Mirza Yasir Abdullah Baig

What if AI agents could do more than generate code? What if they could plan, build, test, and independently verify their own work?

That’s the idea behind LampLighter.
The workflow has three key stages:

• Coordinator: Plans and organizes the task.
• Builder: Generates code and tests.
• Verifier: Re-runs the tests on a clean copy and accepts or rejects the result.

Built with Python, Streamlit, and Groq, LampLighter explores how specialized AI agents can work together to make software development more systematic and reliable.

This hackathon was an opportunity to explore agentic AI, multi-agent collaboration, automated testing, and evidence-based verification.

My biggest takeaway: generating code is only part of the challenge. Building a workflow that checks whether the code actually works is just as important.

Project submission:
lnkd.in/ddfM5XJ3
GitHub repository:
lnkd.in/dJRgHDVN
Thank you to lablab.ai, WeAreDevelopers , and BAND for creating an opportunity for builders to explore the future of AI-powered software engineering.

Thanks to Hamna Munir , Zartaj Nadeem and Nouman Arshad…

I'm Mirza Yasir Abdullah Baig, CEO of Yasir Insights in Lahore, Pakistan. I'm an AI and machine learning engineer and a Kaggle Grandmaster. My work covers generative AI, LLMs, RAG and agentic systems, plus ML projects and web development for clients. On GitHub I share things like a YOLOv11 and SAM2 tumor detection project and my handwritten notes on Python, ML and math.

Right now I'm working across a wide stack: LangGraph, the OpenAI Agents SDK, Claude Code, Cursor, Langfuse for tracing, and open models through Ollama and vLLM. I'm still figuring out which of these tools are worth keeping in a production workflow.

What I'd love from this community: an introduction to founders or teams building agentic products who are open to working with an AI engineer on a real project. I'm happy to share what I've learned in return.