Browsing: Business & Startups

Image by Author   # Introduction  Parsing dates and times is one of those tasks that seems simple until you actually try to do it. Python’s datetime module handles standard formats well, but real-world data is messy. User input, scraped web data, and legacy systems often throw curveballs. This article walks you through five practical functions for handling common date and time parsing tasks. By the end, you’ll understand how to build flexible parsers that handle the messy date formats you see in projects. Link to the code on GitHub   # 1. Parsing Relative Time Strings  Social media apps, chat applications,…

Talking to software feels natural now, until you need real business data. That’s where things usually break. MCPToolbox to Databases fixes this by giving AI agents safe, reliable access to production databases through a standardized MCP interface. Databases become first-class tools that agents can inspect, query, and reason over using clean, production-ready natural language to SQL. In this article, we explain why MCP matters and show how to build your own AI database agent. MCPToolbox to Databases runs as a server that turns database operations into tools AI agents can safely use. Originally built by Google and LangChain, it supports…

I have spent the last several years watching enterprise collaboration tools get smarter. Join a video call today, and there’s a good chance five or six AI agents are running simultaneously: transcription, speaker identification, captions, summarization, task extraction. On the product side of it, each agent gets evaluated in isolation. Separate dashboards, separate metrics. Transcription accuracy? Check. Response latency? Check. Error rates? All green. But here is what I consistently observe as a UX Researcher: users are frustrated, adoption stalls, and teams are trying to identify the root cause. Per the metrics, the dashboards look fine. Every individual component passes…

Image by Author   # Introduction  Technical interviews are not about memorizing random questions. They are about demonstrating clear thinking, strong fundamentals, and the ability to reason under pressure. The fastest way to build that confidence is to learn from resources that have already helped thousands of engineers succeed. In this article, we will explore 10 of the most trusted GitHub repositories for tech interview preparation, covering coding interviews, system design, backend and frontend roles, and even machine learning interviews. Each repository focuses on what actually matters in interviews, from data structures and algorithms to scalable system design and real-world tradeoffs.…

We live in a world where answers are instant. AI copilots, search engines, short videos, and interactive courses can explain almost anything in minutes. Information is no longer scarce. What is scarce is depth, clarity, and the ability to connect ideas into sound decisions. That is where books still matter. In an era of fast content and fragmented learning, well written books offer something most tools cannot. They provide structured thinking, real world context, and the discipline to follow an idea from fundamentals to mastery. For professionals working with data, this depth is critical. Business analytics is not just about tools or…

Image by Author   # Introduction  I am sure if you are GPU-poor like me, you have come across Google Colab for your experiments. It gives access to free GPUs and has a very friendly Jupyter interface, plus no setup, which makes it a great choice for initial experiments. But we cannot deny the limitations. Sessions disconnect after a period of inactivity, typically 90 minutes idle or 12 to 24 hours max, even on paid tiers. Sometimes runtimes reset unexpectedly, and there is also a limit on maximum execution windows. These become major bottlenecks, especially when working with large language models…

At its core, ML involves algorithms that analyze data, recognize patterns, and make predictions. These models “learn” from past data to improve their performance over time. For example, an ML model trained on user purchase history can predict which products a customer might buy next. Artificial Intelligence (AI) is no longer a future concept. This is a boardroom conversation happening in almost every industry. From e-commerce and finance to healthcare and manufacturing, AI is being woven into a lot of businesses. For decision making, however, two words often create confusion: machine learning (ML) vs deep learning (DL). Both can learn the…

Image by Author   # Introduction  I have been vibe coding my Stable Coin Payment platform, running everything locally with my own server setup using Docker Compose.  But at some point, I realized something important: there really is not a simple self hosted platform that can handle scaling, deployment, and multi service Docker management without turning into a full time DevOps job.  This pushed me to start searching for Vercel style alternatives that are easy to use while still giving me the freedom and control I want. The self hosting platforms I am going to share come directly from my own…

Job descriptions of Data Engineering roles have changed drastically over the years. In 2026, these read less like data plumbing and more like production engineering. You are expected to ship pipelines that don’t break at 2 AM, scale cleanly, and stay compliant while they do it. So, no – “I know Python and Spark” alone doesn’t cut it anymore. Instead, today’s stack is centred around cloud warehouses + ELT, dbt-led transformations, orchestration, data quality tests that actually fail pipelines, and boring-but-critical disciplines like schema evolution, data contracts, IAM, and governance. Add lakehouse table formats, streaming, and containerised deployments, and the…

Image by Author   # Introduction  Python is the default language of data science for good reasons. It has a mature ecosystem, a low barrier to entry, and libraries that let you move from idea to result very quickly. NumPy, pandas, scikit-learn, PyTorch, and Jupyter Notebook form a workflow that is hard to beat for exploration, modeling, and communication. For most data scientists, Python is not just a tool; it is the environment where thinking happens. But Python also has its own limits. As datasets grow, pipelines become more complex, and performance expectations rise, teams start to notice friction. Some operations…