Topics / Tracks

In-depth AI Technologies and Technical Fundamentals
A track covering fundamental requirements, technical architecture, and trends such as Agentic AI.

Subtopics:

• Transformers, agentic workflow, and AI autonomous systems
• Engineering fundamentals: Why a Solid Understanding of Core Coding Remains Crucial in the Age of AI
• Retrieval-Augmented Generation (RAG) and vector databases
• Tooling and frameworks (LangChain, HuggingFace, PyTorch, Langfuse, etc)

Examples:

• Stop just using Copilot—it’s time to build your own AI agent.
• Vibe Coding: A Toy or a Threat, and Where Do the Limits Lie?

Innovative Use Cases of AI in Business & Industry
Focus on the practical implementation of AI that delivers value.

Subtopics:

• Process automation and predictive analytics in operations
• Cross-sector adoption of AI (healthcare, finance, education, and others)
• Organizational, cultural, and technical challenges in scaling AI within industries

Examples:

• From PoC to Production: Scaling AI to automate supply chains in manufacturing
• AI in Traditional Retail: Boosting local business revenue through predictive analytics
• Beyond the Hype: How we integrated AI into legacy systems without breaking them

AI Impact in Society, Security, & Inclusivity
Highlighting regulatory issues, technological inclusivity, and data security.

Subtopics:

• AI security: model jailbreaks, prompt injection, and new security vulnerabilities
• Privacy-preserving AI (federated learning, data privacy regulations)
• Bias, fairness, and ethics in societal AI implementation

Examples:

• Quick code, quick bugs: How to secure AI-generated code
• Who takes the blame? Accountability, ethics, and mitigating bias when AI makes mistakes
• Democratizing tech education: Introducing computational thinking and coding for kids

Technology Leadership, Product, & Developer Mindset
Addressing developers' concerns as the role of engineers shifts from writing code to focusing on architecture, product, and business.

Subtopics:

• The Evolving Developer: Navigating careers and mindset shifts to overcome AI insecurity
• Balancing Tech & Business: The role of Tech Leads and CTO in adopting AI efficiently
• Engineering Leadership: Building and leading high-performing tech teams in the automation era
• Refactoring legacy systems without disrupting users

Examples:

• The Junior in 2026: Is the door closing, and how do you break through
• AI Dependency vs. Fundamentals: How to use AI without letting your core skills erode
• From insecure to invaluable: A roadmap for reskilling and personal branding
• ⁠Sustainable Engineering: Prioritizing mental health, physical well-being, and burnout prevention in tech careers