For me, it’s a simple commitment to keep learning, build practical solutions, experiment with ideas, and share what I learn along the way to the community . Looking forward to putting this into practice through small, meaningful projects.

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! 🚀🤖

🔹 NVIDIA tackles expensive AI model handoffs NVIDIA researchers introduced a cross-model KV-cache transfer technique that can reduce the compute and latency involved when agentic workflows switch between different models. This could be particularly valuable for long-running, multi-model agents where repeatedly processing large conversation histories becomes expensive. 🔹 Anthropic introduces CHIVE for investigating unexpected LLM behavior Anthropic researchers unveiled CHIVE, an agentic pipeline that discovers unusual model behaviours and tests potential explanations through counterfactual prompt experiments. Interestingly, the research found that several activation-reading interpretability tools did not outperform simply examining the model transcript for predicting these behavioural changes. 🔹 Slack brings AI coding agents into collaborative channels Slack Code brings agents including Claude Code, Devin, GitHub Copilot and Vercel's coding agent directly into Slack channels. Instead of AI coding remaining a private developer-agent interaction, teams can collectively observe, steer, review and collaborate around agent-generated work. 🔹 NVIDIA gives coding agents new skills for optimizing AI infrastructure NVIDIA has added an Agent Optimization Skillpack to its Dynamo repository, designed to help coding agents optimize AI inference deployments using techniques employed by NVIDIA engineers. It is another sign that coding agents are evolving from code-generation assistants toward specialized engineering agents capable of infrastructure and performance work. Happening today at 11AM GST: Vendor Lock-In in the AI Era: Owning the Seams + Builder Codex 📌https://nas.com/artificialintelligence/events/join-biggest-tech-community-ai-meetup-1783041366122 Happening this Today at 1:30 PM GST: MC06_ Building LLM Wrappers, Function Calls, and Data Integration 📌https://nas.com/artificialintelligence/events/nas-com-artificialintelligence-events-mc06-building-llm-wrappers-function-calls-and-data-integration

It is about creating the infrastructure that helps AI applications scale, perform reliably, and succeed in production. AI Engineers connect data, APIs, MLOps pipelines, deployment, and monitoring—turning promising prototypes into dependable platforms. The model powers the application. The AI Engineer builds the system that enables it to thrive. What is the biggest AI engineering challenge in your organisation?