an agent on M D that has like a thousand lines that get added into the context. We're talking thousands of tokens compared to a couple hundred. And the agent only gets the bunch of info when it realizes it needs this skill. So if I have, let's say, a certain way of generating a report, a certain way of structuring my code, why would I put that in the agent dot M D file? When I can have the agent call on it progressively when it needs it, right? So this is why skills are honestly like I'm a shill, I'm a maxi, but people do it wrong.

• 6:03Speaker 01

  And I'm going to share the right way on how do we create skills? So so far we have the system prompt, the agent dot M D, the skills, and then we have the tools, right? So if you're using Claude code, there's already built in tools, a read tool or write tool. Like there's many tools that it uses. This has to be added into the context because the model, the model doesn't call the tools. It like it's the agent harness around it that allows it to call the tools. And then in this case, we also have our code base, right? Like whatever, if we're building a web app, a mobile app. I know most people here won't care for the specific framework. And honestly, we're getting to a point if you're not technical, you really shouldn't. Um, and then we have the user conversation. So this is what. The complete context window is filled with, right?

• 6:45Speaker 01

  And this can total up to, let's say, like, at the beginning, this could be like 20,000 tokens. And as the conversation continues to grow, you might reach your limit of 25, 250,000 tokens. And that's when you see both Claude Code and OpenAI Codex, they'll compact, right? So beautiful so far, right? This is how context works. Why skills are important and how you should generate skills. Let's say I have a specific workflow. For example, for my YouTube channel, you know, we're at a point right now, Greg, where we get sponsors now. Crazy. When I first joined, not when I first came to the pod, not a thing. We get sponsors now.

• 7:22Greg Isenberg?

  It was just your mom sponsoring the channel.

• 7:24Speaker 01

  Yeah, it was just her showing love, feeding me. But now we get sponsors. I get a lot of emails and some are good, some are bad. And it's a lot of time, I'm sure you're aware, to comb through and to check. So I have an OpenClaw agent that has its own email, right? I don't have it, I haven't given it access to my email. Because there's like attack vectors and I've been hacked before, so I'm very careful with these things. But it has its own email. And every time I get an email from like a sponsor, I forward that email to the agent. Now, the first time, I told my OpenClaw agent, I'm going to forward you emails. Check every 15 minutes when you have an email. And when you check the email, do research on a sponsor and tell me if they're worth it. That's all I told the agent.

• 8:07Speaker 01

  Every sponsor email I sent it, it was like legit, legit, legit, perfect, perfect, perfect. There was no like, there was no rejection. There's no, this is bad, or these guys are a scam, or this product's not good. Like, there was no deep research being done by it. So then I realized, huh, okay, the model needs a step-by-step guide. This is when I create a skill. But here's the problem. A lot of people will, I'll just write it down here, will identify, identify, they have a workflow. Right? You have some sort of workflow. And then they'll jump to create the skill right away. This is the, let me click hide here. This is the worst thing you can do. I'm just going to draw arrows to signify that this is bad. You don't do these.

• 8:52Speaker 01

  And the reason why you don't do this is, imagine... You hire an employee or you're mentoring somebody. Correct me if I'm wrong. You're probably going to tell them what to do. And if they ask you questions on how to do it, you'll help them. You would ideally like them to fail. And then you want to then tell them, no, this is how you do it. Like, there needs to be some sort of experiential learning. The way I've been creating skills, Greg, and I have like 100% hit rate now when I tell my agent to do something specific, is I actually walk with it step by step on doing the workflow. So in the case of my YouTube analysis, I told the agent, okay, I just sent you an email. Tell me about the company. Companies this, this, that, and that.

• 9:37Speaker 01

  Okay, check their Twitter, check their YouTube, check their TrustPilot, check if they've raised any money. If two of these are, have not, if two of these don't exist or not in good standing, automatic rejection. It checked and it was like, you're absolutely right. I was using Opus. These, this is not a good company. And then it would just, we would, we have a spreadsheet in Google Sheets. It'd be like, no contact.

• 9:59Greg Isenberg?

