How Loops Will Make You Way More Money

Everybody is talking about how loops are the next big thing when it comes to AI. For example, you have the founder of Claude Code saying it. The thing that I've been finding myself using more and more is loop. Just like the coolest thing. It's like the simplest thing that works. And you also have the founder of OpenCLAW saying it. So let's go into what that is exactly. And more specifically, let's go into how this will make more money for you, how this will make more money for your business. So let's take a look. First and foremost, the receipts are here. If you look at Boris here, he's the founder or creator of Claude Code. And he has said, I don't prompt Claude anymore. What I mostly use now is loops. There's that word loops. I create loops, they do the rest of the job. In 24 minutes, Boris reveals his real daily Claude code setup, and then Claude plus loops plus routines plus dynamic workflows. So that's one proof point. Okay. And then you have Peter Steinberger who said this recently, by the way, this has 7.6 million views on it. Okay. So this exploded. And here's your monthly reminder that you shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agent. And so what the heck does this mean exactly? So what I did is, so I have this guy that I know, Matt Van Horn. He wrote a post that got 2.8 million views. And so between these, we're talking millions and millions of views, right? And Matt here, he— I'm actually, by the way, if you check my channel on YouTube, I'm actually doing a livestream with him. So you should check that out where we actually go into a little more detail, but I'm talking about this in the context of making more money. and driving more revenue for your business, because that's ultimately what I care about. And that's what my company does. So that's what we're talking about, right? So I think he wrote some really good things here around what is a loop actually. And so it's pretty detailed. But I think the thing to call out here is that when you think about a loop, it's— there's a lot of different people responding as to what that is, right? Is it a Ralph loop? Is it just like a /goal command? What is it exactly? And so what Matt did here was that he ran the last 30 days. That's a skill that you can run to see what everyone was fighting about because nobody has a clear definition right now. And I think he did a really good job with his article here. So here's the monthly reminders that this one got the 7.6 million views, and then nobody knows but him and Boris, right? Somebody, Matthew Berman, who's another creator, said this. What a loop actually is, okay, if you look at the plain version, a loop is a small program that you write that prompts the coding agent for you. It reads what it produced, decides whether it is done, and And if not, prompts it again. You stop being the thing inside the loop typing prompts. You become the author of the loop. The model becomes a subroutine. He says in the last 30 days, this is December 2025, 100% of my contributions to Cloud Code were written by Cloud Code. I landed 259 PRs. And he said he deleted his IDE in November. He hasn't opened it since. So I think the thing to call out here is like, people are just like, oh, you know, it's just a rebranded version of a cron job, right? As to what this loop actually means. And so what Matt's saying here is that a loop is actually an evolution of cron jobs. People are saying, oh, well, isn't this just an evolution of the Ralph loop or the /goal command? And it is. And so maybe this is perhaps the step before recursive self-improvement where the agent improves itself over time. And so the way I think about this is a loop is, like, for example, I run my Hermes agent and I have a bunch of different Slack threads open with it. And oftentimes I have to come back and I have to prompt it, right? Or with my Codex, for example, or my Claude code, I have to prompt it. I have to be the human in the loop that's constantly reviewing all the time. But the idea with the loop is that it's just going to keep working on it over and over. And people say that /goal can do it, like /goal to an extent can do it now that it's been updated. But the Ralph loop back then, a couple months ago, couldn't exactly do that. Like it could work for a little longer, but at least for me, I didn't see it working through the night, right? And so /goal command can actually do that. And /loop is kind of the evolution of that, right? And so Boris posted 5 tips for running Opus autonomously for hours or days. And so that's what we all want, right? We want this to continue to move for us. And so 5 tips in his words, use auto mode for permission. So Claude doesn't ask for approval. This is if you're using Claude, use dynamic workflows to have Claude orchestrate hundreds or thousands of agents to get a task done. I don't think you usually need to use dynamic workflows. Also, it's very token intensive as well. And then use /go or /loop. /loop to nudge Claude to keep going until it's done. Use Claude code in the cloud so you can close your laptop and make sure Claude has a way to self-verify its work end to end. Okay, so that's what we're looking for, right? Tip 5 is the one that the hype skips and the practitioners obsess over. So you want a loop that's trustworthy and it has the ability to check its own work. And so if we move on here, the loop is now the expensive part. So here's where the research turned from philosophy to a finance problem. So the sharpest deflation of the whole agent's methodology came from a working engineer. So Every AI agent I shipped this year is a for loop, an LLM call, and a try-catch around the JSON parsing. The only thing agentic about it is the Anthropic bill at the end of the month. So this, again, it's very token intensive, right? And so when I start to think about this from a business standpoint, I'm going to give you a couple of examples here. Okay. By the way, if you want to grow faster, you need to have a single brain unified intelligence sitting inside of your chat. So it could be inside of Slack or Teams, but you You can see right here this bot's working and it's creating ad creatives. It is doing data pulls from Meta, from Google. You can pull your SEO data. So imagine having all these data connectors. You can ask whatever question you want, get the data pulls a lot faster. Your team can see it as well. They can choose to execute, and then you can run your other specialist agents that you have inside. So we have ad creative agents, we have email infrastructure agents. There's all these agents that can do a bunch of things, and the whole idea is they're all playing together. They're playing with your team as well. That way you're going to be able to just Move a lot faster and then grow a lot faster. So check it out. Just go to singlebrain.com. We'll see you inside. So a couple of examples when it comes to business here is you think about, let's say you have a stale deal, for example. Okay? So you might have a stale deal, then you could summarize