# Why am I building Traceblade: Motivation and MVP Progress

User event tracking and logging is one of the most crucial instrument for understanding user behaviour and ensuring system stability. In my day-to-day job I found myself juggling multiple tools to connect the dots between a user event and related error. This process was not only time consuming but also frustrating, as the data was scattered across various platforms.  
While building **AI Assisted Grading Tool ,** I searched for solutions that could provide both user events and error logs(this is the main focus) in one place- **a unified view**. Unfortunately, I couldn’t find a tool that met this need without breaking the bank.  
Thats when I realised it would be much more efficient(and cost-effective) to build a system tailored to this exact problem- one that bridges the gap between user event tracking and logging. as someone balancing the roles of a product manager and a developer for AI Assisted Grading Tool, I knew this tool could save me time and effort while solving a widespread challenge.

**Building the MVP**

With Traceblade, I set out to bridge the gap between user events and logs. The first version focuses mainly on the fundamentals: capturing user events and logs independently. While the unified view is still in progress, the foundation is a critical step.

**Things I needed for a solid foundation.**

1. **An ingestion pipeline**
    
    The main focus is not just to ingest data into the system but to do it seamlessly. As my project uses React Native and Go, it was only natural that I created a **React Native SDK** and **Go-Gin middleware**, which need to be initialized with an API key configured in the Traceblade project settings.
    
2. **The Interface**
    
    For the MVP, I opted for **separate views (events and logs)**, as this allows me to build a solid foundation before tackling the complexities of a **unified experience**.
    
    **Natural Language Querying**
    
    One of my favorite features is **natural language querying**, which will make it easy to search and filter data using simple, human-readable queries. This feature, demonstrated briefly at the [14-second mark of the video,](#) this can go beyond raw data visualization and provide actionable insights.
    

%[https://www.youtube.com/watch?v=UDDF18GYPeE] 

## **The Next Steps**

So far, I have captured the basics, but there is still so much more to do. My focus for the next milestones includes:

* **Unified Timeline View**: Combining user events and logs into a single interface to understand the ripple effects of user interactions across services.
    
* **Advanced Filtering**: Allowing users to fine-tune searches across events and logs.
    
* **User Paths/Journeys**: Analyzing and presenting user journeys within an app.
    
* **Scalability**: Ensuring Traceblade can handle larger datasets efficiently.