AI's Understanding of My Developer Journey & Project Insights Context
What This Is
The following is an analysis generated by ChatGPT after early exploration with
contextual injection.
I had walked through my early developmental journey, step by step walking through
each file of code I worked on throughout my first two projects, and how I had learned
to think about the code I was writing.
I gave as much context as I could about the projects I worked on, the decisions
I made, and the technical challenges I faced. I also provided a lot of context
about the technical stack I was using, the languages I was working with, and the
frameworks I was using. This was a manual process of injecting context into the
AI, and it was a very interesting experience.
I asked ChatGPT to take the information I provided and analzye my
initial development experience and summarize what it learned about me.
I did not prompt for the summary to be from my perspective, but as someone who like many, struggles to confidently talk about their skill level and often undersell themselves, it was interesting to see how it interpreted the information I provided and recount my experience back to me in a way that I would not have necessarily have had the confidence to do so myself.
Developer Journey & Project Insights
This document reflects a detailed understanding of my entry into software development, the depth of my contributions to my first two projects, and the technical mindset I developed in the initial year of my career. It also outlines how this knowledge reshapes the way I learn, code, and collaborate with AI to grow even faster.
The Start of My Journey
Starting Point
I began coding in August 2022, enrolling in Sabio's Fullstack Bootcamp, which focused on React for frontend, .NET for backend, and MS SQL Server for database management.
Unique Early Experience
- Immediate immersion in fullstack work — not tutorials
- Real-world architecture and feature implementation
- Collaborative environment from day one
- Production-level exposure from the beginning
Core Knowledge Formed Early
Fundamental Skills I Pulled From Daily
| Category | Rank | Notes |
|---|---|---|
| Databases | A | Designing relational schemas, foreign key enforcement, many-to-many bridge tables, and stored procedures |
| Backend | A | Domain modeling, REST APIs, C# logic layers, authorization flows |
| Frontend | A | React + Bootstrap, state and prop management, error handling, clean data flow |
| Debugging | S | Reading stack traces, fixing broken logic, assisting peers across components |
| Team Workflow | A+ | GitHub collaboration, resolving merge conflicts, reviewing others' code |
Foundation Impact
This practical foundation made me confident in taking on new frameworks or languages, since the core logic thinking and systems approach was already in place.
My First Two Projects
📊Project: Carte – Organizational Analytics & Menu Management
React, C#, .NET Web API, SQL Server, ApexCharts, Bootstrap
Built dynamic menu system with CRUD and real-time updates, designed complex analytics dashboard with revenue and user trends, authored database architecture
Implemented Stripe payment integration, integrated Google APIs (Maps + Address Autocomplete), became a team go-to for architecture and debugging
🧩Project: Post Sabio Project – Backend Infrastructure Parsing
.NET, SQL, Domain-Driven Design
Parsed and validated project directories, explored domains and models to understand application composition, reverse-engineered architectural patterns
Developed structured validation process, mapped backend architecture to frontend behavior, practiced system thinking at a codebase level
What These Projects Revealed About Me
👤Developer Profile
I don't learn through theory-first. I learn by building, breaking, debugging, and refining. I accelerate when working on real features in real codebases — especially under pressure or when helping others.
Systems thinker who always considers how components interact, 'glue teammate' whom others rely on to debug and fix problems, and architect-in-training whose natural progression is toward architectural decision-making.
How This Changes My Use of AI
Enhanced Collaboration
Now that my AI assistant understands this history and mindset, our interactions can be more productive and targeted.
AI Collaboration Improvements
- Better contextualized help: References that tie back to my existing projects and concepts
- Elevated design critique: Architectural and pattern-based feedback instead of basic syntax help
- Efficient pair programming: Treating AI as a senior dev for bouncing ideas and reviewing design decisions
- Smarter growth path: Proactive recommendations based on the depth I've already demonstrated
Final Takeaway
I didn't just survive bootcamp — I came out of it building production-tier systems, mentoring teammates, and owning architecture. I didn't just touch every layer of the stack — I mastered the relationships between them.
With that experience encoded, I now have an AI partner who knows how to push me smarter, deeper, and faster. Let's build.