How One Creator Built an AI Content Tool That Hit $50K in 6 Weeks
An engineer who started his career in consulting and ended up in the creator economy built, in just two weeks, an artificial intelligence tool focused on content creators. This product, called Stanley, reached $50,000 in monthly recurring revenue in just 6 weeks and today is part of a business that generates tens of millions of dollars per year.
The most interesting part: none of this started at a big tech company with a huge team. It began with someone who felt underused, decided to learn how to code on his own, and used each new experience as a stepping stone: from a mineral processing lab, through Deloitte and eBay, all the way to creating the Stan platform, built for creators.
In this article, we are going to break down that journey, the concept of vibe coding, how Stanley was built from scratch in 14 days, and why this kind of AI tool is changing the game for people who make a living from content.
From Chemical Engineering to Code: Where It All Started
In 2014, founder Vitalii Dodonov started studying chemical engineering at the University of Alberta in Canada. He did not choose the major out of immediate passion, but because someone recommended it as a stable career path with good pay.
During college, he worked in a mineral processing lab. There, he saw people doing extremely repetitive, manual, and exhausting tasks up close. That routine triggered an alert: it made no sense to keep doing everything that way when a big chunk of the work could be automated with software.
At that point, he began teaching himself how to code. He used the internet, videos, tutorials, and a lot of trial and error. His initial focus was not to become a dev ninja overnight, but to understand how to use code to turn slow processes into something smarter and more efficient.
The First Big Shift: Deloitte and Getting into Data
When Deloitte showed up on campus to recruit, Vitalii was still finishing his degree. He caught their attention not because he had a computer science diploma, but because he had a real track record showing he had taught himself to code and was applying that in concrete projects.
The company offered him a data scientist role at the start of his senior year, and he joined three months after graduating in 2018. At Deloitte, it became clear that his true interest was not traditional consulting, but the world of software, code, and building digital products.
In parallel with his job, he kept building side projects. The main one was Vhinny, a platform that aggregated financial information from publicly traded companies. This project required him to design architecture, databases, integrations, and the interface almost from scratch. Vhinny became a practical lab for everything that would come next.
Building a Technical Foundation with Vhinny
Vhinny was not just a cool side project; it was Vitalii’s private software engineering school. He would wake up early and work on the project from 5 a.m. to 9 a.m., then do his full-time job, and at night jump back into the code from 5 p.m. to 9 p.m.
Over two years, he learned the hard way concepts that usually only show up in the day-to-day work of senior and staff engineers:
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- how to structure the architecture of a full system;
- how to integrate multiple data sources;
- how to scale a platform to many users;
- how to think about performance, availability, and maintenance.
He learned all of that with Google, YouTube, and a lot of research, working backward: starting from the problem and then figuring out which technologies were required to get there.
Moving to eBay and a Jump in Seniority
After almost two years at Deloitte, he hit a familiar feeling: he was being paid less than he was worth and was in the wrong place for the kind of skills he had developed. He knew the right market for his profile was software engineering, not consulting.
That is when a developer role at eBay came up. He applied through LinkedIn’s easy apply feature, with no elaborate networking or complex plan. The challenge showed up in the technical interviews, where he did not do well on the more theoretical parts of algorithms and data structures.
The turning point came in the conversation with the hiring manager. Instead of trying to impress with memorized answers, Vitalii pulled up Vhinny’s architecture and gave a full walkthrough, showing how the system worked end to end.
He explained:
- how he had designed the backend;
- how he orchestrated integrations;
- how he handled data in production;
- how he thought about reliability and scalability.
This practical demo convinced the team that, even without a computer science degree, he was already operating at a senior engineer level. The result: he was hired in May 2020 as a senior software engineer.
The Birth of Stan: Creators, Link in Bio, and Digital Products
It was during his time at eBay that Vitalii met John, who would later become his cofounder. Together, they began sketching what would eventually become Stan, a platform designed to help creators sell digital products using a single link in bio.
The idea was to eliminate the pain of having links, forms, checkouts, and random pages scattered all over the web. Instead, any creator could bring everything together in a single experience, simplifying the sales journey for:
- online courses;
- mentorships and consulting;
- paid communities;
- ebooks and digital products;
- training programs and ongoing offers.
Over time, Stan grew into a large business, reaching tens of millions of dollars in annual revenue. In June 2021, Vitalii decided to leave eBay to work full-time at the company, taking on the role of CTO.
From Selling Products to Building an Audience
In the first few years, the focus was solidifying the creator commerce platform. But as the user base grew, one key insight became obvious: for many creators, the main problem was not building the digital product itself, but having an audience that was truly ready to buy.
