Browsing: Business & Startups

Image by Editor   Python continues to grow every year. New libraries emerge regularly, streamlining coding workflows. In 2026, several have already caught our attention, offering tools for data, AI agents, code analysis, documentation, and synthetic data. Most are open-source and accessible.   # 12 Python Libraries for 2026  These are 12 Python libraries that made waves in 2025, and that every developer should try in 2026.   // 1. MarkItDown Repo: https://github.com/microsoft/markitdownStars: ~86k+ on GitHub (rapid adoption in 2025)Features: MarkItDown converts documents like PDFs, Word, Excel, and PowerPoint into Markdown. It preserves structure such as headings, tables, and lists and is…

You type in a lengthy prompt, well over 500 words, or even 1000 words. It structures everything perfectly. Explains in detail what is to be done, right down to the finer details of each step. And you press enter. Your AI chatbot starts off strong, following every instruction from the top, then trails off slightly in the middle, and completely forgets some of the instructions by the end. At completion, you have a potpourri of output that is not inaccurate in entirety, but certainly not good enough for you to use. If you have ever used AI for a complex,…

Image by Editor   # Introduction  Vertex AI Search, formerly known as Enterprise Search on Google Cloud, represents a significant evolution in how organizations can implement intelligent search capabilities within their applications. This powerful tool combines traditional search functionality with advanced machine learning capabilities to deliver semantic understanding and natural language processing (NLP). For data scientists and machine learning engineers working with the Google Cloud AI ecosystem, understanding how to leverage Vertex AI Search opens up new possibilities for building sophisticated information retrieval systems. This guide explores the essential components, implementation strategies, and best practices for building production-ready search applications using…

The AI researcher Andrej Karpathy has developed an educational tool microGPT which provides the easiest access to GPT technology according to his research findings. The project uses 243 lines of Python code which does not need any external dependency to show users the fundamental mathematical principles that govern Large Language Model operations because it removes all complicated features of modern deep learning systems.   Let’s dive into the code and figure out how he was able to achieve such a marvellous feat in such a economical manner. New art project. Train and inference GPT in 243 lines of pure, dependency-free Python.…

Sponsored Content        Introduction: When AI Stops Being a Tool and Starts Being a Partner   I’ve spent the last several weeks pushing Abacus AI’s DeepAgent to its limits, and I need to be upfront: this isn’t your typical chatbot review. What I encountered fundamentally changed how I think about AI assistants and, frankly, about where we’re headed as a technological civilization. DeepAgent isn’t just another GPT wrapper with a fancy interface. It’s something qualitatively different—an autonomous AI system that can actually do things in the real world. And after extensive testing, I’m convinced we’re looking at one…

Swarm architecture brings together specialized AI agents that collaborate to solve complex data problems. Inspired by natural swarms, it pairs a Data Analyst agent for processing with a Visualization agent for chart creation, coordinated to deliver clearer and more efficient insights. This collaborative design mirrors teamwork, where each agent focuses on its strength to improve results. In this article, we explore swarm fundamentals and walk through designing and building a practical analytics agent system step by step. What Are Swarm Agents? Swarm agents function as self-operating AI entities who perform dedicated duties while working together according to defined procedures instead…

Image by Author   # Introduction  You’ve probably done your fair share of data science and machine learning projects. They are great for sharpening skills and showing off what you know and have learned. But here’s the thing: they often stop short of what real-world, production-level data science looks like. In this article, we take a project — the U.S. Occupational Wage Analysis — and turn it into something that says, “This is ready for real-world use.” For this, we will walk through a simple but solid machine learning operations (MLOps) setup that covers everything from version control to deployment. It’s…

A-T-S!! A hurdle that most applicants just can’t cross. Spending hours on Overleaf and resume making websites to create their perfect resumes: just to find out that it has an ATS score of 40! For some it’s a dead end. Regardless of what they try, the score doesn’t seem to get anywhere near the required limit (80+).  What to optimize? Or more specifically how to optimize? Lack of information about optimizing resumes further adds to the problem. This guide is here to solve that problem. Contained within are guidelines, tips and techniques that you can use with an AI, to…

Image by Editor   # Introduction  Getting labeled data — that is, data with ground-truth target labels — is a fundamental step for building most supervised machine learning models like random forests, logistic regression, or neural network-based classifiers. Even though one major difficulty in many real-world applications lies in obtaining a sufficient amount of labeled data, there are times when, even after having checked that box, there might still be one more important challenge: class imbalance. Class imbalance occurs when a labeled dataset contains classes with very disparate numbers of observations, usually with one or more classes vastly underrepresented. This issue…

Sixteen autonomous AI agents. Two weeks of continuous execution. Nearly 100,000 lines of Rust code. That’s what it took for Anthropic to build a working C compiler capable of compiling large real-world projects like the Linux kernel. There is, however, a kicker here. The project, internally referred to as the Claude “agent teams,” wasn’t written by a human engineering team. It was developed by a coordinated swarm of Claude agents working in parallel, almost completely without human input. But know this – this wasn’t autocomplete on steroids or a chatbot stitching together random functions. The Claude agents operated like a…