We built a self-learning security system that adapts and updates itself without overwhelming network infrastructure. Instead of centralised updates, our hierarchical distributed learning lets devices learn locally, escalate patterns through clusters, and trigger global updates only when truly needed — making security faster, more efficient, and scalable across massive networks.

Two Cities, Two Conferences, Two Completely Different AI Narratives January 2026 delivered a tale of two conferences that couldn't have painted more contrasting pictures of artificial intelligence's future. In Las Vegas, CES 2026 showcased gleaming robots, trillion-dollar chip announcements, and Jensen Huang's victory lap with tiny waddling robot companions. Just two weeks later in Davos, Switzerland, the world's elite gathered for the World Economic Forum's Annual Meeting, w

The $200 Billion Reality Check: Most AI Investments Still Aren't Paying Off Global AI investment has crossed $200 billion, yet only 6% of organizations report meaningful bottom-line impact from their AI initiatives (McKinsey & Company, 2025). This isn't a temporary learning curve. It's a fundamental execution gap separating winners from the massive majority stuck in what analysts call "pilot purgatory." 2026 marks the year this changes. The experimental phase is over. Product