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Title: RFID Clone Attack Mitigation & Rolling Code Dashboard
Role: Python Developer & IoT Security Scripter
Description: Built a security simulation dashboard using Python Flask and cryptographic rolling code authentication to detect and mitigate RFID cloning and replay attacks in access-control systems. Features real-time log visualization and threat alerting.
Tools / Skills: Python, Flask, Cryptography, Security Auditing
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Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
Project Title: AI Perfume Product Design
Description:
I created these perfume designs using Ideogram AI. I experimented with different colors, backgrounds, lighting, and bottle styles to give each design a unique and premium look. My goal was to create attractive product visuals that could be used for social media and advertising.
Tools Used: Ideogram AI
Skills: AI Image Generation, Product Design, Advertising Design, Creative Direction
Project Type: Personal Portfolio Project
Building software with AI requires more than a good prompt.
I've been developing my own Autonomous Engineering Harness to structure how I build software with AI.
The idea is simple: instead of relying on AI to generate code one task at a time, I wanted a more structured process that connects specifications, task planning, implementation, code review, and QA.
The harness organizes the workflow into clear stages:
- Specifications: define what needs to be built.
- Task planning: break requirements into manageable units.
- Implementation: coordinate AI-assisted development.
- Review: check the result against the intended requirements.
-QA: validate the software before considering the work complete.
The goal isn't to make AI autonomous for the sake of it. It's to make AI-assisted engineering more structured, repeatable, and easier to validate.
I'm applying this approach to my own projects and continuing to refine the framework as I learn what works in practice.
I wrote more about the approach, the engineering decisions behind it, and how the framework works on my website.
Read the full story: https://yitsymosqueda.com/autonomous-engineering
I'd carry acceptance criteria from the spec into QA as explicit checks, with a result attached to each one. When the spec changes mid-build, does the workflow flag which completed tasks and QA results need to be revisited?