He Applied To 1200 Jobs a Day With OpenClaw

0:00In this video, I'm going to be talking about an open claw use case that is very, very interesting, especially when it comes to the job market. We're going to be talking about a guy named Nerik. And Nerik applied to 8,000 jobs on LinkedIn with his open claw setup in one day. And after 30 days, he had created $428,000 pipeline in salary offers. And I want to be straight up with you because the reason I'm talking about this is not because of the job hunting is the whole point of the story. The point is is what this story shows you about what agents are actually doing when you treat them like software instead of a slightly better assistant. So, Nerik posted on his X a few days ago. He built an open claw workflow back in January that would

0:47find job postings on LinkedIn, and then it would fill out the applications, submit them, and the craziest part is is the agent was set to auto start. So, if he actually got hired somewhere, no human in the loop after that step. Just the agent going end-to-end on the entire process. His target was 8,000 applications per day. And you know what happened the moment he started running it? LinkedIn captchas, rate limits, posting filters, all of that kicked in and knocked his actual daily throughput down to about 1,200 applications per day. Still crazy when you think about it. Which, you know, in the end is only 15% of what he was aiming for, but still. And I think a lot of people hear that number and immediately think, well, you know, the thing right? It only hit 15% of the goal, but the framing was

1:34completely backwards. Here is what 1,200 applications per day actually means over the course of a month. 36,000 job applications in 30 days after running a full month of 4.0% of the companies had move forward with him. So, 4% of the companies move forward with him in some type of hiring capacity. And all of this happened on autopilot. So, only 12% had actively rejected or fired the agent. And that means the vast majority of contracts were still warm or in the process. And the total value of the roles he was in conversations about added up to $428,000 in potential salary. So, $428,000 essentially for like a cron job that's running in the background. So, if you

2:19just want to do the math out loud because I think when you hear it laid out, it really hits different. The most aggressive human job seeker, someone who is, you know, really grinding, might send out 200 to 300 applications over the next few months before they are completely burned out. Nerik sent out 36,000 job applications in 30 days. I mean, that's a startup idea in itself, just helping people find jobs. That's the way to go. Real quick, before we keep going, if you're watching this and you want to actually build with some of these tools >> [music] >> not just watch videos about them, then you're going to want to check out our community down below, Shipping School. We have a full Claude code course, a full Open Claw course, and four live bootcamps every single week where we actually help you get set up from scratch. Like actually set this thing

3:05up, not just watch a tutorial and figure it out by yourself. And we also provide one-on-one coaching, so you could book a call with me, we could share screens, and I can help you get Claude code or Open Claw running on your machine. That's it, no fluff. I built this community because watching YouTube's only gets you so far. We launched it just 3 days ago and we have over 55 members. You need people around you who are actually building, people who hold you accountable, and coaches who can help you when you get stuck. I'll put the link in the description down below. Get in now before the price goes up. And that is 180 times more applications in a fraction of the time. He did not work harder, he did not have a better resume, he had an agent running at scale that a human body physically can't match. And

3:51that is the whole point, and that is the whole thing. You know, that is the insight that gets missed when people talk about AI agents. Agents give you a software level distribution on human workflow. You're not replacing the human, you are giving one human the throughput of an entire team running 24 hours a day. The job seeker does not disappear from the process. Nerik is still the one who has to show up in the interviews, pass the technical assessments, and decide which offer to take. But the entire cold outreach layer, the part that normally burns people out, that got delegated to a machine. Which is pretty cool when you really think about it. I mean, that's the whole point of running Open Claw or, you know, if you're running something like Perplexity Personal Computer or the new Claude Co-work. That's the whole point of autonomous, redundant tasks

4:38that are handed off to AI agents, so we can work on the higher level stuff. Now, I know some of you are thinking about whether this is the right thing to do, whether if it's fair to other job seekers, whether LinkedIn should even allow it, and I totally get that. That is a real conversation worth having, but from a pure systems thinking perspective, what Nerik built is just a workflow running at scale. Every single action the agent takes is something a human could do manually. Find a posting, fill out a form, click submit, right? And then move on to the next one. The only difference is the volume. And volume is exactly what software does better than most humans. So, the more interesting question for me is what does this pattern unlock for your business? Because most of you watching this are

5:24not trying to automate job search. But I would bet a lot of you have workflows that look exactly like this, like cold outreach, right? And basically, you have not built an agent for them yet. So, let me think about it. You need to reach out to potential clients, we all do. You need to pitch your product to newsletters or directories. You need to apply to brand deals or podcast appearances. I do that. You need to follow up with people who have not responded to your first message. And you need to contact suppliers or partners or distributors. Every single one of those is a job application problem with different clothes on it, right? It is a list of targets, a standard action, and a logging system to track what happened. The category is outreach. The form in the form it takes is different for

6:09everyone. But the underlying structure is the same. And here's the thing. The bottleneck is almost never the technology. Open Claw can run cron jobs. It can hit APIs. It can fill out forms through browser automation. It can even send messages. It can log results to a database. All that's really easy. And all of that has been built in and documented. The bottleneck is basically you sitting down and actually defining the workflow clearly enough that the agent can actually execute it on a reliable basis. That is the real work, not the code at all, and not the setup. It is the thinking that happens before you open a terminal. You got to map out your workflow and then hand it off to the agent. So, I'm going to break down what that setup actually looks like for Nerik and like in terms of the components. Like what you would actually need if you were building something similar for your business. So, like the

