Empowering Independent Voices, Professional Insights, and Global Perspectives
Large Language Models (LLMs) are the backbone of modern AI coding agents, powering tools that write, debug, and refactor code. The dream is to feed these models entire codebases or vast chat histories, letting them reason over everything at once. But a critical issue, dubbed “context rot,” undermines this approach. Based on insights from Chroma... Continue Reading →
RAG (Retrieval-Augmented Generation) is a hybrid AI approach that combines: 1. Retrieval-based systems (for accuracy and up-to-date knowledge) 2. Generative models (for fluent, natural language responses) Why do we need RAG? Where was it some time back? A user enters a natural language query, such as "What are the latest features in Kubernetes 1.30?” The query is converted into... Continue Reading →
An intriguing case study that's keeping me awake — a zero-employee organization is a real possibility. This example is taken from Pot-Pie (now I re-branded myself as "Developer Assist"), a solution built using the Agentic AI framework. It enables the development of agents to support an organization across various SDLC phases. In this article, I... Continue Reading →
In today's fast-paced data landscape, business users want answers without waiting for a data team to write SQL queries. The Genie Space framework bridges this gap by allowing users to ask data questions in plain language and get back the results, the SQL logic, and a clear explanation. Powered by LLMs and guided by curated... Continue Reading →
This research article discusses the challenges and solutions related to optimizing a microservice designed to fetch data from a Data Lake. The microservice runs complex SQL queries that interact with numerous tables, some containing billions of records, which currently take 50 to-300 seconds to execute. This execution time is problematic because the API that utilizes... Continue Reading →
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[…] Also read, “Why Decision Trees are the “Cleaners” of the Data World?” at https://journals-times.com/2026/02/24/how-decision-tree-deal-with-imperfect-datasets/ […]
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