  It's so frustrating too, right? Cause you're like, you give it a task and it seems like so binary, like right or wrong. And then when you tell it, hey, like, why didn't you look at the TrustPilot? Why didn't you see if they've raised money? You're absolutely right.

• 10:14Speaker 01

  Yeah, absolutely.

• 10:15Greg Isenberg?

  It's like, what?

• 10:17Speaker 01

  And, and the thing is, the reason why this is the case is, the models actually don't think. They're predictors of tokens, right? So when you give it English, when I give it English, it maps it on this vector graph, and then it looks for the closest resemblance and it says, this is the response, right? So when you say, what is the capital of France? It maps it again on this graph and it says, oh, Paris is pretty close, right? Then it gives you Paris. It has no, it doesn't think, it doesn't understand. It feels like it understands, it feels like it thinks. Heck, it even feels like it has emotion. That's because it's been trained on so much data, but it actually does not know how to think. And this is where a lot of people will be frustrated with, like, why is it not understanding me? You have to walk with it. So I told it, okay, this is how you research. And it's like, okay, it researches. And guess what?

• 11:02Speaker 01

  This is part of the context. And they're like, okay, now that you're done researching, when it's a good company, these are the qualities you look for. And then when it's really good, send me an email. And then, once we had a successful run and we did it again and again, then I converted it to a skill. The reason being is a lot of people create the skills themselves, or I mean, they'll use the AI to create the skill, but it doesn't have the context on what a successful run looks like. Right? Because most of the times, especially if you're using OpenClaw, it's probably going to fail at the API call. It's probably going to call the data wrong. Like, there's so many places it's going to get wrong. And I see a lot of people saying, "It's just so frustrating. This is terrible technology. Why doesn't it work?" It's because you don't understand how an agent works, right? It will mimic you perfectly, but you've given it nothing to mimic, right?

• 11:46Speaker 01

  So, I will do the workflow myself. So, the updated version is identify the workflow, go back and forth and teach it. So, like, I'm doing it, like, I'll be like, "Okay, first, do the research. Here's the result." And I'm like, "What do you think about this?" "Oh, these guys are terrible. You're absolutely right." "Okay, you should go to the Google Sheet and mark this as bad company." I've done that. Once I've had that back and forth, then I tell the AI,

• 12:17Speaker 01

  "Review what you did and then create the skill." So, now it has actual context with how it worked, and it's going to create the skill beautifully. I don't handwrite skills. I don't think you need to. You can use AI to do it. They even have a skill to create skills, Skill perception. But you should have the context of what a successful run looks like. And this is why, by the way, Greg, I don't install skills. Like, I've seen people like, "Oh, this Notion skill, this social media skill, whatever." I'll review it. I'll check it out. I'll even give it to my AI and be like, "Oh, what are some things we can learn from this?" But I don't download skills because your agent needs the context of a successful run, which you then turn to skills, right?

• 12:58Speaker 01

  And this is the big thing. I see you see skills marketplaces. You see, download this and that. First of all, it's an easy way to attack somebody. So, I would be very, very careful with downloading some random person's skills. But second of all, again, it's all about context, right? It's all about, and you know, OpenClaw has a memory layer and all these type of things. You want it to do the right thing, and the only way it can do the right thing is if you give it the proper context. And to me, the best way to create a skill is to work with it in your specific workflow. Once you have a successful run, tell it, "Okay, review what you just did. This is the skill you need to create." I'll pause here.

• 13:37Greg Isenberg?

  I mean, it makes sense, right? Because... If you hired an employee, you would do the same thing.

• 13:43Speaker 01

  Yeah.

• 13:44Greg Isenberg?

  You wouldn't, you wouldn't just be like, okay, go do this thing. Good luck.

• 13:47Speaker 01

  Yeah.

• 13:48Greg Isenberg?

  Uh, and by the way, this is how you're gonna go do things forever. You would map out a workflow, you would identify what right and wrong is. You would, uh, do it iteratively. And then, once you've gotten to that point, you would codify it.

