Founder | AI Engineer | Builder

I build AI systems at Teckas Technologies and document what I learn along the way — the concepts, the implementation details, and the mistakes. This profile is less a resume and more a running log of that process.

Right now, my focus is AI Engineering — going from LLM fundamentals to systems that actually run in production. I'm working through:

• Large Language Models (LLMs)
• Prompt Engineering
• Context Engineering
• Embeddings
• Vector Databases
• Retrieval Augmented Generation (RAG)
• AI Agents
• Tool Calling
• Model Context Protocol (MCP)
• Multimodal AI
• AI Evaluation
• Production AI Systems

I don't learn a concept until I've built something with it. If it's listed above, there's a project attached to it, in some stage of "working" or "not yet."

LLM Fundamentals
      ↓
Prompt Engineering
      ↓
Context Engineering
      ↓
Embeddings
      ↓
Vector Databases
      ↓
Retrieval Augmented Generation (RAG)
      ↓
AI Agents
      ↓
Tool Calling
      ↓
Model Context Protocol (MCP)
      ↓
AI Evaluation
      ↓
Multimodal AI
      ↓
Production AI Systems

Every stage on this roadmap gets the same treatment:

1. Theory — understanding the concept properly, not just the API surface
2. Practical implementation — a working build, not a tutorial follow-along
3. GitHub project — public, so the work is checkable
4. Lessons learned — what broke, what I'd do differently

Nothing here is marked finished until it's actually finished. Statuses will update as repos go live.

I believe the best way to learn AI is by building it, not by reading about it.

Understand → Build → Break → Debug → Improve → Share

That's the loop. Repeat per concept.

AI Engineering Practical breakdowns of AI concepts — how they work, why they matter, and what it actually takes to implement them.

Building in Public Projects, experiments, and the failures that came with them. Lessons included, not edited out.

Software Engineering Backend work, system design decisions, and the engineering lessons behind them.

Founder Journey What running Teckas Technologies has taught me — the practical, unglamorous parts included.

AI LLMs · RAG · AI Agents · Embeddings · Vector Databases · OCR

Backend Python · FastAPI · Node.js · TypeScript · PostgreSQL

Frontend React · Next.js

Cloud AWS · Docker · CI/CD

Other Blockchain · Solidity · Web3

I'm building Teckas Technologies, a software development company creating AI-powered products and solutions. I started my career in 2018 as a software engineering intern, and the path since then has run through backend development, blockchain and Web3, and now AI systems — which is where most of my attention is today.

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Building in public. Learning continuously. Sharing the journey.