Similarities Aspect Core idea Delegate multi-step tasks to AI — it plans, executes, checks in, and delivers AI engine Both use Claude as the underlying model and share the same “agentic harness” — the system that allows the AI to use tools and the guardrails around how it functions Human-in-loop Both show you a plan before acting, require approvals for significant actions, and let you redirect mid-task Background execution Tasks run asynchronously — you can step away and come back to completed work MCP standard Both use the Model Context Protocol for connecting to external tools Status Both currently in…
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Read my new article “Context Window explained” in LinkedIn – https://www.linkedin.com/pulse/context-window-explained-praveen-nair-yif9c
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I was lucky to participate in the event last Saturday 14/Mar/2026.
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I agree here that the comparison is between apples and oranges, but it has at least a near-similar round shape. Cosmos DB: Account -> Database -> Container -> Item/Document Firestore: Database -> Collection -> Document -> Subcollection -> Document -> …
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In the past, the tech world was obsessed with the raw potential of artificial intelligence. Today, the conversation has moved entirely to verifiable integrations, strict governance, and clear returns on investment. Building a massive system is only half the battle. The other half is proving the system is secure, compliant, and financially viable for the business. Let us explore the core views that shape how large scale systems are built and validated in the real world. Read full article: https://www.linkedin.com/pulse/architecting-enterprise-ai-reality-blueprint-integration-praveen-nair-72sdc
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Completed two courses from AMD AI Academy: AI Agents 101: Building AI Agents with MCP and Open-Source Inference AI Agents 201: Design to Deployment: A Guide to Multi-Agent Systems A good, hands-on course on Building AI Agents with MCP and Open Source Inference. Course uses vLLM, Qwen3, Pydantic-AI, and MCP. The best part is, you get a fast AMD GPU for free to do the exercises. You can join AMD AI Developer Program here – https://www.amd.com/en/developer/ai-dev-program.html
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I recently worked on an AI project that was quite unusual. It was about analyzing the past, present, and future of a person’s life. Yes, you guessed it right. Astrology. More specifically, this project focused on Vedic Astrology using the Kerala system. As someone who builds AI systems for a living, and being someone who loves solving challenging problems, I was excited. Modern AI tools make it incredibly easy to spin up apps quickly. But here is the reality check. The whole concept of “vibe coding” is still evolving, especially when it comes to complex data analysis, probabilistic workflows, and…
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AI agents are often described as autonomous, adaptive, and intelligent. This can create the impression that they automatically learn from every interaction and continuously improve on their own. In reality, most AI agents do not learn automatically. They must be explicitly designed, configured, or programmed to learn. Understanding this distinction is critical when building real-world AI systems. Key Takeaways: Contrary to popular belief, AI agents do not automatically learn from interactions. Once deployed, their internal models are “frozen” unless explicitly engineered otherwise. An agent remembering your name or past context is simply data retrieval (eg. RAG), not learning. True learning…
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Sharing my Python revision notes here. I created these while revisiting the fundamentals and thought they might be useful for beginners or anyone brushing up on the basics. GitHub URL: https://github.com/ninethsense/code-share/tree/master/Python