• 14:03Speaker 01

  A hundred percent. And I think like, that's the thing, like, we should treat models and these agents like very new employees versus like these black magic boxes that like know everything, right? They know everything because they've been trained on a lot of data, but they don't know your workflow, your steps, right? So I see a lot of people who have, you know, fifteen, like right off the, they'll set up OpenClaw and, um, fifteen subagents, thirty skills, yet you haven't even set up your own workflows, right? And these things are cool right off the bat and, and there's a perfect time to use subagents. I use subagents a lot, but the way you build, like, I call it scaling for productivity, not scaling for what looks cool, right?

• 14:48Speaker 01

  Like I've seen, like, for example, Paperclip, Paperclip looks awesome, cool. I used it, I loved it, right? But I think people would be more productive if they built up from scratch their own version. Meaning like, okay, you have your own like, um, you know, like editor, right? Content creator.

• 15:05Greg Isenberg?

  So you're, you're asking people to do the work, basically?

• 15:08Speaker 01

  Hundred percent, hundred percent. And cause the thing is, it's like, look, I'm in a position where like people using like these beefed up things make a lot more sense for me. And the reason being is like, I can build a product like that, like I know what your audience wants. I know what my audience wants, like, you know, heck, I can spin up agents and build this thing, right? But if I'm gonna be completely honest, if you wanna scale for productivity, it starts with one agent and you building up the skills. And then, okay, now you've built up some skills and now you add a subagent and your one agent manages multiple agents, right? Like, imagine this, like, imagine I start a company and off rip, I have ten employees. Never managed a team in my life, heck, I don't even have a really big family. So like, I'm alone, you know what I mean?

• 15:50Speaker 01

  So it's like, you have to sort of, yeah, it's not sexy. Um, and I apologize if this is not the cool thing people wanted to hear, but you sort of have to put in the work and build it up. And I, and I personally believe you're building skills, like your personal human skills, not. Skilled on MD files that when the models get better, when the agents get better, you will be more valuable because at the end of the day, as long as there's no new paradigm for models, LLMs just predict tokens. They don't understand or know the way you and I do. Right? And this is why, although like the job scene and all this stuff is scary, I genuinely believe anyone who knows how these tools work and like knows how to build agents and like craft skills and like knows how to make them productive. We're in for a good run.

• 16:35Greg Isenberg?

  Hmm. So you're saying that if you know how to do this, you won't join the permanent underclass.

• 16:40Speaker 01

  The permanent underclass.

• 16:44Speaker 01

  So is the permanent underclass basically like I've seen this, these, this, this on Twitter a lot. Is that basically AI has replaced you. So now you're just.

• 16:53Greg Isenberg?

  From what I understand, it's once AGI comes, all these white collar workers are going to lose their jobs. And if you don't know how to build skills, use AI, people say you're joining the permanent underclass. That's, that's the term.

• 17:12Speaker 01

  It's permanent too. That's scary. So I just have a little bit of time left.

• 17:17Greg Isenberg?

  Yeah. By the way, like it's ridiculous to call it a permanent underclass.

• 17:22Speaker 01

  Yeah. Cause that's terrifying.

• 17:23Greg Isenberg?

  I can understand underclass.

• 17:24Speaker 01

  But permanent, it's like. Like you say, there's no hope. Like.

• 17:28Greg Isenberg?

  No.

• 17:29Speaker 01

  Yeah. Um, I mean, we are in like knowledge that took 20 people, 20 years to acquire is now like 20 bucks a month, right? So there is like a huge shift, right? People who are non-technical or I think I saw yesterday, like some guy had like a hundred million dollars. Um, and he vibe coded the whole app. I think it was him.

• 17:49Greg Isenberg?

  1.8 billion.

• 17:51Speaker 01

  Billion?

• 17:51Greg Isenberg?

  Yeah.

• 17:54Speaker 01

  So you know what I mean? Like it is the, there is a shift, right? And I think this idea of like.

• 17:59Greg Isenberg?

  I love how you were like billion. You were about to just leave this podcast and just be like.

