freeCodeCamp

Generative AI agents are transforming .NET development by helping developers automate repetitive coding tasks, generate unit tests, assist with debugging, document code, and accelerate CI/CD workflows. This article demonstrates how to integrate AI agents into enterprise .NET environments responsibly.

Use a Sheet for secondary workflows: It's ideal for filters, shopping carts, settings, navigation menus, and forms that shouldn't interrupt the main page. Keep the layout consistent: Use a fixed header, a scrollable content area (flex-1 overflow-y-auto), and a fixed footer so actions remain visible even with long content. Derive UI values from state: Counts, totals, and badges (such as active filters or cart items) should be calculated from state instead of being stored separately.

PDF files are widely used for sharing documents because they preserve formatting across different devices. Many PDFs contain valuable images such as logos, product photos, charts, diagrams, illustrations, and marketing graphics. While these images are easy to view, extracting them individually isn't always simple. Many users rely on screenshots or manual cropping, which can reduce image quality and take unnecessary time.

Your LLM product experiment just came back positive, with a promising 8-percentage-point lift in task completion. You ship the feature and leadership celebrates. Three months later, the core metric has barely moved. The experiment was statistically sound. It simply answered the wrong question. An average treatment effect compresses the entire treatment response across your user base into a single number. That compression is useful when you're deciding whether to build a feature in the first place.

A Postman collection is a great place to explore an API. But it's a poor place to keep your tests. Most teams find this out the slow way. Someone exports the collection, converts the requests into test code once, and moves on. Six months later the tests are red, nobody trusts them, and they get skipped in the pipeline. The conversion was never the hard part. Keeping the suite alive is. This tutorial takes you from a Postman collection to a pytest suite that still passes next quarter.

Technology changes faster than almost any other industry. The half-life of technical skills continues to shrink. Frameworks, cloud platforms, AI tooling, and cybersecurity practices evolve so quickly that knowledge gained two years ago may already be outdated. New programming languages emerge, frameworks evolve, security threats become more sophisticated, and development practices continue to shift. For technical teams, staying current is no longer optional.

A new type of app is emerging with the development of LLMs and AI-native apps. It lives inside an AI chat (like ChatGPT) rather than being a fully native web or mobile app. In this tutorial, you'll learn how to build an MCP (Model Context Protocol) server from scratch, including a UI you can use as a ChatGPT app with authentication and a database. You'll go through the process of building, testing, adding the ChatGPT app as a connector, and submitting it to publish to the app directory.

The first time I built an API without thinking about roles, I gave every logged-in user the same access. It worked fine until a regular user accidentally hit a delete endpoint and wiped test data. That was the day I actually sat down and learned RBAC properly. Role-Based Access Control sounds fancy, but the idea is simple: what you can do depends on who you are, not just that you're logged in. An admin deletes users. An editor creates posts. A regular user just reads.

Healthcare data is among the most sensitive data there is. Sending it to a cloud AI service is often not an option because of privacy requirements, regulatory compliance, or both.

For years, the promise of AI-assisted development felt just out of reach for WordPress developers. You could ask a chatbot to generate a block of PHP, paste it into your editor, run into a conflict, copy the error back into the chat, and repeat the whole cycle until something worked. It was useful, but it was also exhausting. The gap between "AI knows how to do this" and "AI can actually do this in my environment" stayed stubbornly wide.