Rogers Jeffrey Leo John, CTO of DataChat, shares insights on GenAI trends, agentic AI, SLMs, and building responsible, real-world AI systems. Welcome, Rogers. To start, can you share a bit about your professional journey and what led you to co-found DataChat?I’m Rogers Jeffrey Leo John, co-founder and CTO of DataChat, a natural language based no-code…
As large language models (LLMs) evolve from simple text generators to agentic systems —able to plan, reason, and autonomously act—there is a significant increase in both their capabilities and associated risks. Enterprises are rapidly adopting agentic AI for automation, but this trend exposes organizations to new challenges: goal misalignment, prompt injection, unintended behaviors, data leakage,…
Microsoft has taken a major leap into the future of web browsing with the launch of Copilot Mode in Edge, positioning it as the company’s first real step toward an AI-native browser. This marks a pivotal moment not just for Edge, but for the entire concept of what a browser can be in the era…
The landscape of AI foundation models is evolving rapidly, but few entries have been as significant in 2025 as the arrival of Z.ai’s GLM-4.5 series: GLM-4.5 and its lighter sibling GLM-4.5-Air. Unveiled by Zhipu AI, these models set remarkably high standards for unified agentic capabilities and open access, aiming to bridge the gap between reasoning,…
The White House just released the U.S. AI Playbook—formally titled “America’s AI Action Plan”—a sweeping, high-impact federal strategy that clarifies one thing: the United States is going all in on artificial intelligence. Whether you’re in Silicon Valley, leading a Fortune 500, or managing a critical government agency, the message is unambiguous: scale AI fast, dismantle…
In this tutorial, we walk through the complete implementation of an advanced AI agent system powered by Nomic Embeddings and Google’s Gemini. We design the architecture from the ground up, integrating semantic memory, contextual reasoning, and multi-agent orchestration into a single intelligent framework. Using LangChain, Faiss, and LangChain-Nomic, we equip our agents with the ability…
The landscape of artificial intelligence continues to evolve rapidly, with breakthroughs that push the boundaries of what models can achieve in reasoning, efficiency, and application versatility. The latest release from NVIDIA—the Llama Nemotron Super v1.5—represents a remarkable leap in both performance and usability, especially for agentic and reasoning-intensive tasks. This article provides an in-depth look at…
In this tutorial, we guide you through the development of an advanced Graph Agent framework, powered by the Google Gemini API. Our goal is to build intelligent, multi-step agents that execute tasks through a well-defined graph structure of interconnected nodes. Each node represents a specific function, ranging from taking input, performing logical processing, making decisions,…
The importance of memory in AI agents cannot be overstated. As artificial intelligence matures from simple statistical models to autonomous agents, the ability to remember, learn, and adapt becomes a foundational capability. Memory distinguishes basic reactive bots from truly interactive, context-aware digital entities capable of supporting nuanced, humanlike interactions and decision-making. Why Is Memory Vital in AI…
In this tutorial, we build a GPU‑capable local LLM stack that unifies Ollama and LangChain. We install the required libraries, launch the Ollama server, pull a model, and wrap it in a custom LangChain LLM, allowing us to control temperature, token limits, and context. We add a Retrieval-Augmented Generation layer that ingests PDFs or text,…