• 18:03Speaker 01

  No, you know what it is? I just realized, man, I overthink things like I just need to drop the thing, release the thing. And there's like wisdom in that, like. There needs to be this level of delusion, which I don't have. Like, I'm trying to work on where you're like, this is just gonna work out. We're just gonna launch the product. It's gonna succeed. And if it doesn't, on to the next one, cuz 1.8 billion.

• 18:23Greg Isenberg?

  Yeah, dude.

• 18:24Speaker 01

  Like B?

• 18:25Greg Isenberg?

  B.

• 18:26Speaker 01

  USD?

• 18:27Greg Isenberg?

  We're, yeah, we're not talking Monopoly Canadian dollars.

• 18:31Speaker 01

  Cuz it was Canadian. Uh, it's, it's, uh.

• 18:34Greg Isenberg?

  We're not talking Carney coins.

• 18:40Speaker 01

  We're talking, we're talking real Benjamin.

• 18:42Greg Isenberg?

  Yeah.

• 18:43Speaker 01

  Yeah, that makes sense. That makes sense. But yeah, like, I, I hope this, like, understanding of, like, again, I personally don't think, you don't need any agent dot M D file, unless you have something proprietary. Skills are valuable. Build your own, though. Build, build your own. Like, you know, like, when you asked your mom when you were a kid, oh, can we have McDonald's? And she's like, we have food at home. We have food at home. Build your own skills. For coding perspective, from coding wise,

• 19:11Speaker 01

  a lot of the companies, model companies have realized that the agents are really good at writing code, particularly TypeScript. And this is why there's been, like, you see this advancement with, like, Claude Co-Work and, like, even Open Claw. Really what they're doing under the hood is they're writing code, right? They're writing code, calling APIs and all this stuff. So, when it comes to building a project,

• 19:35Speaker 01

  you actually don't need skills or, like, you don't need an agent M D file specific to the text that you use. Like, I remember we used to, I'm using React and, you know, Convex, or I'm using NextJS and Superbase. I'm using this and I'm using that. And you put that in the agent M D file, and you have, like, all these lines. For the most part, unless, again, you have a specific, specific workflow, unnecessary. And the reason being is, code itself has become context now. So, the more, the more important thing is starting with a solid foundation. Templates used to be big back in the day. People made lots of money with templates. I believe templates are gonna have a renaissance. Because if you have a solid, like, template, right? Like, whether it be, like, for a web app or mobile app,

• 20:18Speaker 01

  because that becomes context for the agent, it's going to build on top of that, right? And, again, I didn't need some large agent dot M D file. I didn't need any large Claude M D file. What I needed was, again, minimal context usage and skills. So, if there's anything. Anyone can learn from me is build your own skills. Build your own skills and there's this methodology. I don't know if I've shared this with you recursively building skills. So let's say you've built your skill, right? I have, I'll draw a diagram because why not? Let's say I have a workflow and after you like setting up my workflow with an agent. I've decided, you know what? I'm going to turn this into a skill, right?

• 21:03Speaker 01

  So this is my skill dot M D. Now here's the thing, even though you have the skill dot M D. The agent at some point is still going to mess up because there's probably gaps in the information it has in the skill. So when it messes up, I'm going to work with it again. How do I work with it? You messed up. Try calling the API again. Try doing this again, or even ask it when it tells you, oh, I failed. I couldn't do this task. Believe it or not, when you tell the agent, why did you fail? When you ask it like, what's the error that you got? It will tell you descriptively, oh, I got a 505 error. You, you have insufficient credits like, oh, okay. So it's a credit issue. Fine. So I would tell it that.

• 21:44Speaker 01

  And then I would pass that failure back to the agent. So let's say it did something wrong. We identified the failure. All I did was asking it. I will give that failure back to the agent. I'll be like, you failed here. This didn't work. Fix this. It's going to fix. It's going to write code. It's going to do whatever it does. Once it fixes it and it's done it right. Now you tell it with the new fix, update the skill. So this doesn't happen again. I have like for my YouTube channel, I have like a report generator. It calls notion, dub analytics, YouTube analytics, Twitter analytics, post from mine. It pulls from like eight data sources. There's no way you're going to one prompt and the agent's going to do it.

