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August 11, 2025 (9mo ago)

Projects & Lessons Learned — My Journey in Building Intelligent Applications

A showcase of my key projects, the technologies behind them, and the lessons I’ve learned building intelligent, scalable applications.

🚀 Projects & Lessons Learned

Over the course of my career as a full-stack software engineer, I’ve worked on projects that combine modern web development with AI, real-time systems, and data-driven decision-making. Here’s a closer look at some of them — what I built, how I built it, and what I learned.

🧠 Udeffy – AI-Powered Personalized Learning Platform

Goal: Help learners master skills faster through adaptive learning paths.

What I Built:

  • Backend in NestJS with a clean modular architecture
  • AI lesson generation pipeline with credit-based access control
  • Background processing with BullMQ and job event streaming via Server-Sent Events (SSE)
  • Frontend in Next.js with rich UI using Tailwind CSS + Shadcn UI

Key Challenges & Solutions:

  • Challenge: Generating diverse lesson formats (article, video, quiz, assignments) without blocking the main thread. Solution: Offloaded generation to BullMQ jobs and used event streaming for live updates.
  • Challenge: Personalizing learning without overwhelming users. Solution: Used survey-based pre-tests to tailor learning content dynamically.

Outcome: A scalable platform that can handle multiple concurrent learners with individualized AI-driven content.

🏡 LinkAI – AI for Smarter Real Estate Decisions

Goal: Provide actionable insights for property buyers, investors, and agents.

What I Built:

  • Property valuation and trend analysis engine
  • Integration with real-time market data APIs
  • Custom Copilot-style buyer agent powered by prompt engineering
  • Output guidance per focus key (e.g., price, schools, crime) for relevant sections

Key Challenges & Solutions:

  • Challenge: Providing accurate insights from volatile market data. Solution: Combined API data with AI summarization and strict business logic rules to ensure reliability.
  • Challenge: Keeping AI answers on-topic. Solution: Designed structured prompts with guardrails to avoid hallucinations.

Outcome: An intelligent assistant that produces market insights users can trust.

📍 AI-Powered Map Discovery Tool

Goal: Help users find places that match specific personal preferences.

What I Built:

  • Map-based search with conversational AI interface
  • Criteria filtering (e.g., “restaurants with a sunset view and vegan options”)
  • Favorites system for saving places
  • Next.js frontend with real-time map rendering

Key Challenges & Solutions:

  • Challenge: Translating vague user requests into actionable map queries. Solution: Used AI to parse natural language into structured location search criteria.

Outcome: A conversational location search that feels like talking to a local expert.

🔌 AI-Assisted Wiring Diagram Tool

Goal: Speed up wiring diagram creation for engineers and designers.

What I Built:

  • AI layout optimization for spacing and alignment of components
  • Assisted drag-and-drop UI for quick design
  • Export options for various industry-standard formats

Key Challenges & Solutions:

  • Challenge: Maintaining precision in AI-generated layouts. Solution: Implemented constraint-based algorithms that respect design rules.

Outcome: Reduced manual layout time significantly for technical teams.

📚 Lessons Learned Across Projects

  1. AI is only as good as the workflow around it — guardrails and validation are critical.
  2. Real-time features need strong architecture — event-driven systems scale better than polling.
  3. User experience is king — even the smartest AI fails if the UI is clunky.
  4. Scalable patterns matter — modular NestJS architecture + Next.js SSR is a winning combo.

💡 What’s Next

I’ll continue sharing deep dives into:

  • AI integration in full-stack apps
  • NestJS backend patterns
  • Real-time event streaming
  • Modern frontend performance tuning

If you want to follow along, keep an eye on my blog or connect with me: