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🚀 Backend Developer | Building Fast, Stable & Secure Systems

Hello! I’m Rizqi Mulki, a Backend Developer who thrives on crafting high-performance software that doesn’t just function—it excels. My mission? To engineer solutions where speed, stability, and security are non-negotiable, because in today’s digital landscape, cutting corners isn’t an option.

With deep expertise in PHP, Python, and Node.js, I design scalable backends that handle heavy traffic seamlessly. My database skills span MariaDB, MySQL, and PostgreSQL, ensuring data is not just stored but optimized for peak efficiency. On the infrastructure side, I’m at home in Linux environments and cloud platforms like AWS & GCP, deploying resilient systems with precision.

Beyond code, I believe in sharing knowledge and growing together. I hope this website serves as both a showcase of my work and a resource for fellow developers. Whether it’s refining APIs, optimizing queries, or hardening systems against threats, I’m passionate about writing clean, maintainable code that stands the test of time.

Let’s build something remarkable—fast, stable, and secure.

rizqimulkisrc@gmail.com | +628526865056


Latest Post

Multimodal LLMs: Integrating Text, Images, and Other Modalities

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LLM Alignment: RLHF, Constitutional AI, and Safety Training

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Fine-Tuning Techniques: LoRA, QLoRA, and Parameter-Efficient Methods

Introduction Parameter-Efficient Fine-Tuning (PEFT) telah merevolusi cara kita mengadaptasi Large Language Models (LLMs) untuk task-specific…
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Deep Dive into Transformer Attention Mechanisms

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Building Your First LLM-Powered Application

The rise of Large Language Models (LLMs) has opened up incredible possibilities for developers to…
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LLM Evaluation Metrics: How to Measure Model Performance

Evaluating Large Language Models presents unique challenges that distinguish it from traditional machine learning assessment…
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Retrieval-Augmented Generation (RAG): Enhancing LLMs with External Knowledge

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Working with LLM APIs: Integration and Development Basics

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Fine-Tuning vs. Prompt Engineering: When to Use Which Approach

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