In the previous tutorials, we shared business logic between Android and iOS. The UI stayed separate — Compose on Android, SwiftUI on iOS. But what if you could share the UI too? That is what Compose Multiplatform (CMP) does. Write your Compose code once. Run it on Android, iOS, Desktop, and Web. Same @Composable functions. Same Modifier chains. Same MaterialTheme. If you followed our Jetpack Compose tutorial series, you already know how to build Compose UI. CMP uses the exact same API. No new framework to learn. ...

In the previous tutorial, we created a KMP project and ran it on Android and iOS. Now let’s understand exactly how that project is organized — because once you understand the structure, everything else in KMP makes sense. This is the tutorial that separates developers who struggle with KMP from those who build confidently. Take your time. The Core Idea: Source Sets A KMP project is organized into source sets. Each source set is a collection of Kotlin files that compile to specific platforms. ...

In the previous tutorial, we learned what KMP is and why it matters. Now let’s get our hands dirty — create a real project, understand every file in it, write shared code, and run it on both Android and iOS. This is a long tutorial. Take your time. By the end, you will have a working cross-platform app and understand exactly how every piece fits together. Part 1: Setting Up Your Environment What You Need Tool Required Purpose Android Studio Yes IDE for KMP development (latest stable) Xcode Yes (Mac only) Builds and runs iOS apps JDK 17+ Yes Kotlin compiler depends on it Mac computer For iOS Apple requires Xcode, which only runs on macOS CocoaPods Optional Some KMP libraries need it for iOS If you are on Windows or Linux, you can still write shared code and build the Android app. But you cannot build or run iOS. There is no way around this — Apple controls iOS builds. ...

You are a Kotlin developer. You build Android apps. But your company also needs an iOS app. And maybe a desktop app. And a web app. Do you write everything four times? Do you switch to Flutter and learn Dart? Do you hire a separate iOS team? Or do you use the Kotlin you already know — and share your code across all platforms? That is what Kotlin Multiplatform (KMP) does. ...

You used to craft the perfect prompt. Tweak the wording. Add examples. Get a better answer. That era is ending. In 2026, the best AI coding workflows are not about prompts. They are about loops. You give the agent a goal, a test gate, and permission to run. You come back to a completed PR. This article explains how agentic workflows work, what they look like in practice, the risks nobody talks about, and how to set one up properly. ...

AI coding tools have changed a lot in 2026. A year ago, GitHub Copilot was the obvious choice. Now you have 7+ serious options — and the right one depends on how you work. This article covers the top AI coding tools as of March 2026. Real pricing. Honest pros and cons. No hype. What We Compare SWE-bench score — a standard coding benchmark. Higher = better at real coding tasks. Best use case — what the tool is actually good at Pricing — what you actually pay Honest verdict — where it falls short The Power Rankings 1. Claude Code Best for: Complex debugging, multi-file changes, full codebase understanding ...

PyTorch is the standard framework for deep learning research and production. Most AI papers, Hugging Face models, and state-of-the-art systems use PyTorch. This article gets you from zero to a working neural network. Setup pip install torch torchvision import torch print(torch.__version__) # 2.x print(torch.cuda.is_available()) # True if you have a GPU Tensors A tensor is the fundamental data structure in PyTorch. It is like a NumPy array, but it can run on GPU and supports automatic differentiation. ...

Neural networks power most AI you use today. ChatGPT, image recognition, voice assistants — all neural networks. You do not need a math degree to understand them. This article explains the concepts clearly, with code examples in plain Python and PyTorch. What Is a Neural Network? A neural network is a function. It takes numbers in, does math, and produces numbers out. That’s it. The magic is in how it learns which math to do. ...

You know NumPy and Pandas. Now it is time to train a model. scikit-learn is the standard library for machine learning in Python. It is simple, well-documented, and works for most real-world tasks without a GPU. Setup pip install scikit-learn pandas numpy import sklearn print(sklearn.__version__) # 1.5+ The ML Workflow Every supervised ML task follows these steps: 1. Load data 2. Prepare features (X) and target (y) 3. Split into train and test sets 4. Train a model 5. Evaluate on test set 6. Make predictions on new data Let’s go through each one. ...

Before you train any machine learning model, you need to handle data. NumPy and Pandas are the two libraries you will use every day. This is a practical crash course. No theory — just the operations you actually need. Setup pip install numpy pandas Check versions: import numpy as np import pandas as pd print(np.__version__) # 2.x print(pd.__version__) # 2.x NumPy: Arrays NumPy gives you fast multi-dimensional arrays. They are faster than Python lists for math operations. ...