Ai-Coding

Mobile development is where AI tools face their toughest test. Android has complex APIs, Jetpack Compose changes fast, and platform-specific patterns do not always match what AI learned from web development tutorials. In this article, you will build a complete notes app with Kotlin and Jetpack Compose. You will use Claude Code as the primary tool (running alongside Android Studio) and Copilot for inline completions. Every prompt, every AI mistake, and every manual fix is shown. ...

You have built a CLI tool and a REST API with AI. Now let us go full-stack. You are going to build a complete analytics dashboard with a frontend, backend, database, authentication, and charts — all in one project. The project: DevPulse — a developer activity dashboard that tracks coding sessions, displays charts, and lets users manage their data. It uses Next.js, Tailwind CSS, PostgreSQL, and Auth.js for authentication. This project uses three AI tools together: Cursor Composer for scaffolding and UI, Claude Code for backend logic and database work, and Copilot for inline completions. You will see how they complement each other in a real workflow. ...

We built a CLI app and a REST API with Claude Code. Both were backend projects. Now let’s see how Claude handles the visual side: a modern landing page with responsive design, animations, and dark mode. The project: build a landing page for a fictional SaaS product called “FlowBoard” — a project management tool. No React, no frameworks. Just HTML, CSS (Tailwind), and a tiny bit of vanilla JavaScript. Total time: 38 minutes from empty folder to live on Netlify. ...

Android 16 ships a platform feature called App Functions. It lets an AI assistant — like Google Gemini — discover and call typed functions inside your app from a natural-language request, without the user opening your app. You annotate a suspend fun with @AppFunction. A KSP compiler plugin reads your KDoc and turns it into an XML schema. Android indexes that schema when the app installs. When a user says “set a 10-minute pasta timer,” an assistant like Gemini matches the request to your function and calls it. Your function runs locally, inside your app’s own process. ...

In the previous article, you built a CLI tool with AI. Now let us scale up. You are going to build a complete REST API — with a database, authentication, tests, and deployment — all driven by AI. The project: a Notes API that lets users create, read, update, and delete notes. It includes user registration, authentication with JWT, PostgreSQL storage, and proper error handling. Simple enough to build in one session, complex enough to show real patterns. ...

Last time we built a CLI todo app in 35 minutes. That was a local app. This time, we go bigger: a REST API that runs on the web. The goal: build a bookmark manager API with CRUD operations, pagination, search, rate limiting, tests, and a Docker setup. All with Claude Code prompts. Deploy it and get a live URL. Total time: 52 minutes from empty folder to deployed API. What We’re Building A bookmark manager REST API with these features: ...

Enough theory. Let us build something real. In this article, you will build a complete CLI tool using AI — from the initial description to a published package on PyPI. Every prompt, every AI response, and every manual fix is shown. You will see where AI excels, where it struggles, and how to handle both. The project: linkcheck — a markdown link checker that scans your documentation files and reports broken links. It is a small, useful tool with enough complexity to show real patterns. ...

Can you build a working CLI application in 30 minutes using only Claude Code? I tried it. Here is exactly what happened — every prompt, every mistake, and the final result. The goal was simple: start with an empty folder, talk to Claude Code in the terminal, and end up with a fully working todo app. No manual coding. Just prompts. Spoiler: it took 35 minutes, not 30. But the result was better than expected. ...

One AI agent is powerful. Multiple AI agents working together are something else entirely. One developer tasked 16 Claude agents with building a Rust-based C compiler from scratch — one agent worked on parsing, another on code generation, another on optimization passes — all communicating and validating each other’s work. Multi-agent coding is no longer theory. Anthropic released Agent Teams in February 2026, and multi-agent system inquiries surged over 1,400% in the past year. By the end of 2026, 40% of enterprise applications are expected to include task-specific AI agents. ...

Debugging AI-generated code is a unique challenge. You are looking at code you did not write, understanding logic you did not design, and fixing bugs in patterns you did not choose. Traditional debugging skills still apply, but you need a different approach. Here is the uncomfortable truth about AI debugging: Claude Code almost never fails because it lacks intelligence. It fails because it lacks visibility. You have browser dev tools, console logs, network requests, and actual behavior on your screen. The AI has code files. That is it. ...