Meet the engineer who’s building an AI that might replace him — and why he’s not worried
- info329524
- May 9
- 4 min read
Grigorij Dudnik didn’t set out to build a startup. He set out to stop doing boring things.
After years of slogging through prototypes, boilerplate, and code reviews, he had a simple idea: what if the code wrote itself?
Today, that idea is Clean Coder — an AI-powered autonomous developer that doesn’t just autocomplete your syntax. It builds entire applications, plans sprints, tests code, gives itself visual feedback, and even knows how to work in massive codebases. And Grigorij is still building — not despite the fact that it could replace him, but because of it.
We sat down with Grigorij after his Clean Coder demo at our GT internal workshop to dive deeper into his mindset, tech philosophy, and where he sees dev work heading in an AI-first future.
💭 “Honestly, I was too lazy to write the same kind of code over and over again.”
When asked what started it all, Grigorij answers without hesitation: “Laziness.”
“I didn’t want to spend another month building the product that might not even work. So I built something that could build it for me.”
That something became Clean Coder, an LLM-based system of agents that can write, validate, and iterate on entire codebases with minimal human input.
Grigorij built the product with an idea to promote his core philosophy: humans should focus on thinking, and leave repetitive execution to machines.
🛠 How Clean Coder works: beyond copilot
Most developers are familiar with AI assistants that offer autocomplete or snippet suggestions. Clean Coder goes several layers deeper.
It’s not a helper. It’s a system.
It consists of multiple collaborating agents:
A Project Manager that breaks features into tasks and populates them in a project tracker. Kind of a Scrum Master agent that organizes tasks, reassigns priorities, and handles project flow
A Coder that writes full-stack code across frameworks
A Debugger that runs static analysis and interprets Python errors
A Frontend Feedback agent that opens your app in a browser, takes a screenshot, and analyzes whether the UI is broken.
“We’re building an AI that doesn’t just write code. It understands project context, navigates thousands of files, and teaches itself from feedback.”
It can onboard itself to an unfamiliar codebase, write new endpoints, style buttons correctly, and even debug it’s own code.
Clean Coder isn’t a silver bullet — but it’s already hugely effective in the right contexts.
Here’s where it shines:
Rapid prototyping of MVPs
Greenfield projects with clear business logic
CRUD-heavy web applications
Automated refactoring and optimization of legacy code
Where it struggles? Complex logic, domain-heavy apps, or unfamiliar frameworks. But even there, Grigorij says it can get surprisingly far.
“On complex projects, I still validate and clean up what it generates,” Grigorij admits. “You need to understand the logic behind what it builds. It’s a tool, not a replacement — for now. But the AI gets smarter every week. And often it writes usable code in frameworks I’ve never touched.”
⚡ The long-term vision: AI that writes, fixes, and ships software — without you
Grigorij’s vision for Clean Coder isn’t to build a smarter autocomplete. It’s to redefine how software is made.
“In the future, I want to ask for a feature and get a working solution — tested, deployed, debugged. And if something breaks, the AI should fix it. I shouldn't have to step in unless I want to.”
He sees Clean Coder eventually functioning as a fully autonomous developer, capable of understanding business goals, generating solutions, and managing itself end-to-end.
In the next 2–3 years, the system is expected to:
Build and test features independently
Orchestrate collaboration between specialized AI agents
Navigate entire product lifecycles without human supervision
The long-term goal? AI systems that plan, code, test, deploy, and self-improve — while humans design the vision. It’s not science fiction. Grigorij is building it now.
🤔 So what happens to devs?
Grigorij has no illusions: AI will change the nature of development. But he doesn’t believe the role disappears — it evolves.
“The goal isn’t to replace developers. It’s to free us to do what we’re really good at — thinking in systems, solving complex problems, designing elegant solutions.”
In his view, developers will become strategists, architects, and product thinkers. People who define what needs to be built — not necessarily those who type it out.
His prediction? In two to three years, most development work will be handled by networks of AI agents — with humans supervising only the most critical layers. Looking ahead, Grigorij imagines ecosystems where autonomous agents do everything from market research to code generation to UX design — and they collaborate like agile teams.
“One AI will build your app. Another will test it. A third will fix the bugs. You’ll just talk to your agent, and it’ll handle the rest.”
If you're a developer curious about AI automation, Grigorij recommends trying Clean Coder — and following a few key practices to get the most out of the agent:
📌 Give the AI context: Use rules files or comments to explain architecture and logic.
📌 Document your code: “Readable code helps humans and AIs alike.”
📌 Start with side tasks: Let AI handle the simple stuff while you validate the output.
🛸 Final thought: build tools that set you free
Grigorij’s motivation hasn’t changed from the start: he wants to automate the boring parts of his job — so he can focus on what actually matters.
“AI isn’t here to take your job. It’s here to give you back your time.”
At GT, we believe great engineering starts with asking better questions — and giving people the space to explore the answers.
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