• Architecture - Code - Data

    Redis integration tutorial – C# and Python – for Beginners

    January 12, 2026 - By 

    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…

  • Code

    Kafka integration for C# beginners

    January 11, 2026 - By 

    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…

  • AI/ML

    CrewAI–my quick thoughts

    January 7, 2026 - By 

    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…

  • General

    Article: AI Governance isn’t just rules. It’s an art

    January 2, 2026 - By 

    If you treat governance as a static rulebook, you stall innovation. But if you treat it as an active engineering layer embedded in your strategy, frameworks, and runtime controls, you actually move faster. I broke down the 3 essential layers of modern AI governance (Strategy, Frameworks, and Technical Controls) in the article below. I have also included an implementation reference sample. Read: https://www.linkedin.com/pulse/ai-governance-isnt-just-rules-its-art-praveen-xfeqc

  • Bookmark - Personal

    My 2025 book life

    December 29, 2025 - By 

    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.…

  • AI/ML

    Run Qwen 2.5 72b on Ollama on your local computer

    December 21, 2025 - By 

    1. Download Ollama from https://ollama.com/download 2. To download and run the model, use the command: ollama run qwen2.5:72b 3. Once model download is completed, just type your prompts! Meanwhile, my poor GPU Next, here is a bonus. Access the model from a LangChain python script. Your Ollama service is already running at http://localhost:11434/ Install dependencies: pip install langchain-ollama Create a test_client.py program: from langchain_ollama import ChatOllama from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser import time # 1. Configuration # We use the specific model tag you pulled in Ollama MODEL_TAG = “qwen2.5:72b” print(f”— Connecting to Local Ollama ({MODEL_TAG}) —“)…