A full stack engineer building toward AI-native systems.
I'm Farkhan Nabiel Makarim, a software engineering student at Politeknik Negeri Lampung focused on building full-stack systems that are useful, reliable, and thoughtfully designed.
I enjoy working across the stack — from modeling data and building APIs to crafting interfaces and interactive experiences. I've built business applications, e-commerce systems, LMS platforms, and immersive web experiences with WebGL. I'm now exploring how AI can become a deeper part of the systems I build, not just another feature added on top.
I care about what happens beyond the demo: how a system behaves under real constraints, how easily it can evolve, and whether people can trust it to do what it is supposed to do.
Four commitments behind every system
- 01
Systems thinking
I design for the whole, not just the feature. Data flow, failure modes, architecture, and the seams between components deserve the same attention as the interface.
- 02
Software engineering
I value clear boundaries, maintainable code, and pragmatic engineering decisions. The goal isn’t to make a codebase clever — it’s to make it understandable and easy to evolve.
- 03
Experimentation
AI evolves quickly, so I learn by building. I prototype, test ideas against real constraints, and let what proves useful guide what moves toward production.
- 04
Problem solving
I start with the actual constraint, not just the requested feature. Good engineering often means finding a simpler, sharper solution to the problem underneath the brief.
From making things work to making them hold.
I started with a simple satisfaction: making something work.
That gradually became a habit of building — applications, academic projects, client work, and interactive products that pushed me across different parts of the stack.
Over time, the question changed from "Can I build this?" to"How should this be built so it keeps working?"
That shift shaped how I approach engineering today. I care about the decisions behind the implementation: the architecture, the trade-offs, the failure cases, and the experience on the other side.
I'm still early in that journey, but the direction is clear — becoming an engineer who can take an ambiguous problem, turn it into a working system, and increasingly use AI to make those systems more capable.
What I bring to a team
Full Stack
- Frontend to backend
- Interfaces, APIs, databases, and deployment working as one
AI Systems
- LLMs, retrieval, intelligent workflows
- AI integrations built around real problems
Architecture
- Domain modeling
- API design, data architecture, and pragmatic system decisions
Product Engineering
- Turning ideas into working products
- From the first model to production
Experience
- Thoughtful interfaces
- Interaction, accessibility, and performance
Exploration
- Prototyping
- Emerging technologies and experimental interfaces
Have a system worth building well?
I work best with founders, CTOs, and teams who care about building things that remain useful after the demo ends.