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
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I will be demonstrating a “Cache-Aside” pattern, which is a basic Redis integration to an app. What it basically does is: Find the GitHub code – https://github.com/ninethsense/code-share/tree/master/RedisSample Prerequcites Step 1: Setup Docker with Redis Use the below docker-compose.yml Next, you can create the image using command below: This is my command window looks like: Additionally, you can verify on Docker Desktop if you use one Optionally, you can check in the browser if Redis Dashboard is working – http://localhost:8001/ Step 2 – .NET Program Create your .net project using Visual Studio Code or .NET CLI. You require a dependency to…
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Every technology feels like rocket science until you take the first step. I am trying to demonstrate a quick tutorial on setting up Kafka and writing two programs to consume from it. I am purposefully ignoring the what and why of Kafka and its event-driven architecture behaviour, as you can find tons of tutorials and interview material on that. I will be using Docker Desktop to host Kafka. You can find my Visual Studio solution on GitHub – https://github.com/ninethsense/code-share/tree/master/KafkaIntegrationSample Step 0: Prerequisites Step 1: Setup Kafka We will be using KRaft method instead of old Zookeeper. Use the below docker-compose.yml…
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I have been hearing about CrewAI for a while, and today I decided to spend some time exploring it. I must say, it genuinely impressed me. Within a minute, I was able to create a simple Vedic Astrologer agent. If you are curious, watch this video I recorded: While most of us are already aware that AI tools significantly reduce development time, what stood out to me here is something deeper. These tools are also transforming how AI systems themselves are built. The ability to download the generated code and customize it further makes the experience even more powerful. By…
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List of Agentic AI Patterns and Architectures. This is still an evolving field, so the list is updated as of 04-Jan-2026.
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List of RAG (Retrieval-Augmented Generation) Patterns. This is still an evolving field, so the list is updated as of 04-Jan-2026.
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As 2025 wraps up, this year looked very different from my earlier ones. I did less physical reading overall, with most of my learning happening digitally. That said, here are my notable offline reading milestones. Deep Work by Cal Newport : Picked this up based on Reddit recommendations. A solid read on managing distractions and cultivating focus. Did it completely change my habits? I am still figuring that out. Mathematical Statistics by S.C. Gupta and V.K. Kapoor : A classic from my college days. Unexpectedly relevant again, and extremely helpful for revisiting the foundations behind my more traditional AI learning.…