• AI/ML - Articles - General

    My journey of building an AI-Powered App for a pseudoscience project

    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…

  • AI/ML - Articles - CTO

    Article: Do AI Agents learn automatically?

    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…

  • AI/ML - Articles

    Hosting a SLM Qwen 2.5 on Raspberry Pi 2

    This article is a write up of my experience on hosting Qwen 2.5 the 0.5B model in Raspberry Pi 2 using Llama.cpp. Qwen 2.5 is one of the small SLM with 0.5B parameters so a small development board like Raspberry Pi can hold it. RPi 2 Model B comes with 900Mhz speed and only 1GB of memory. But to be honest, setting up the project might take 1-2 hours, and the prompt execution is only some 1-2 tokens per second. So you need to be patient. Let us begin! Step 0: Pick up the Raspberry Pi 2 from the attic.…

  • AI/ML - Articles

    Strategy for adding AI to the existing application

    How should a technical leader respond when a customer asks to add AI to an existing application? The answer requires structure and clear thinking. 1. First, clarify the Actual ProblemNever assume that AI is the right solution. I would start by understanding the business objective. Many requests framed as AI needs turn out to be workflow issues, reporting gaps, or rule based automation opportunities. Accurate problem definition prevents unnecessary complexity. 2. Evaluate Data/App ReadinessAI depends completely on data quality. Assess what data exists, how clean it is, and whether privacy or compliance concerns limit its use. If data foundations are…

  • AI/ML - Articles

    The “Stochastic Parrot” problem and why it still matters in AI system design

    The term stochastic parrot was introduced in a 2021 paper by Bender, Gebru, and colleagues (ref: Wikipedia). It highlights a fundamental limitation of large language models. These systems generate text by predicting the next token based on statistical patterns. They do not possess grounded understanding of the world. This can lead to convincing output that is incorrect, biased, or superficial. What the metaphor captures is simple: the probabilistic, statistically driven nature of these models. Parrot evokes an entity that mimics language without real understanding. The critique is not about style. It is about reliability. When a model draws from vast…

  • AI/ML - Architecture - Articles - General

    Why you should use FastAPI?

    If you are building an AI-powered application today, the way you expose your models through APIs can make a big difference in scalability and developer experience. FastAPI has become one of the most popular choices for creating robust, production-ready APIs, especially for AI and LLM-based workloads. It is fast, type-safe, asynchronous, and easy to work with, which makes it ideal for developers who want both speed and clarity. While Flask, Django, BentoML, and Ray Serve are all valid alternatives, FastAPI provides a good balance between simplicity and performance. For enterprise-level applications, however, more powerful frameworks like Ray Serve or BentoML…

  • AI/ML - Articles

    In the era of Gen AI, should we still learn statistics and ML?

    With the rise of Generative AI, many professionals wonder if learning the old school foundations of statistics, classical machine learning, and data science is still relevant. After all, tools today can generate insights, code, and even models with just a few prompts. It is tempting to skip the basics and focus only on leveraging Gen AI platforms. But the reality is, foundational knowledge still holds significant value, especially depending on who you are and what you do. For Data Scientists and Analysts If you are building models, validating results, or making sense of patterns in data, a strong foundation is…

  • Articles

    Article: Keep Learning, Keep Moving

    Technology is moving fast. Life is moving fast too. New tools show up, old skills become less valuable, and the way we work keeps shifting. If you are in IT, software, project management, or any role that touches tech and business, continuous learning is not a nice-to-have. It is survival, growth, and confidence in one simple habit. A quick story that changed how I look at learning When I was in college, I was bitten by a mad dog and went straight to the hospital for vaccination. I still remember the doctor. He had a big pile of books beside…