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AI-accelerated source code development is transforming the daily work of many developers. LLMs and coding agents can generate functions in seconds, but they also carry risks including hallucinated dependencies, slopsquatting, and insecure automation steps. This article shows how to systematically mitigate these risks using engineering principles, guardrails, and automated validations.

In executive circles, there is a growing belief that software will soon be free. At the same time, tech gurus are declaring software engineering dead because humans are no longer needed. Both are wrong. When software is produced at the speed of light, entirely new questions arise about purpose and...

Picture this: you give a single voice command and, minutes later, an OpenAI-powered writing agent drafts an event brochure, a Gemini spreadsheet agent reconciles supplier invoices, and a Claude negotiation agent emails final quotes. Each working from the same facts, updating the same timeline, and handing off sub-tasks automatically.

Generative AI accelerates software development – that is real and measurable. But most teams introduce AI at one point in the process and call it done. They speed up code generation. They ship prototypes in days. And then the overall system starts breaking in ways they didn't anticipate. The problem...

Vibe coding and AI coding tools are rapidly changing how software is built. Developers are experimenting with LLM-powered assistants such as Cursor, Claude Code, and Copilot to speed up coding, debugging, and prototyping. But where do these tools actually help and where do they create new risks? Experienced developers share...

Enterprise teams have run the experiments. Now comes the harder part to make AI really productive.

Together, we will analyze findings from four real-world development projects to shed light on the practical use and limitations of modern AI coding assistants. The “vibe coders” here—Cursor, Aider, Roo Code, JetBrains Pro AI Agent (Rider, WebStorm), GitHub Copilot (VSC), and Claude Code—promise deep contextual understanding even for extensive codebases.

You’ve probably built — like almost everyone in engineering by now — one or more AI prototypes. You’ve tried ChatGPT, connected a model to your data, maybe wrapped it in a small service or even shipped a first AI-powered feature. It likely worked faster than expected. Often better than expected....

Vibe Coding is an emerging approach to software development that places intuition, exploration, and spontaneous decision-making at the center of the workflow. Instead of following rigid plans and step-by-step implementation guidelines, developers focus on defining what a system should do and why it should behave in a certain way. The...

Implementing AI in software development will become necessary in order to stay relevant, but just as it enhances productivity, its complexity means that a software development expert still needs to be on board. Learn about the importance of the human element in AI-assisted software development.