• 22:28Speaker 01

  But every time I tell it to do that workflow, it takes like 10 minutes. It executes it flawlessly. Why? I went through five loops of this. Five iterations of recursively building this skill. And that skill is so good. I genuinely think if anyone's going to, if like skills marketplace is going to be a thing, there's going to be people who sell skills, like really well-defined, like step-by-step skills, because people are just creating them without having built out the workflow with the agent. Right? So use the workflow by hand, like telling it each step. Once it's done it completely, create the skill dot M D file. Continue to use it. It's going to mess up. When it messes up, you, thank God, you don't complain because a lot of people like, oh, it messed up. I'm angry.

• 23:12Speaker 01

  No, this is a moment where you identify the error. Tell it, this is the error. Fix it. It'll fix it itself. And then you tell it to update the skill file so that this doesn't happen again.

• 23:23Greg Isenberg?

  So that's a little bit about shifting your expectation, right? Because people just assume it's going to work in the beginning. You're saying basically it's not going to work initially. There's going to be two, three, five, six hiccups. And over time, it should be good.

• 23:41Speaker 01

  So this is most people's expectations, right?

• 23:44Greg Isenberg?

  Yeah.

• 23:45Speaker 01

  And the way I've personally experienced is it's like this. So there's like this early area of investment that you have to make that sucks, that nobody will tell you, especially agent harness this company because they wouldn't raise as much money if they did. But like this, maybe I would give it two weeks because it took me two weeks. Like OpenClaw, when I first set up OpenClaw, I thought the same thing. I'm like, what is this garbage? Or like, it doesn't understand anything. It's confused. And then I realized like, oh, like, let me go lower level. The models and the agents, like they, they don't think like you and me, right? I could, I could tell you, hey, Greg, we need a report on like, you know, the financials in Notion.

• 24:29Speaker 01

  Because you're probably, we're in the same business. We work together. You would understand based on the context you have the business, what that means. But imagine a new guy joins like, yeah, I need a report on the financials. So where do I even start?

• 24:39Greg Isenberg?

  You know what it reminds me? I wonder if we can put this clip in, but in the office, you watch the office?

• 24:45Speaker 01

  I am not an office watcher, unfortunately.

• 24:47Greg Isenberg?

  There's a clip that, uh, there's a new boss and the new boss goes to Jim, one of the main characters. Yeah. And he asked for a rundown. So go, go the office, the office rundown.

• 25:05Greg Isenberg?

  Basically, Charles, the whole episode is about Jim trying to ask around and be like, "What is a rundown?" Like, "What is a rundown?" He's like calling his dad, like, "What is a rundown?" You know what I mean? He's just, um, he didn't have the context. He didn't have the context.

• 25:26Speaker 01

  Yeah. And it goes back to my initial point. The models are really, really good now, but the context matters more than anything, right? So when you see like these large agent, like companies and subagents, and again, I'm not saying those don't work, but I'm saying, probably won't work for you off rip because you haven't built it up to get to that point, right? So let's say like for me, for example, I started with, um, one agent. Let me draw this. I started with one agent and this was like my main agent. This did everything, right? This checked my spreadsheet. This checked my sponsors email and all these type of things. And once I had like predefined workflows, let's say for like working with sponsors,

• 26:08Speaker 01

  then I can actually have a subagent. What's the purpose of the subagent? The subagent does all the marketing stuff, right? But I'm not creating the subagent for the sake of creating it. It's going to have skills. It's going to have context and it actually makes sense for me to have subagents, right? So I've built out my thing to like, now I have five subagents. I have one for marketing, one for business, one for personal, and that's it. And I'm willing to bet if I want open cloud to open cloud with anyone, my system is more productive because I didn't scale for what looks cool. I scaled for productivity. That was a bar.

• 26:45Greg Isenberg?

  That was a huge bar. We gotta clip that. I was just thinking, that clip, that's gonna rip.

• 26:50Speaker 01

  Yeah, that was a bar.

• 26:52Greg Isenberg?

