Browsing: Business & Startups

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…

Image by Author   # Introduction  Everyone focuses on solving the problem, but almost no one tests the solution. Sometimes, a perfectly working script can break with just one new row of data or a slight change in the logic. In this article, we will solve a Tesla interview question in Python and show how versioning and unit tests turn a fragile script into a reliable solution by following three steps. We will start with the interview question and end with automated testing using GitHub Actions.  Image by Author   We will go through these three steps to make a data…

Another month, another AI-powered trend taking over the internet, and this one is all about turning yourself into a caricature using ChatGPT Image. From LinkedIn feeds to group chats, people are sharing playful versions of themselves that capture not just their faces, but also their profession, personality, and vibe. The best part is that you don’t need any design skills or complex tools. With just a photo and a simple prompt, ChatGPT can create a fun caricature in seconds. In this article, we will walk you through what this caricature trend is, how you can try it yourself, the prompt that works best, and a…

According to the National Center for Education Statistics, only 64% of students who begin a bachelor’s degree complete it within six years. For a mid-sized university with 10,000 students, that’s 3,600 students who never graduate. Universities track these numbers closely. Most have sophisticated analytics identifying at-risk students: declining GPAs, attendance patterns, financial aid status. The data exists. But knowing who is struggling and acting fast enough to help are two different problems. Traditional analytics operates on timelines measured in days or weeks. Student dropout decisions happen faster. A student misses three consecutive classes, falls behind in coursework, starts questioning whether…

Image by Author   # Introduction  Artificial intelligence (AI) agents represent a shift from single-response language models to autonomous systems that can plan, execute, and adapt. While a standard large language model (LLM) answers one question at a time, an agent breaks down complex goals into steps, uses tools to gather information or take actions, and iterates until the task is complete. Building reliable agents, however, is significantly harder than building chatbots. Agents must reason about what to do next, when to use which tools, how to recover from errors, and when to stop. Without careful design, they fail, get stuck…

Text autocompletion and chat queries are no longer the only roles for AI agents. They now refactor repositories, generate documentation, review codebases, and run unattended workflows, creating new challenges in coordinating multiple agents without losing context, control, or code quality. Maestro, the latest AI Agents orchestration platform, addresses this need as an application that creates long lived AI processes and developer workflows. It treats agents as observable, independent systems that mirror engineering practice. In this article, we examine what Maestro is and how to use it in our development workflows. What is Maestro? The Maestro is a desktop-based orchestration platform…

Image by Editor   # Introduction  Exploratory data analysis (EDA) is a crucial stage prior to deeper data analysis processes or building data-driven AI systems, such as those based on machine learning models. While fixing common, real-world data quality issues and inconsistencies is often deferred to subsequent stages of the data pipeline, EDA is also an excellent opportunity to proactively detect these issues early on — before silently biasing results, degrading model performance, or compromising downstream decision-making. Below, we curate a list that contains 7 Python tricks applicable to your early EDA processes, namely by effectively identifying and fixing a variety…

Learning AI in 2026 is definitely not the same as it was just a couple of years ago. Back then, the advice was simple (and intimidating): learn advanced math, master machine learning theory, and maybe – just maybe – you’d be ready to work with AI. Today, that narrative no longer holds. And the reason is quite simple – AI is no longer confined to research labs or niche engineering teams. It’s embedded in everyday tools, products, and workflows. From content creation and coding to analytics, design, and decision-making, AI has quietly become a general-purpose skill. Naturally, that also changes…