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AI agents are getting smarter—but most still can’t talk to each other. That’s where Google’s Agent2Agent (A2A) Protocol comes in. It gives AI agents a shared language to collaborate, delegate tasks, and build smarter systems together. In this blog, we break down what A2A is, how it works, and why it could be the future of multi-agent AI.
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Prompt injection attacks trick AI into ignoring its rules. This guide explains what they are, why they matter, and how to defend your systems with smart prompt design.
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Large language models are powerful, but isolated. Model Context Protocol (MCP) changes that—creating a universal way to connect LLMs to real-world tools, data, and environments. Here's why it matters, how it works, and why it might just be the USB-C moment for AI.
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Large Language Models can do more than just generate text. With frameworks like ReAct, they can reason, act, and learn from external information—turning static models into dynamic agents capable of solving real-world, multi-step problems with precision and depth.
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Ever wondered how AI can think, plan, and act all on its own? In this deep dive, we explore the rise of LLM-powered autonomous agents—intelligent systems that don’t just follow commands, they take the lead. Whether you're an AI enthusiast or just curious about the future, this guide breaks it down in a way that's easy to follow and exciting to imagine.
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Ever had your Node.js app freeze up during a heavy request? I’ve been there—and it’s frustrating. That’s when I discovered worker_threads, and wow, game-changer. Let me show you how I fixed the bottleneck and made my API lightning-fast.
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