Browsing: Business & Startups

Researchers today can draft entire papers with AI assistance, run experiments faster than ever, and summarise literature in minutes. Yet one stubborn bottleneck remains: creating clear, publication-ready diagrams. Poor diagrams look unprofessional and can obscure ideas and weaken a paper’s impact. Google now seems to have a solution to this – and it is called ‘PaperBanana.’ From model architectures to workflow pipelines, publication-ready visuals still demand hours in PowerPoint, Figma, or LaTeX tools. Plus, not every researcher is a designer. This is where PaperBanana enters the picture. Designed to turn text descriptions into clean, academic-ready visuals, the system aims to…

Image by Author   # (Re-)Introducing Hugging Face  By the end of this tutorial, you will learn and understand the importance of Hugging Face in modern machine learning, explore its ecosystem, and set up your local development environment to start your practical journey of learning machine learning. You will also learn how Hugging Face is free for everyone and discover the tools it provides for both beginners and experts. But first, let’s understand what Hugging Face is about. Hugging Face is an online community for AI that has become the cornerstone for anyone working with AI and machine learning, enabling researchers,…

Microsoft Excel remains one of the most in-demand skills across roles. From data analysis and business reporting to finance and operations, Excel sits at the center of business decision making. Its beginner friendly approach makes it so that one can learn it effortless, with ease. But basic familiarity alone isn’t enough. To stand out, you need structured learning and credible certification that proves the mettle. This article brings together some of the best free Excel certification courses available today. Each course serves a different type of learner, whether you want a quick certificate, career-focused skills, or deep expertise. 1. For…

Image by Editor   # Introducing Feature Stores  Feature stores are no longer a niche infrastructure, but a key front-end that helps push the boundaries of data pipelines, particularly those involving machine learning and other AI systems. They have become a trend in the present year largely due to the industry shift from experimental machine learning model-building to the need to operationalize scalable AI-fueled solutions, products, and services. This article gently introduces feature stores, describing their origins, main characteristics, reasons for their current significance, and popular tools at present.   # Tracing the Origins and Evolution of Feature Stores  The term “feature…

Alibaba’s Qwen lineup has evolved rapidly over the past few weeks. We recently saw Qwen3-Coder-Next targeting developers with an AI coding assistant. This was followed by Qwen Image 2.0, which pushed the platform’s image generation quality even further. Each release strengthened a specific capability within the ecosystem. Now, building on that evolution, comes the Qwen 3.5 family with two new AI models – its first open weight model: the Qwen3.5 397B-A17B, and the Qwen3.5-Plus. Among the two, the former, or the Qwen3.5 397B-A17B, is the flagship model, while the Qwen3.5-Plus is the hosted model available via Alibaba Cloud Model Studio.…

Image by Author   # Introduction  Building your own local AI hub gives you the freedom to automate tasks, process private data, and create custom assistants, all without depending on the cloud or having to deal with monthly fees. In this article, I will walk you through building a self-hosted AI workflow hub on a home server, giving you complete control, greater privacy, and powerful automation. We will combine tools like Docker for packaging software, Ollama to run local machine learning models, n8n to create visual automations, and Portainer for easy management. This setup is perfect for a moderately powerful x86-64…

Artificial intelligence tools are evolving rapidly, but the real productivity gains don’t come from using one The real power of these tools comes from using them together. Google NotebookLM specializes in structured knowledge synthesis, helping users analyze curated sources, generate summaries, and clarify complex material. LM Studio offers a private local workspace for running open-weight LLMs, enabling rapid experimentation and iterative content creation. Combined, they form a practical workflow: LM Studio for exploration and generation, NotebookLM for organization and understanding. In this article, we show how this pairing supports real-world research and knowledge work through hands-on examples. Understanding the Complementary…

PyCaret is an open-source, low-code machine learning library that simplifies and standardizes the end-to-end machine learning workflow. Instead of acting as a single AutoML algorithm, PyCaret functions as an experiment framework that wraps many popular machine learning libraries under a consistent and highly productive API  This design choice matters. PyCaret does not fully automate decision-making behind the scenes. It accelerates repetitive work such as preprocessing, model comparison, tuning, and deployment, while keeping the workflow transparent and controllable.  Positioning PyCaret in the ML Ecosystem  PyCaret is best described as an experiment orchestration layer rather than a strict AutoML engine. While many…

Recommendation systems are the invisible engines that can personalize our social media, OTTs and e-commerce. Whether you are scrolling through Netflix for a new show or browsing Amazon for a gadget, these algorithms are working behind the scenes to predict something for you. One of the most effective ways to do this is by looking at how other people with similar tastes have behaved. This is the core of modern personalization. In this article, we will explore how to build one of these systems using collaborative filtering and make it smarter using OpenAI. Without any further ado, let’s dive in. …

Image by Author   # Introduction  Data analytics has changed in recent years. The traditional approach of loading everything into a relational database and running SQL queries still works, but it is often overkill for some analytical workloads. Storing data in Parquet files and querying them directly with DuckDB is faster, simpler, and more effective. In this article, I will show you how to build a data analytics stack in Python that uses DuckDB to query data stored in Parquet files. We will work with a sample dataset, explore how each component works, and understand why this approach can be useful…