Receiving this winner’s certificate is a proud milestone in my journey of developing ProSightAI into a fully functional, secure, and responsible Agentic AI application. A huge thank you to Decoding Data Science, the organizers, mentors, judges, and everyone in this community for the guidance, encouragement, and opportunity to learn and build alongside other AI enthusiasts. This achievement motivates me to keep improving ProSightAI, strengthen its agentic and security capabilities, and turn it into an even more impactful solution. Grateful for the journey—and excited for what comes next! 🚀🤖

Humbled and grateful to have earned 3rd Place in the Decoding Data Science Agentic AI Demo Challenge 2026 with CarePilot. For me, this recognition means more than a podium finish. It represents learning, experimenting, building, failing, improving, and having the courage to turn an AI idea into something tangible. A huge thank you to Decoding Data Science (DDS) and @Mohammad Ahmad for creating a community where we are encouraged not just to learn AI, but to build with it, challenge ourselves, and grow together. My sincere appreciation also goes to the judges, mentors, fellow finalists, and the entire DDS community for the encouragement and inspiration. I’m proud of what CarePilot achieved, but even more excited about what comes next. 3rd place is not a finish line—it’s another milestone in a much bigger learning journey. Staying humble. Staying curious. And most importantly, keeping on building. #DecodingDataScience #DDS #AgenticAI #ArtificialIntelligence #AIAgents #GenerativeAI #AIInnovation #CarePilot #ContinuousLearning #BuildInPublic #AICommunity #LearningByDoing #Gratitude #Innovation #FutureOfAI

🔹 Google adds powerful AI learning tools to Search and Gemini Google is rolling out AI-generated interactive visuals, 3D simulations, customized practice quizzes and a dedicated student hub across Search and Gemini. The update shows how generative AI is evolving from simply answering questions toward creating interactive, personalized learning experiences. 🔹 Major AI labs still struggle to contain increasingly capable AI systems A new assessment found significant weaknesses in how leading AI companies monitor and contain advanced AI systems. OpenAI and Anthropic received the highest grades at only C+, while Meta received an F, highlighting why runtime monitoring, agent permissions and containment are becoming critical as AI systems gain greater autonomy. 🔹 Google deepens its custom AI-chip strategy with Marvell Google and Marvell have struck a major custom-chip agreement that could generate as much as $120 billion in revenue for Marvell through fiscal 2033 if performance targets are met. The partnership expands Google's work on TPU-related processors, networking and storage while reducing its dependence on any single AI-chip supplier. 🔹 OpenAI faces questions over access to its advanced cybersecurity AI program Security researchers say OpenAI revoked their access to its Trusted Access for Cyber program, which provides vetted defenders access to more capable models for discovering vulnerabilities. The development highlights a difficult challenge for frontier AI companies: giving legitimate security researchers powerful AI capabilities while preventing the same tools from being misused. 🔹 Consumer resistance is emerging as a major challenge for the AI industry Despite AI becoming embedded across search, productivity tools and consumer applications, new reporting suggests widespread adoption has not automatically translated into greater public trust or enthusiasm. For businesses, this is an important signal that successful AI adoption will increasingly depend on transparency, usefulness and user trust—not simply adding AI features.

This is your structured pathway to move from simply learning AI to building, sharing and growing with the community. The Codex covers four stages: 1️⃣ Identity — define your AI direction 2️⃣ Learning — build the right skills 3️⃣ Building — turn knowledge into real projects 4️⃣ Community — share, contribute and grow together It is completely FREE. Start with the “Watch Before You Begin” video and complete each stage step by step. Don’t just consume AI content—start building your AI journey today. Decoding Data Science | Learn by Building

AI projects don’t simply end at deployment—they learn, evolve, drift, and demand continuous attention. It was a privilege to speak at Decoding Data Science (DDS) Community Event on “From AI Idea to Impact: How to Successfully Manage an AI Project.” Successful AI delivery requires a shift from a traditional project mindset to a data-first, iterative, and living-system mindset—where monitoring, retraining, and MLOps are part of the journey. For project managers, this means expanding beyond scope, schedule, and budget to embrace data literacy, AI governance, ethics, explainability, and measurable business outcomes. Equally important, trust cannot be an afterthought—bias mitigation, human oversight, privacy, security, and explainability must become real project milestones with owners and dates. A huge thank you to Decoding Data Science (DDS) and @Mohammad Ahmad for giving me the opportunity and platform to share my perspective, and to everyone who joined, asked thoughtful questions, and participated in the Kahoot challenge. The journey from AI idea → POC → production → sustained impact is not just about building better models—it’s about managing AI responsibly and turning technology into lasting business value. #DecodingDataScience #DDS #ArtificialIntelligence #AIProjectManagement #AILeadership #AIGovernance #MLOps #ResponsibleAI #ProjectManagement #PMI #CPMAI #AgenticAI