6:56first piece is just basically a clean source of targets, like in Nerik's case, it was LinkedIn job postings filtered by whatever criteria he had set up. So, for you, it might be a spreadsheet of podcast hosts or like a database of brands in your niche or it could be anything, right? A list of potential clients from a directory. That's not the point. Whatever it is, you need that source to be structured so the agent can pull from it consistently. If your list is messy, your agent will be crazy and going everywhere. The second piece is the action itself, the specific thing the agent does for each item in the list. This needs to be clean and well-defined. The more standardized the action, the more reliable the execution. Nerik was going through LinkedIn's interface,

7:42which is why the captchas hit hard, and they have that in place for a reason. If you can work through an API instead of a browser, your throughput will be much higher and far more consistent because you are not fighting the platform's bot detection. So, when you are designing this for your use case, always ask yourself, is there an API I can use instead of the browser browser automation? The third piece to this puzzle is the logging layer, and this is the one that people skip. You need your agent to record what it did, when it did it, and what the result was. Because when you are running 1,200 actions a day, you can't manually review each one. The log is how you know what is working and what is not. And Nerik knew his 4% conversion rate because the system was tracking outcomes at every step. Without

8:27that tracking, you're just guessing about what is happening inside your pipeline. The fourth piece is the autonomy boundary, how far the agent goes before it checks in with you. If you're struggling to keep up with content, what I'm about to save you about 40 days worth of work. [music] I built something called Content Machine. It's 10 AI agents that run on the Open Claw orchestration, and they handle everything. Scripts, thumbnails, X [music] posts, blogs, outreach, clips, newsletters, all of it. So, I went from 1,000 subscribers to 4,000 subscribers on YouTube in 7 days using this exact system. Every single morning I wake up [music] and the content's already done. I spend maybe 15, 20 minutes reviewing

9:12and approving them, and I move on with my day. [music] It works for any niche, fitness, finance, real estate, marketing, whatever you are building, and it is 100% completely customizable to your [music] use case. So, you get the mission control dashboard, all of the cron jobs, everything I've built over the last 40 days helping me gain more and more people to subscribe and join the community. So, you plug in your own thing, and it molds it to you, it learns how you talk, and it writes so it doesn't sound like AI slop. $97 one time, it's not a subscription. I'll put the link down below, and you'll thank me later. Nerik ran his pretty far out letting it auto start if hired. That is pretty aggressive and not, you know, what I would recommend for most businesses, but what I think what works

9:58better is having the agent handle the volume work and then surface the wins free to review. It processes 1,200 things and then it pings you on Telegram when something positive comes back. You look at the response, you decide what to do next. The ball's in your court. The agent does the scale work basically and you do the judgment calls. I do that for my social media. You know, that is the design pattern I keep coming back to. It is the one where, you know, you get leverage without the risk of the machine doing something you did not want it to do. For anyone inside the community, you know, who is thinking about building their first agent, listen to this story. The story should be the thing that gets you moving. The barrier to entry here is just not high at all. You need to replicate Narek's exact setup. You do not need to hit 1,200 actions per day

10:44out of the gate. Start with a list of 100 targets. Write down in plain language in a spreadsheet exactly what the agent should be doing for each one. Build a small test and then run it. Something that hits 10 like 10 action items. Log the results, see what breaks, then clean up the edge cases and then scale it up from there. That is the whole game. Define the workflow, log everything, learn from those logs, and then scale what works. Narek ended up with a $428,000 pipeline because he was willing to run the experiment and let the numbers tell him what was actually happening. Most people are literally still just trying to decide whether they should start running things on Open Cloud. I know you watching the vi- this video are already far ahead. I'm just doing these use

11:29cases to kind of set an example for people that want to do cold outreach, want to build their sales pipeline. Take the components from what Narek did and apply them to your use case. The one thing that it was you're going to have problems with, and so did Narek, is the capture problem. You know, that cut his throughput from 8,000 to 1,200 per day, but still that's a lot of volume. But he didn't quit, too. Neither should you. He noted it as a constraint, he kept running, and just let the math do its thing. That's the mindset you should have. You're not looking for perfection on every run. You are looking for a positive aggregate result across the volume. And when you have an agent doing the work, you have the volume. You could see Narek's original post, I put a link down below. Go read it. Pay attention to how he frames the capture program because the way he turned a miss on his

12:17daily target into a data point about where real friction lives in the workflow, that thinking is worth starting on its own. And I personally would take that article and give it to your agent and say, "How can we apply what Narek did in his LinkedIn outreach to my sales, to podcast outreach, to customer service?" Start doing that. Take this video, link the the link to the video. Give it to your agent and start applying some of these concepts and principles cuz they're no different. And if you haven't joined the community yet, we have 218 people that have joined in the last 28 days. We go over these exact systems built out with AI agents. I'll put the link to it down below. We do nine live boot camp calls a week. We just added a local LLM call where you can install your first local model and

13:03get it running on your hardware today. So, I'll put the link down below. If you haven't subscribed to the channel already, please do cuz we do videos like this every single day. We'll see you in the next one. Be blessed.