AI • Cybersecurity • Automation — Thoughtful engineering, responsible research, elegant solutions.

I design and build intelligent systems and secure software with a focus on reliability, privacy, and responsible disclosure. I combine applied machine learning, automation, and large-scale system design to deliver pragmatic research and production-ready tools.

I value:

• Clear thinking and careful implementation
• Ethical security research and coordinated disclosure
• Reproducible experiments and robust infrastructure

💻 Making machines think, breaking security walls.
🚀 AI | Cybersecurity | NeuroTech | Ethical Hacking
😏 Sometimes I build, sometimes I hack. Always I win.
📍 Find me where the code runs & the fire burns.
💻 Building AI that thinks, learns & dominates.
🔐 Breaking barriers in security & automation.
😏 Living in code, thriving in intelligence.
👉 Work Hard, Hack Smart, Stay Untraceable.

• Machine Learning & AI — model prototyping, inference orchestration, prompt engineering
• Cybersecurity & Red-team (Ethical) — vulnerability research, penetration testing with responsible disclosure, secure design
• Automation & DevOps — CI/CD, containerization, orchestration (Docker, Kubernetes), observability
• Web & Backend — Flask / FastAPI, Python, REST APIs, scalable architectures

A modular data collection and enrichment platform designed to pull, normalize, and serve data for downstream ML pipelines. Focuses on reliability, data quality, and observability.

A framework for hosting multiple agent personas & models, with pluggable backends for commercial and open-source LLMs. Emphasizes safe defaults, rate limiting, and auditable logs.

Tools and notes from responsibly conducted assessments and red-team exercises. All findings are handled via coordinated disclosure channels.

Project details available in individual repos. For sensitive materials or exploit code, reach out for responsible access.

I build with intent and operate with integrity. The goal is not spectacle — it is dependable systems and clear improvements. I practice and promote:

• Responsible disclosure and ethical research
• Test-driven development and reproducibility
• Documentation, observability, and maintainability

• Languages: Python, JavaScript/TypeScript, Bash
• ML: PyTorch, Hugging Face ecosystem, model orchestration tools
• Web: Flask, FastAPI, Next.js (when needed)
• Infra: Docker, Kubernetes, Nginx, MySQL/Postgres, Redis
• CI/CD & Monitoring: GitHub Actions, Prometheus, Grafana

• Open to collaboration on ML systems, automation pipelines, and security engineering projects.
• For vulnerability reports or sensitive matters, please use coordinated disclosure channels and include reproduction steps, impact, and suggested mitigations.
• I prefer written introductions with scope and expected outcomes.

I do not publish exploit code or step-by-step instructions for wrongdoing. When publishing security research, my priority is to help defenders fix issues and to share findings responsibly so the community benefits.

— Professor Johnny — building thoughtful AI and secure systems.