Data Science

  • CLI Agents vs AI-Native IDEs: The Token Economics

    CLI Agents vs AI-Native IDEs: The Token Economics

    Here is the benchmark that’s been driving architecture decisions across the industry in 2026: a typical CLI command costs an agent around 200 tokens. The equivalent operation through an MCP server costs 32,000 to 82,000 tokens. That’s not a typo, and it’s not cherry-picked. Independent benchmarks from Scalekit, Apideck, and others keep landing in the same range — roughly a 35× overhead for MCP on identical tasks. When “MCP is dead. Long live the CLI” hit the top of Hacker News and Perplexity’s CTO publicly described moving away from MCP internally over context waste, they were all pointing at the… Go to Post

  • Understanding Vector Databases in the Modern Data Landscape

    Understanding Vector Databases in the Modern Data Landscape

    In the ever-expanding cosmos of data management, relational databases once held the status of celestial bodies—structured, predictable, and elegant in their ordered revolutions around SQL queries. Then came the meteoric rise of NoSQL databases, breaking free from rigid schemas like rebellious planets charting eccentric orbits. And now, we find ourselves grappling with a new cosmic phenomenon: vector databases—databases designed to handle data not in neatly ordered rows and columns, nor in flexible JSON-like blobs, but as multidimensional points floating in abstract mathematical spaces. At first glance, the term vector database may sound like something conjured up by a caffeinated data scientist at 2… Go to Post

  • Harnessing Data Science in Microsoft Azure: A Practical Guide to Tools, Workflows, and Best Practices

    Harnessing Data Science in Microsoft Azure: A Practical Guide to Tools, Workflows, and Best Practices

    Data science is an interdisciplinary field that involves the scientific study of data to extract knowledge and make informed decisions. It encompasses various roles, including data scientists, analysts, architects, engineers, statisticians, and business analysts, who work together to analyze massive datasets. The demand for data science is growing rapidly as the amount of data increases exponentially, and companies rely more heavily on analytics to drive revenue, innovation, and personalisation. By leveraging data science, businesses and organisations can gain valuable insights to improve customer satisfaction, develop new products, and increase sales, while also tackling some of the world’s most pressing challenges.… Go to Post

  • Unraveling the Data Science, Machine Learning, AI, and Generative AI terminology: A Practical, No-Nonsense Guide

    Unraveling the Data Science, Machine Learning, AI, and Generative AI terminology: A Practical, No-Nonsense Guide

    We often hear the buzzwords—Data Science, Machine Learning, AI, Generative AI—used interchangeably. Yet each one addresses a different aspect of how we handle, analyze, and leverage data. Whether you’re aiming to build predictive models, generate human-like text, or glean insights to drive business decisions, understanding the core concepts can be transformative. My goal here is to draw clear lines between these often-overlapping fields, helping us see how each fits into the bigger picture of turning data into something genuinely impactful. This is a vast and deep field… we’ll just scratch the surface. Data Science: The Foundation and Bedrock Data Science encompasses… Go to Post