  What else do you want to leave people with? Or is this, this is the main point?

• 26:55Speaker 01

  Yeah, like here's like the, we've got to a point where the models are good. The models are really good. The context matters plus the harness, right? So for example, um, there was this benchmark, although I'm not 100% supporting it, that there was a difference between the quality of output that Cursor generated versus Cloud Code versus Codex, right?

• 27:19Speaker 01

  So what that tells me is that we've reached a point where the models are really, really good. They're probably going to get better. The next iteration is probably going to get better. But the harness and the tools that you surround it, the context that you give it is going to matter even more. And just like in everything in life, less is more, right? Like, building up step by step, making it productive for you first, before you add the shiny new thing. Like, because I tried all these tools all the time, like especially Paperclip. Paperclip blew up and a lot of people have been talking about it, and it's fantastic. But I'm willing to bet if people took two weeks to build up to the version, because you can prompt OpenClaw to do all that stuff, if they built up their own version of Paperclip in two, three weeks, where like, they're building things that they actually

• 28:03Speaker 01

  need, their productivity level will skyrocket through the roof.

• 28:07Greg Isenberg?

  It's a hot take. It's a hot take.

• 28:09Speaker 01

  Might get me in trouble.

• 28:10Greg Isenberg?

  No, okay. Who's it gonna get you in trouble with?

• 28:12Speaker 01

  Maybe Perfect Clip loses a billion dollars and they don't acquire my podcast.

• 28:18Greg Isenberg?

  I think, uh, listen, you're, you're out there, you're trying things, and you're just sharing what you're learning in real time. So if you're just...

• 28:26Speaker 01

  Things can change, by the way. Like, two weeks from now, it could be like, no, give the agent everything. There's this new memory paper that Google released that like, now, like, it has the ability to index information and stuff, but as it, as it pertains to real life, less is more, simple is better, right? If you can't explain it in, in a few sentences, you probably don't really understand it, right? And I find that the models are trained on so much information, especially when it comes to programming, building, and like, and, um, what do you call, like, day-to-day work, like, financial work, or like, any sort of, like, you know, checking contracts and stuff, like, the, the model companies are focusing on that, like, on white-collar work. The models are really, really good.

• 29:08Speaker 01

  What matters more is the harness and the tools you provided. And the one thing that you and I have that the models don't have is my specific workflow, my specific taste, my specific strategy of doing things. And those can be qualified in skills, right? This is why, like, skills make sense when you build them. Not if you download my skill, like, I have this one skill, um, like, again, don't download it. Dude, I'm, I'm telling you now, do not download it, don't use it. I just put it so I can get some GitHub stars.

• 29:39Speaker 01

  I have this one skill and it's literally a code structure skill. And I'll put the mark down so people could see it. It's 116 lines. It's basically after AI has generated a bunch of code. I like it structured in a certain way, so it's easy for me to review it. And like I mentioned earlier with skills, the only thing that gets added into context is the name and description. So when I look at the name, it's code structure. When I look at the description, use when multiple workflows duplicate the same operational logic when deciding that blah, blah, blah, blah, blah, some nerd stuff. So when I tell the agent, I want to clean up the code structure. It checks the skills it has, it sees the name, it reads the description. It's like, oh, this makes sense.

• 30:19Speaker 01

  Then it progressively discloses, meaning once it realizes it needs this skill, then it adds the rest of this, right? Versus if this was my agent dot M D file, imagine every single time, and we can actually check how many tokens this is. Let me check. What was it? OpenAI token, tokenizer. If I go to this. So this is 944 tokens. So if this was an agent dot M D file, every single time I have a chat, I'm adding 944 tokens. Tokens ain't cheap now.

• 30:53Greg Isenberg?

  No.

• 30:53Speaker 01

  But if I just have the name and the description, it's just 53 tokens.

• 30:58Greg Isenberg?

  And it's not even cheap. It's just like you're not trying to hit the limit quicker than you need to hit the limit.

