Articles

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.

Most companies have AI agents running in various projects or departments, but few have managed to roll them out well across an entire organization. This article tries to answer the question: What is the best organizational structure for a team of AI agents?

Discover how to build an AI agent that runs its own LLM experiments with autoresearch, an open-source Python tool that automates the process of training language models.

The author's personal website, salih.dev, currently redirects to another site, but they want to create a proper site with custom redirects.

Codex and Claude Code are two of the strongest AI coding assistants available, but they approach software development differently. Codex focuses on autonomous execution, while Claude Code emphasizes developer collaboration and reasoning quality.

Most AI support agent tutorials focus on resolving FAQs, but this approach breaks down when dealing with sensitive tickets. This article presents a pattern for escalation-first design, which commits routing decisions before text generation and verifies answers with AI judges.

Discover the power of Manis AI and learn how to automate real-world projects with this comprehensive guide.

In this article, we explore the concept of building a multi-tool shopping assistant that uses artificial intelligence to answer real questions and provide a more personalized shopping experience.

The announcement of OpenAI Frontier marks an important organizational inflection point in how companies are adopting AI, transitioning from AI as a tool to AI as an organizational capability.

Building a single AI agent is a solved problem, but creating a multi-agent system that runs in production requires a different approach. This article answers infrastructure questions and provides working code for a real system.

Personalization is a key feature in AI-powered applications, but it can also be a major cause of agent failure. In this tutorial, we'll explore the architectural patterns for building reliable and maintainable AI agents that remember user preferences without breaking context.

Every major programming language in history was built around the same assumption: that they are meant for developers. But what if the next programming language isn't designed with developers in mind?