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Backend & DevOps engineer building APIs and deploying cloud apps with Python, JavaScript, AWS, and DigitalOcean. I write practical tutorials to help developers ship production-ready software.
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I’ve been thinking about writing for a long time, but like many developers, I kept postponing it.
As a backend engineer, my days are usually spent in the "deterministic" world—where Input A should always lead to Output B. I build APIs, fix bugs, and optimize cloud infrastructure. But lately, the industry has shifted. We aren't just building CRUD apps anymore; we are building Intelligent Systems.
I realized that "later" never comes, so this is me starting—documenting my transition from a traditional backend developer to an AI Engineer.
I primarily work with Python and JavaScript, but my focus has evolved. While I still love a clean PostgreSQL schema, I’m now spending more time thinking about:
Vector Databases & RAG: How to give LLMs a "long-term memory" that actually scales.
Agentic Workflows: Moving beyond simple prompts to autonomous agents that can execute tasks.
LLMOps & Deployment: Getting AI out of the playground and into production environments like AWS and DigitalOcean.
I work full-time as a software engineer, so JuCodes isn't about pretending I have all the answers. It’s a "Build-in-Public" lab where I share the messy, non-deterministic parts of AI engineering that you won't find in a polished documentation page.
The "AI hype" is everywhere, but there is a massive gap between a cool demo and a production-ready AI feature. I created this space to bridge that gap.
Writing helps me:
Codify My Stack: Deep-diving into the tools (like Pinecone, LangChain, and Gemini) that I use every day.
Solve for Reliability: Documenting how to handle AI hallucinations and latency in the backend.
Share the "System" View: Focusing on the architecture, not just the prompt.
On JuCodes, expect deep-dives into:
AI Orchestration: Using Python and Node.js to manage complex LLM chains.
Production-Grade RAG: Strategies for data chunking, embeddings, and retrieval.
DevOps for AI: Deploying and monitoring models in real-world environments.
The AI Engineering Stack: Reviews of the tools and APIs that actually survive a stress test.
This blog is for engineers, not just enthusiasts. If you are a backend-focused developer trying to figure out how to integrate AI into your existing stack without breaking everything, you’re in the right place. We focus on practical insights and real code over hype.
This is just the beginning. The goal is to build a useful corner of the internet for developers who care about engineering AI properly.
If you’d like to follow along, feel free to bookmark the blog or follow me here on Hashnode.
Welcome to JuCodes.
