Benefit from a cohesive system where individual components, such as LLM, MCP server, and MCP client, integrate seamlessly to deliver meaningful results.
Learn how to build self-healing CI/CD pipelines that fix minor code issues automatically, reduce developer toil, and keep your DevOps flow fast and reliable.
This article provides a hands-on tutorial for building AI agents using the Model Context Protocol (MCP) and C#, an open standard that enables Large Language Models (LLMs)
AI is reshaping cybersecurity. Here's how Google Gemini shields consumers on-device, while Microsoft Security Copilot automates enterprise detection and response.
AI agents expand attack surfaces, demanding safety by design, advanced red teaming, and shared benchmarks to build secure, trustworthy intelligent systems.
With growing computing power for AI and its misuse in cyberattacks like autonomous exploits, deepfake scams, and smart malware becomes even more worrisome.
Small language models (SLMs) offer 90% of the value of large models at a fraction of the cost. Devs can maximize AI ROI by training SLMs on domain-specific data.
In this article, we will explore the value of AI agents, introduce popular agentic AI platforms, and walk through a hands-on tutorial for building a simple AI agent.
Junior developers are shipping features faster with Cursor and GitHub Copilot, while senior engineers question if AI-assisted code is maintainable at scale.
AI-first backends let LLMs drive dynamic, personalized API logic in real time replacing static rules. Validation and guardrails keep them reliable and secure.
The era of AI autonomously doing the work is here. Agentic AI systems can plan multi-step workflows, make decisions, use tools, and coordinate with other agents.