My job search experiment with OpenClaw, now turning into a full scale SaaS idea

It started as a curiosity experiment

If you have ever wished for a brutally honest job match score before spending an hour tailoring a CV, that is basically the problem I was trying to solve. My whole job search experience had started to feel like a bad game played across endless job-search websites.

In February 2026, OpenClaw swept the internet. You know how it goes. New thing drops, everyone loses their minds for a week, then most people forget about it. I downloaded it like everyone else. Then I sat there staring at my phone thinking… what do I actually do with this?

I’d been actively looking for my next role. The job market was miserable. So I thought, fine, let me see if this agent can actually help me find work. Not in some theoretical “AI will change everything” way. Just practically. Can it make my job search less painful when every job listing, job posting, and application process already felt designed to waste my evening?

That was the experiment. It got out of hand quickly.

Week one was duct tape and enthusiasm

I started small. Built a few skills to automate a browser, pull job descriptions off boards, email them to myself. It sort of worked. Felt clever for about two days.

Then I hit the obvious walls. I needed Python to scrape LinkedIn properly. I needed SerpAPI to cover other job boards and turn scattered search results into something useful. And I desperately needed to stop generating CVs by pasting updated lines into a Word document one at a time like it was 2014.

That last problem led me to RenderCV, an open-source library that lets you take structured data and output a properly formatted CV programmatically. Massive shoutout to that project.

So now I had discovery and CV generation somewhat automated. But I was drowning in roles. Twenty, thirty new listings a day that looked vaguely relevant. I couldn’t apply to all of them even with faster CV turnaround, especially when most job-search websites still make you upload your resume and repeat the same details for online applications.

Why job match scoring was the real breakthrough

I added another LLM to score each job against my profile. Match percentage, strengths, gaps, honest assessment.

This changed everything.

The AI was brutally honest in ways I wasn’t with myself. I’d find a role that looked exciting and the model would just say no. You’re underexperienced for this. Or, this “Head of AI” role is actually a mid-level data analyst position in disguise. Move on.

But it worked the other way too. I would never have dared apply to Amazon or Microsoft for something titled Senior Solutions Architect. Imposter syndrome would have killed that idea before it started. But the model scored my consulting background as a genuinely strong fit. So I applied.

I got interviews from both.

The useful bit was not some magic match percentage floating in the air. The score tried to compare my work experience, soft skills, and actual evidence against the job title, job requirements, and the shape of the work in the JD. It gave me something closer to a 0 to 100 fit check than a vague vibe.

It was also not a love calculator for jobs. It would not flatter me just because I liked the company. It would ask whether my background could actually match the job in front of me, especially for specific jobs where the title sounded better than the day-to-day reality.

Ten interviews in four weeks

The results were honestly absurd. I applied to far fewer roles than I thought I’d need to. And I got far more interviews than I could schedule around. Ten in four weeks. I became a full-time interview machine. A pleasant burden, but a burden nonetheless. It was the same shift from volume to selectivity that I unpack more directly in Less is more, even in a broken job market.

For interview prep, I used NotebookLM to study the JD, identify gaps, rehearse answers. But everyone has their own approach there so I won’t go deep on that.

I ended up with multiple offers and chose my favourite. In this market. Still feels a bit surreal if I’m honest.

To put this in context, job seekers can now submit 200+ applications on average before receiving an offer, with most online applications resulting in a 0.1% to 2% success rate. The UK labour market in early 2026 shows 2.6 unemployed people per vacancy, up from a year earlier. So getting 10 interviews from a focused batch of targeted applications felt like a cheat code. It wasn’t. It was just better targeting, a lower number of application attempts, and far less time wasted on the wrong roles.

The system worked. For me.

Here’s the problem. My workflow required comfort with Markdown, JSON, YAML, version control, git, Python environments, API keys, and the patience to debug things at midnight.

My wife tried using it once. She gave up within ten minutes. Completely fair.

The outcome this system delivers is for everyone. But the system itself was built for someone who thinks in terminals. I kept browsing r/UKJobs seeing people make the same mistakes I used to make. Spraying applications everywhere. One generic CV for everything. Never hearing back. Getting demoralised. I wanted to help but I couldn’t exactly say “here, just learn Python, YAML, and prompt engineering and you’ll be fine.”

So I’m turning it into something usable

That’s where OutRung comes in. It’s my attempt to take this workflow and make it accessible without requiring a software engineering background.

The idea is simple. You create one master profile. Career history, skills, achievements, writing preferences, what you’re looking for, what you care about in a role. Then OutRung helps you discover relevant jobs, score how well they match your background, explain strengths and gaps honestly, generate tailored CVs from that single profile, track your applications without the whole thing dissolving into tabs and guesswork, and eventually see which search lanes are actually converting in Search Insights.

It’s not completely barrier-free yet. I won’t pretend it is. But it’s dramatically simpler than what I was running before. And the core principle stays the same. Stop applying to everything. Start applying to the right things. Let a scoring layer tell you the truth about fit before you spend an hour tailoring a job application that was never going to land.

What I’d tell anyone searching right now

Get something to score your fit before you apply. Your gut feeling about a job listing is unreliable. You’ll overestimate your chances on exciting roles and underestimate yourself on intimidating ones. A decent AI job search tool helps with that first decision before you sink an hour into tailoring. The broader hiring data keeps pointing the same way: recruiters are drowning in application volume, so better targeting matters more than spraying CVs.

Tailor every CV, but not from scratch. Maintain one master profile and adapt from it. This is exactly what OutRung is built around, but even if you do it manually, stop sending the same document everywhere. Use an AI CV builder or CV tailoring workflow to match the job without rebuilding the whole story from nothing. Open-source CV tools like RenderCV prove that programmatic, structured CV generation is both possible and useful for real job seekers.

Apply to fewer roles, not more. I know this sounds counterintuitive when you’re getting ghosted. But volume without targeting just adds noise for everyone. Employers are drowning in irrelevant applications too. It goes both ways. Using job alerts on LinkedIn or similar tools makes far more sense than spamming every platform blindly.

Treat your job search like a project. Have a pipeline. Track stages. Know where everything is. Don’t rely on memory and twenty open browser tabs. A job application tracker is boring in the best possible way because it stops good applications disappearing into chaos, and a dedicated Search Insights view helps you see whether those stages are turning into replies and interviews. The UK National Careers Service’s advice on where to find vacancies reinforces that using structured, multi-source search across job openings, government sources, LinkedIn, and other job boards beats spraying applications from one site.

Use scoring to improve the search, not just the CV. A real score should look at job descriptions, work experience, and whether you can do the job full time in the context being asked for. It should help you decide whether to continue the application process before you waste time trying to impress hiring managers with a story that does not fit. That same decision gets stronger when it is paired with freshness, role-volume, and salary-band context from a market activity tracker.

The honest bit

I don’t know if OutRung will work as a product. It might. The workflow proved itself for me. But turning a personal hack into something other people can use is a different kind of problem entirely. I’m figuring it out as I go.

What I do know is that the current job search experience is broken for technical professionals. And the answer isn’t “apply to more things.” It’s apply to the right things, with the right story, at the right time. If I can make even a small part of that easier for other people, then the experiment was worth it.

A good job match score will not get the job for you. But it can stop you wasting half your search on roles that were never a fit, whether they came from a glossy board, a random search result, or one more job listing that looked promising until you read the requirements properly.