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AI is everywhere right now. New models, new benchmarks, new capabilities every week.
I decode data, craft AI solutions, and write about everything from algorithms to analytics. Here to share what I learn and learn from what I share. 🚀 Data Scientist | AI Enthusiast | Building intelligent systems & simplifying complexity through code and curiosity. Sharing insights, projects, and deep dives in ML, data, and innovation.
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But here’s the uncomfortable truth:
Using an LLM API is not impressive anymore.
Anyone can do it.
And honestly, most people are doing the same thing.
Call an API → write a prompt → get a response.
That’s not system building. That’s just usage.
At some point, it hit me:
There’s nothing creative about just using an LLM API.
Unless you’re training or fine-tuning models yourself, you’re operating at the same level as everyone else.
The real differentiation is not the model.
It’s everything around it.
How you structure the system
How you control execution
How you manage data flow
How you handle reliability, cost, and scale
That’s where actual engineering begins.
I started with a simple but frustrating problem:
Getting reliable, unbiased, and well-researched news is hard.
Not just headlines—but actual understanding.
Things get messy quickly:
News is scattered across sources
Bias is everywhere
Verifying authenticity takes effort
Exploring a topic deeply requires jumping across multiple links
Now try answering something like:
“What are the latest global developments in AI regulation, and what do they actually mean?”
This is not a single search.
This is a multi-step research process.
You need to:
Discover relevant articles
Extract sources and links
Read and validate content
Follow deeper references
Synthesize everything into a coherent answer
This is where naive “LLM + prompt” approaches fail.
I stopped thinking:
“How do I get a better answer from the model?”
And started thinking:
“How do I build a system that can arrive at a better answer?”
That’s a completely different problem.
Instead of a single AI component, I broke the system into layers.
Handles:
Decision making
Multi-step execution
When to fetch data
How to synthesize results
Handles:
Fetching latest news
Scraping websites
Returning structured and unstructured data
Under development...
Handles:
Authentication (JWT)
Rate limiting
User access control
System exposure
At a high level:
The agent thinks
The MCP server executes
The gateway controls
The database remembers
This is where things started to click.
Once responsibilities are separated:
You can scale components independently
You can secure the system properly
You can control cost and abuse
You can evolve each part without breaking everything
Most “AI projects” skip this entirely.
That’s why they don’t go beyond demos.
I could have used Python again. But, I didn’t.
Because this layer is different.
This is not about AI.
This is about control and infrastructure.
Go fits better here:
Lightweight and fast
Strong concurrency model
Ideal for API gateways and control services
This was less about learning a new language, and more about choosing the right tool for the role.
This is not just a “news agent”.
It’s evolving into:
A modular LLM system for controlled, iterative research workflows
Where:
reasoning is separated from execution
execution is separated from control
and everything is designed to scale
A few things became very clear during this process:
LLMs are the easiest part of the system
System design is where the real complexity lies
Control and safety are not optional
Separation of concerns is not over-engineering—it’s necessary
Completing the Go-based gateway
Integrating all components
Deploying the system on AWS (likely ECS)
Building a UI layer on top
This started as an AI project.
It turned into a system design exercise.
And honestly, that’s where the real learning happened.