Stan’s mission began to shift slightly. Instead of just being a place to monetize, the company wanted to help creators build and grow their audience, so they would have a solid base of buyers. After all, without a warm audience, even the best offer in the world gets ignored.
That was the context in which the need emerged for a more advanced content product, capable of:
- helping creators publish consistently;
- maintaining each person’s voice and style;
- optimizing posts based on real engagement data;
- making audience growth less random.
Vibe Coding in London: 14 Days to Build Stanley
Vitalii and John decided to go beyond small features inside Stan and bet on a new AI product focused on content. To do that, they adopted a concept known as vibe coding: diving fully into creation, with no distractions, letting product intuition and outcome focus guide the process.
The two locked themselves in a flat in London for 14 days. They chose the city precisely because it was a new environment, far from their routines, helping them see the problem with fresh eyes.
During this intense period, Vitalii focused 100 percent on building Stanley, defined as Stan’s AI head of content. The idea was to build a tool capable of:
- generating posts that sound like the creator themselves;
- initially focusing on the LinkedIn platform;
- analyzing post performance;
- adjusting style based on what performed best.
Important detail: he was not bouncing around between a thousand files and frameworks at the same time. The workflow was basically him and the cursor, testing, tweaking, integrating language models, and refining the product experience in short cycles.
What Stanley Actually Does
Stanley started out as an AI writing assistant, but quickly evolved into something more robust. Instead of just suggesting generic text, the tool:
- learns the user’s style based on existing content examples;
- generates new posts aligned with that style;
- publishes and tracks engagement metrics;
- identifies formats and topics that perform best;
- helps repeat and improve what works.
With that, Stanley stopped being just a post generator and became a performance engine, helping creators consistently produce content with viral potential, without having to start from scratch every single day.
The entire development process was documented on video and published on YouTube, openly showing the step-by-step product build in real time. When they officially launched, on the very first day they landed about 250 paying customers.
From Zero to $50K MRR in 6 Weeks
The impact was fast. Six weeks after launch, Stanley had reached around $50,000 in monthly recurring revenue. From there, the product kept growing.
Today, Stanley is on track to hit $3 million in annual recurring revenue, split across two main fronts:
- about $1.5 million from Stanley focused on Instagram;
- about $1.5 million from Stanley focused on LinkedIn.
Stan as a company operates around $40 million in annual revenue across the entire business. Stanley has become a key piece of that engine.
Long-Term Vision: An AI Head of Content for Everyone
The vision behind Stanley is broad: to create what they call the world’s AI head of content. The idea is that anyone who is excellent at what they do but has little digital presence can have their voice amplified without needing to become a full-time influencer.
The logic is straightforward:
- building a relevant online presence has become almost mandatory in many fields;
- doing it well requires time, communication skills, and strategy;
- a lot of people do not have the time or simply dislike the operational side of content creation;
- a well-trained AI can take over a big part of that backstage work.
With a system like this, professionals can focus on what they do best — teaching, building, serving clients, researching — while the AI head of content handles the heavy lifting of turning ideas and experiences into consistent posts, threads, newsletters, and campaigns.
What It Takes to Vibe Code Efficiently
In Vitalii’s view, you do not need years of experience to start vibe coding. What matters most is the mindset:
- deeply understanding the problem you want to solve;
- focusing on delivering real value for a lot of people;
- not confusing code with the end goal: it is just the means;
- working backward from a clear objective.
He suggests a very direct approach: define a concrete goal, such as building a $10 million business, having the freedom to leave a traditional job, or creating financial security for your family. From there, ask the key question: What do I need to build to get there?
We are living in a moment when AI models and intelligent agents give individuals a level of power that used to require entire teams. The real barrier is now less about access to technology and more about the willingness to learn, experiment, and keep iterating until the product truly fits a real market pain.
Personal Plans and a Future Outside Corporate
With all this context, Vitalii’s ambition today is to build a generational company, the kind that stays relevant for decades and becomes part of the world’s infrastructure in 30 or 40 years.
He is working on different fronts to position himself on that path and, looking back, he sees it as unlikely that he will return to the traditional corporate world. His feeling is that he has found the ideal space to connect technology, product, AI, and the creator economy into something with big, long-term impact.
More than just a story of quick AI success, Stanley’s case shows how combining:
- self-taught learning;
- full immersion in real problems;
- smart use of language models;
- and a focus on the creator economy;
can generate products that not only make a lot of money but also help thousands of people make a living from their own work with more autonomy, digital presence, and consistency.