• 31:04Speaker 01

  Cause the model will get dumb as the context window closes, right? So if you have like a context window, and I can draw this out. If this is your context window and like the optimal is you're between like, there's always like, maybe like 10% is already filled with all the system prompt and all that stuff. You want to be between like, you know, fresh to like 70%. Cause the closer you get to 99, 100%, like 99, 90, 80%, it starts to get dumb, right? And you could think of this like a human, like imagine you throw a bunch of information again and again and again and again. And this is why like, when I like was in school, like last minute studying never worked for me. Cause like I didn't pay attention the entire year.

• 31:48Speaker 01

  Now I have to learn about polynomials and I have to do these graphs and there's this weird notation. It's impossible for me to catch up, right? And it's the same way with the agents. You want to keep your context when you want to save your context window because it saves you money. But not only that, it makes a more performant agent. So less is more, less is more. Rely more on the model's strengths and what the model needs is what's unique and special about you, your workflow, your business, not general knowledge. Don't tell the model, use React. It knows to use React. Don't tell the model, you know, things that like should already be known for the like, you know,

• 32:29Speaker 01

  tasks. Like for example, like let's say I'm doing a financial report and in the agents dot M D file, I say to denote money, use a dollar sign. It's going to use a dollar sign, right? Now, if you have a specific currency, then you like, oh, use this currency. This is the, you know, like for something that the agent won't do manually, like won't know manually. That's when you have like your agent dot M Ds, Claude dot M Ds. But honestly, these are a farce. You don't need them. Skills, skills, skills, skills, skills is what it's at.

• 33:03Greg Isenberg?

  Thanks for keeping it real. I appreciate you, man.

• 33:05Speaker 01

  It's all I'm gonna do.

• 33:06Greg Isenberg?

  No, I appreciate it. Like always, I'll include links where you can follow Ross Mike on YouTube and X and other places. In the show notes in the description. So go follow him there. Always clearly breaking down things. We I have to be real with you. You weren't going to come on the show today.

• 33:24Speaker 01

  I wasn't. And I'll be honest, I told Greg, and I'm just gonna be frank. I'm like, I don't have that banger. You know, something new dropping. Let's review it. Cause if we're gonna be honest, there are not that many tools dropping nowadays. Like unfortunately, the big dogs are running the show.

• 33:40Greg Isenberg?

  Yep.

• 33:41Speaker 01

  The Claude's and the Anthropics and the OpenAI, especially when it comes to general purpose and and coding. They sort of run the game. So they're releasing updates and like all the stuff has already been covered. So I was like, Greg, I don't know if I have anything valuable.

• 33:57Greg Isenberg?

  And what did I say?

• 33:58Speaker 01

  You're like the people, you know, you gotta think about impact. You gotta think about what, you know, this could apply to someone's. And you showed me like a testimony, right?

• 34:05Greg Isenberg?

  I sent a text to you.

• 34:07Speaker 01

  Yeah.

• 34:07Greg Isenberg?

  I'm gonna pull it up.

• 34:10Greg Isenberg?

  I sent a text to you of someone who saw a video that we did together. And that video got him into coding. Now he's running a cake business and he's making $150,000 a year and growing. And he said, "The Greg and Ross Mike episode in November last year is what got me into coding. I've recommended to everyone asking how to start out." And I just sent you that text and I said, "It's not about the numbers. It's not about..." you know, because you said in the text...

• 34:43Speaker 01

  You don't see it sometimes, right?

• 34:44Greg Isenberg?

  I need everything we do to get to 200K views minimum.

• 34:48Speaker 01

  Yeah, yeah, yeah.

• 34:49Greg Isenberg?

  And I'm just like, I hope this gets 200K views or more. So like and comment to juice those algorithms. But if it gets 2,000 and two people end up taking this information and changes their business, their productivity, how they think about things. And, you know, I think that's why, I think that's why you and myself have been put on this planet Earth is to inspire people to get their creative juices flowing. And so I thank you for, for coming on and, and taking time out of your day.

• 35:18Speaker 01

  And I appreciate the motivation and yeah, I hope this helps somebody and I can't wait to be back with more.

• 35:23Greg Isenberg?

  Absolutely. All right, catch you later, dude.