Browsing: Business & Startups

Image by Author   # The Setup  You’re about to train a model when you notice 20% of your values are missing. Do you drop those rows? Fill them in with averages? Use something fancier? The answer matters more than you’d think. If you Google it, you’ll find dozens of imputation methods, from the dead-simple (just use the mean) to the sophisticated (iterative machine learning models). You might think that fancy methods are better. KNN considers similar rows. MICE builds predictive models. They must outperform just slapping on the average, right? We thought so too. We were wrong.   # The Experiment…

Agentic browsing is quickly becoming mainstream. People don’t just want AI agents to research products anymore. They want agents to actually buy things for them: compare options, place orders, handle payments, and complete the entire transaction. That’s where things started to break. Today’s commerce stack is fragmented. Every merchant, platform, and payment provider uses proprietary integrations. So even if an agent is smart enough to make decisions, it struggles to act at scale because it has no common way to talk to these systems. This is exactly the gap Google’s Universal Commerce Protocol (UCP) is designed to fix. UCP creates a standardized, secure way…

Image by Author   # Introduction  Automation has become the strength of well-structured business operations. Companies worldwide are automating repetitive tasks, combining multiple applications, and building intelligent workflows to save time and minimize manual errors. n8n is a powerful, open-source workflow automation tool that’s revolutionizing how teams approach automation, and it’s completely free to host yourself. Unlike expensive software as a service (SaaS) solutions like Zapier, n8n gives you full control over your automation infrastructure. When you combine n8n with Docker, you get a containerized, scalable, and portable automation platform that can be deployed anywhere — from your local machine to…

Hackathons are different!. The good ones pull you in, stretch your thinking, and leave you with something real—regardless of the outcome. The problem is choice. It’s hard to find the right one! Too many hackathons. Too many formats. And too much noise. So this list is built with that in mind. Instead out outlining Hackathons that might expire any minute, it lists the top 10 places where you can look for Hackathons depending upon your requirement—money, name, game. Pick the one that best suits your interests.  1. For the Popular Hackathons Devpost | Creativity-first, competitive building Devpost hackathons reward creativity…

If you’re curious about trending terms like AI Agents or Agentic AI, you’re in the right place. Agentic AI is rapidly moving from experimentation to enterprise adoption. According to Gartner, over 60% of enterprise AI applications are expected to include agentic components by 2026, while more than 40% of early agentic AI projects are projected to be abandoned due to poor architecture, cost overruns, and lack of governance. In short, Agentic AI is becoming a big deal but building it correctly is the right skill to have! So what does it take to build such systems? A clear understanding of what to build and how to build it. That is exactly why I created this…

Image by Author   # Introduction  Building Extract, Transform, Load (ETL) pipelines is one of the many responsibilities of a data engineer. While you can build ETL pipelines using pure Python and Pandas, specialized tools handle the complexities of scheduling, error handling, data validation, and scalability much better. The challenge, however, is knowing which tools to focus on. Some are complex for most use cases, while others lack the features you’ll need as your pipelines grow. This article focuses on seven Python-based ETL tools that strike the right balance for the following: Workflow orchestration and scheduling Lightweight task dependencies Modern workflow…

AI is taking over the world. If you don’t agree to this, you need to have a look at the latest technologies presented at one of the biggest annual technology events – the CES 2026. Consumer Electronics Show, which takes place in Las Vegas, US, every year, brings forward the best of technologies being pursued by the leading and upcoming companies across the world. And this time around at CES 2026, AI is not just the “hot goss!” It is the engine behind almost every product and service being made. Robotics, Energy Systems, Wearables, Desk devices, Home Machines, you name…

Image generated by Author   # Introduction  n8n is an open source workflow automation platform that allows you to connect applications, APIs, and services using a visual, node based interface. It helps automate data movement, system integrations, and repetitive tasks without requiring complex code. n8n is widely used because it is flexible, supports self hosting, integrates with hundreds of tools, and gives developers full control over logic, execution, and data handling, making it a strong alternative to closed automation platforms. In this article, we will learn about the top 7 n8n workflow templates for data science. These templates are plug and…

Retrieval-Augmented Generation (RAG) technology almost immediately became the standard in intelligent applications. This was a result of the quickly developing field of artificial intelligence that combined large language models and external knowledge bases with different real-time access methods. RAG implementation of the traditional kind poses major difficulties: complex vector database setups, intricate embedding pathways, orchestration of infrastructure, and the necessity for pulling in the DevOps specialists. Here are some of the main drawbacks of RAG’s traditional development: Infrastructure setup and configuration can take weeks. Vector database solutions can be extremely costly. There is a need for integration of multiple tools,…

Image by Editor   # Introduction  As a data professional, you know that machine learning models, analytics dashboards, business reports all depend on data that is accurate, consistent, and properly formatted. But here’s the uncomfortable truth: data cleaning consumes a huge portion of project time. Data scientists and analysts spend a great deal of their time cleaning and preparing data rather than actually analyzing it. The raw data you receive is messy. It has missing values scattered throughout, duplicate records, inconsistent formats, outliers that skew your models, and text fields full of typos and inconsistencies. Cleaning this data manually is tedious,…