Ahoy there 🚢,
This episode is brought to you by Sam Greenwood, MLOps Tech Lead at Fuzzy Labs.
There’s a lot of hype around vibe coding at the moment. Everyone’s theorising about what it means for the future of programming, but very few people are truly putting these tools through their paces.
I’ve been using AI to assist my coding since GitHub Copilot launched in 2021. I’ve watched the capabilities increase from line completions that felt like pure magic to fully autonomous coding agents. Given I’ve been using these tools for quite some time (in the recent timeline of software), I’m also very aware of their shortfalls and their ability to cause all sorts of problems, if used the wrong way.
So when I wanted to properly test vibe coding, I wasn’t going to do it on customer projects but I needed a sandbox with real stakes. Inspired by our Hackathon and a desire to remove some of the manual admin from hiring, I decided to set myself the task of building an Applicant Tracking System (ATS), for Fuzzy Labs, using only AI coding tools.
Not a single line of code written manually. Love Island on in the background. A true test of if you can actually build production-ready software entirely through prompting, whilst equally being ready for the inevitable “I’ve got a text”.
However, my “experiment” wasn’t solely about coding today. It was, on a deeper level, about testing what I believe programming will look like in 2030, and what this current trajectory means for the entire SaaS industry.
(Artistic representation of what we imagine Sam looked like creating the ATS)
My prediction
I am very confident that the way software is built is going to look very different in the next 5-10 years.
The capabilities of LLMs have increased drastically in the last 3 years and I believe this is going to keep being the case. Besides the raw capabilities improving, the tooling that leverages them is going to keep getting better. Using Cursor, for example, is a much better way of using AI for coding than copying and pasting code into ChatGPT and then copying the output back.
And we’ll only get more tools like Cursor and tools like Cursor will keep on improving.
As a result of these developments, the speed of building software is going to increase and subsequently the cost is going to fall. What once took a squad of 5 engineers could potentially be done by 1 engineer using AI assistance.
My mum was a programmer in the 90s and when you compare the way people coded then to now, it is completely different. So when the narrative arises that the way we build software today will stay the same for the next twenty years, it seems counter intuitive. It never has before.
This shift puts lots of SaaS businesses into a precarious position. It does raise the question “is SaaS dead?” when companies and individuals can just create their own software instead of paying for expensive subscriptions.
There is some truth in that we might start seeing LLMs as an operating system in the future (dynamically creating applications for certain workflows on the fly). I’m not saying that this is what will happen, just that it has the potential.
One of the reasons I’m sceptical of SaaS being dead, at least in the short term, is that even if a company could in theory build their own HR, communication, or sales platform using AI, they would still have to manage it, ensure it stays up to date, address security concerns, bugs, and so on. There’s an argument AI can do all of that too, but in its current form it still needs an experienced engineer to oversee.
But what I do think will happen, is that disruptors will come into the SaaS industry and build competing software for a fraction of the price of what it currently costs. They’ll be leveraging AI assistance heavily and have a fraction of the number of expensive engineers. Therefore they can offer the same SaaS products for tens of pounds a month rather than the hundreds .Even more crucially, they can ship features faster and build a superior product to incumbents that aren’t making effective use of these tools.
Why try and build software internally when someone else can sell it to you for £40 a month and they can be responsible for new features, security updates, compliance, and so on? It’s a win win.
So that’s the theory. But I wanted to test where we actually are with vibe coding a SaaS product, how far we are from this near future, and what tools need to be developed to get us there.
The experiment
I asked ChatGPT Deep Research what the best way was of building this full-stack application. It recommended Railway for deployment. I prompted it to build it out and it worked really well. The frontend came together shockingly fast.
Then the GDPR problem emerged. An ATS stores personal identifiable information with strict GDPR requirements. Railway didn’t provide enough configurability as there were no options to have data stored in the UK. This could get problematic with GDPR. The trust just wasn’t there.
Where it got a bit muggy
The backend was probably the biggest challenge. I moved it to AWS Amplify but Amazon has released two major versions of amplify and the models kept getting confused between version one and version two. Meaning it kept breaking.
I spent all evening trying to prompt Claude to fix a deployment error and couldn’t do it. I knew if I dug into the errors myself I could fix it, but that would defeat the point. The experiment needed to be just prompting.
GPT-5 came out the next day. Same prompt. Fixed first try… just goes to show.
I also solved a lot of issues using an MCP server called Context7, which grounds models in actual documentation.
Since deployment, I can now build new features much quicker. But I still sometimes hit problems, it could be that AWS isn’t the right approach, or there needs to be middleware between the models and AWS, but I digress.
Cracking on
At regular stages throughout development, I’d prompt Cursor to do full security reviews, create tickets, then fix things. This caught quite a few issues so would highly recommend you do this also.
The ATS is now in testing with the team using it for actual hiring. And the team has been rigorously testing and trying to break it from a security perspective - so far it’s held up.
I don’t know exactly how much time I spent on this, but it’s safe to say it’s a fraction of the time it would’ve been if I’d built it the traditional way. But I know for certain that at no point was I just focused on it. I was always doing other things at the same time, which shows you can have it as a background task, whereas in the past you’d have to be completely focused.
Closing off
When you see people commenting online about where the future’s going to be and what the capabilities are, question if they’ve had a go...
We should be rapidly playing with and testing these tools; figuring out what works and moving ahead, not stuck debating the future of programming. The future is here.
Another sign of just how quickly this space is evolving is that since first vibe-coding this ATS, OpenAI have significantly improved Codex. It has now actually become my go-to AI coding tool (sorry, Cursor!).
It’s an exciting time for SaaS and programming - as we move into a chapter where things can be built far quicker than we imagined 10 years ago.
Keep an eye out for our new ATS, and if you’re interested in this way of thinking, why not use the tool to join the team. We’d love to see you here.
About Sam
Sam’s career spans industries from Defence to Logistics, where robustness and reliability are non-negotiable. Passionate about the transformative potential of AI, he thrives on building scalable solutions that make a real-world difference. Sam studied Computer Science at Newcastle University and outside of work can usually be found playing tennis or on the golf course.
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About Fuzzy Labs
We’re Fuzzy Labs. A Manchester-rooted open-source MLOps consultancy, founded in 2019.
Helping organisations build and productionise AI systems they genuinely own: maximising flexibility, security, and licence-free control. We work as an extension of your team, bringing deep expertise in open-source tooling to co-design pipelines, automate model operations, and build bespoke solutions when off-the-shelf won’t cut it.
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MLOps Engineer
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