the context, right? This is talking about in the context of sales. So you have a stale deal, okay? It's not going anywhere right now. It's a lead that you might've talked to a while back. And now what you're going to do is you're going to summarize the context of the deal, draft the follow-up, approve it, Ship it, and then you're going to track replies, and then you're going to keep looping this as needed until you maybe clear up all your stale deals, right? Or even talk about reviving deals as well for deals that you might've lost a while back, maybe 60 days out to— you can go out even like 3, 4 years or so and just try to keep reviving these deals, right? Because these are just things that you need to repeat over time. And then speed to lead. So let's say a lead comes in. So then you basically need to score that lead very quickly to see if it's a fit. You want to assign an owner, you want to draft outreach, and you want to track time to first response. Okay. So that is something that you could loop as well. Content, for example, think about all the topics that you can cover, maybe use that last 30 days skill where you can see the last 30 days what has been talked about for that particular topic. And then you can draft for different channels, you can peer review, you can publish, you can capture engagement, and then feedback for the next cycle. So you can continue these loops, right? So by the way, when it comes to business standpoint, there are, there are 5 parts over here, right? So number one is you have a trigger. So Let's say something signals that work needs to happen. Okay. Number 2 is you have signal and context. So pull the right info before acting, right? I just gave you a couple of examples over there. Number 3 is an action that you're going to take. So an agent does something. So for example, you might want to have it draft an email, for example, or you might want to draft content for you. And then maybe it's scoring something or it's routing something. Okay. Number 4, you have an eval gate. So you want to make sure that an eval is just a definition of what success looks like. And so then it helps your agent decide if something's actually good or not good. And so a human or system can ask if something is accurate, if it's safe, if it's sending. So emails are very important. Part 5, you want to stop conditions. So if it's shipped, approved, killed after 7 days, like you want to decide what that actually looks like, what that stop condition looks like. And so those are the 5 parts of every business loop ultimately, right? Again, you can apply it to content, you can apply it to sales, you can apply it to paid media, you can apply it to all these different things, you can apply it to recruiting as well. I just gave you an example earlier, like I work with my Hermes inside of Slack. The problem with working inside of Slack or even Telegram, for example, with your autonomous agents, is that those are broken loops, right? So when I look inside of Slack, it's like, oh, you have all these open threads that are basically triggers that have no actions, right? You're not pulling signals, okay? So you're basically acting blind, and you have to keep kicking it every single day. You have no eval gate as well, so you're guessing if output is good, and you're constantly doing different things inside of Slack as well, and there's no kill criteria, right? So there's stale items basically rot forever, and so that's where things go to die. That's why it's important for you to start thinking about how can you set up these these loops. And you don't have to be an engineer, you have to think about how this is applied to business, right? And I just gave you some examples there. So I'll give you some more examples. How do you apply to recruiting and operations? And so let's say you open a role, and then you need a source, then you need to score to fit, then a human reviews, and then you, then you reach out after, and then you track, you track the response rate, and you iterate on the messaging based on how the numbers are looking. So those are examples of how this all applies for business, and ultimately how you can make more money with this. And so I would encourage you to think about When you're thinking about a loop audit, for example, think about what are the most important workflows that are taking a lot of time right now or costing you a lot of money, right? And also like, where does work sit idle? So those are the broken loops that you wanna look at, right? So if something's sitting idle for a very long time, like for example, for me in Slack, I have so many open Hermes threads and I've kind of forgotten a lot about them by now. And so those are broken loops. That's why I've created like a Hermes execution OS that can keep the loops moving, right? And we're still testing that right now. What data do you pull before acting? So these are signals ultimately, and you want the loops to pull from these signals. And then who decides if the action is good enough? Is it you? Is it the agent that decides it? Do you have a really good eval for it? When does it stop? So every loop needs a kill criteria. And then does the next run get better because of the last one? So that's the compounding ultimately. And a lot of people are talking about this right now, but I haven't seen a lot of situations where people have truly cracked it yet, at least from my eye. So I would just go to say, That if people are— if you're having the creator of Claude Code and also OpenCLAW talk about this right now, in terms of this being the future and how basically prompting is, you know, having to prompt these things and sit there all the time is kind of the old way of doing the work, you should probably pay attention. The hope here is that, you know, we're no longer just one-off prompting all the time, is that we're compounding with these systems. You know, prompts ultimately, one-off prompts end up going to die, they go to the graveyard, and that loops will persist, Right? Especially if you have good feedback that you're continuing to give it, evals and good stop conditions that will make these loops durable. I think ultimately, if you're going to get on this right now, if you're having tighter loops around revenue, content, recruiting, ops, you're going to be ahead of like 99% of businesses, even 99.99% of businesses, 'cause they're not thinking about this stuff. And, you know, if you think about compound interest being the 8th wonder of the world, I just look at all this as compounding, right? And the sooner you can get not just yourself, but your business on it, and also your team on this, thinking like this, you're going to be ahead of everyone else, right? So I would just say this, like, it all comes down to loops now. If you're serious about business, you should learn this stuff. And you should definitely go check out that livestream that I did with Matt Van Horn, where we talk about, you know, from his perspective, from a more engineering background, how he thinks about loops. So we'll see you in that video.