Hi, I’m Recep Emre Erçetin. I work across technology and product, usually in places where software, user experience, product decisions and the systems behind them need to come together.

My background is deeply technical. I started from programming and software development, and over more than a decade of hands-on work that expanded naturally into product development, UX/UI, system architecture, analytics, digital strategy and the operational side of building and improving digital products. I never really experienced those areas as completely separate disciplines. In real projects, decisions made in one of them almost always affect the others.

That is still how I approach my work today. I like understanding the problem first, working out what actually needs to be built, deciding how the product or system should behave, and then choosing the right technical approach for it. Sometimes that means writing software. Sometimes it means designing a product flow, building an interface, creating an analysis system, working through architecture or improving something that already exists. Most projects eventually involve several of those things at once.

I’m comfortable working across different technologies because I don’t treat programming languages or frameworks as the foundation of software engineering. The fundamentals come first: algorithms, data structures, programming concepts, architecture, reading unfamiliar code and understanding how systems behave. Specific languages and tools matter, of course, but they are choices made around the problem rather than the identity of the person solving it. That also makes it much easier to move into a different stack when a project calls for it.

A large part of my work revolves around turning ideas, requirements and sometimes fairly messy problems into working digital products and systems.

That can include web applications, software products, platforms, internal tools, automation and analytics systems, product interfaces, prototypes, AI-enabled applications and the infrastructure around them. I also work on existing products where the job is less about starting from zero and more about understanding what is already there, finding what is holding it back and improving the product without breaking the parts that already work.

UX and product thinking are closely tied to that process for me. I care about what happens behind an interface, but I care just as much about whether the interface makes sense to the person using it. A technically correct system can still be a bad product, just as a polished interface can hide poor architecture underneath. The work becomes much more interesting when both sides are considered together.

My background in analytics, growth and digital measurement also affects the way I build. Event tracking, user behavior, experimentation, SEO, GEO, attribution and performance data are not things I only look at after a product has been launched. They can influence how a product is structured, what should be measured and what questions an analysis or automation system should be able to answer in the first place.

AI has become an important part of my professional workflow, but I don’t treat it as a separate identity or as a substitute for understanding the work.

I use it across research, analysis, software development, product exploration, prototyping, testing, documentation and production. Depending on the project, it may help examine a problem, accelerate development, work through alternatives, review implementation, automate repetitive parts of a process or become an actual component of the product itself.

The important part for me is keeping the human in control of the system. I decide what problem is being solved, how the work is structured, which direction is worth pursuing and whether the result actually makes sense. AI can make parts of that process dramatically faster, especially when several disciplines are involved, but speed is only useful when the person directing it understands what is happening.

That is also why I work with different models, development tools, coding agents and AI-assisted environments rather than tying my workflow to one product. The tools change quickly. The underlying job does not: understand the problem, make good decisions, build carefully, verify the result and improve it.

Right now I’m especially interested in what happens when AI is integrated into real software and product development rather than added as a layer of novelty.

I’m exploring AI-assisted development workflows, intelligent product and analysis systems, automation, developer and product tooling, UX experiments and different ways of using AI across the lifecycle of a digital product while keeping the architecture and decision making human-directed.

I’m also gradually bringing more of my work into public repositories. GitHub has historically been more of a working tool for me than a public portfolio, so this profile will grow as I release projects, experiments and open-source work that are actually worth showing rather than filling it with repositories just to make it look busy.

My development stack changes depending on what I’m building. I regularly work with PHP, JavaScript, Python, C++, HTML, CSS, React Native, MySQL and Microsoft SQL Server, along with APIs, Git, GitHub, cloud services and the supporting technologies needed to connect those pieces into complete applications and systems.

I’m comfortable moving beyond that set when a project requires it. After years of working with different codebases and platforms, I care more about understanding architecture, logic and the conventions of a technology than forcing every problem into the same stack.

On the product and experience side, I work with Figma, Adobe Photoshop, Adobe Illustrator, Pixelmator Pro and Unreal Engine, alongside AI-assisted design and production tools. Unreal Engine remains particularly useful when an idea goes beyond a conventional interface and needs a more interactive or real-time experience.

AI in my workflow is less about one specific model or product and more about how different tools can be combined around a real problem. I work with AI-assisted development environments, coding agents, model and API integrations, automation, research and analysis workflows, and systems where AI becomes part of the product itself.

The tools I use change as the ecosystem changes, so I deliberately avoid building my workflow around a single vendor or model. What matters more is understanding where AI is useful, where conventional software is the better choice and how the two can work together without giving up technical judgment or control.

For analytics, measurement and growth systems, my work spans tools and practices around Google Analytics, Google Tag Manager, event tracking, dashboards, experimentation, advertising platforms, SEO, GEO, attribution and product analytics. I often use that knowledge while building analysis products and internal systems, not only for marketing work.

Product and project management are part of the same working process. Agile and Scrum practices, Jira, Miro and similar tools are useful when they help structure the work, but I try not to let the process become more complicated than the product itself.

Most of what I’m building, writing and experimenting with eventually appears somewhere across my website or social channels.

For professional conversations, collaborations or questions, you can reach me at info@recepemreercetin.com.