AI & LLM

The OWASP LLM Top 10 exists because shipping an LLM to production without a security model is a new category of risk that the existing web application security playbook doesn’t fully cover. Prompt injection has held the #1 spot on that list since the first version was published, and it’s not there because researchers think it might be a problem someday. It’s been demonstrated against production systems at companies that knew what they were doing.

Before MCP existed, adding tools to an AI application meant writing the same glue code over and over. You had OpenAI’s function calling syntax. Anthropic had tool use with a slightly different schema. LangChain abstracted over both, but now you depended on LangChain’s versioning decisions. Every new model provider meant rewriting your tool definitions. Every new tool meant re-registering it across every AI integration you maintained.

Every vendor selling software right now claims their product is “agentic.” I’ve seen chatbots with a system prompt called an agent. I’ve seen a scheduled Python script described as autonomous AI. I’ve also shipped actual agents to production — at an insurance company, handling FNOL triage, policy lookup, and claims routing. The gap between what gets marketed as an agent and what you’d actually build is significant.

Overview # I just returned from Microsoft Ignite in San Francisco, and if you’re building AI agents for enterprise, this was the event you needed to attend.

Docker has introduced comprehensive MCP (Model Context Protocol) tooling that enables organizations to build custom catalogs with complete control over AI tool access. With over 220+ containerized MCP servers available and the ability to create private catalogs, enterprises can now deploy AI tooling with appropriate security guardrails.

In the rapidly evolving landscape of artificial intelligence, development teams face significant challenges when integrating multiple AI models into their workflows. The proliferation of different providers, APIs, and pricing models creates complexity that can slow down innovation and increase technical debt. This article explores a powerful solution: a Docker-based setup combining LiteLLM proxy with Open WebUI that streamlines AI development and provides substantial benefits for teams of all sizes.

In today’s fast-paced development landscape, AI coding assistants have become indispensable tools for developers seeking to maintain high-quality code while meeting demanding deadlines. GitHub Copilot stands at the forefront of this revolution, offering intelligent code suggestions that can significantly accelerate development. However, the true power of Copilot lies not just in its base capabilities, but in how effectively it can be customized to align with your specific project standards and best practices.

TL;DR: This guide walks you through building a production-ready RAG system using FastAPI, ChromaDB, MinIO, and OpenAI. Learn document chunking, vector embeddings, hybrid search, and real-world deployment strategies. Introduction # As a .NET developer watching the AI landscape evolve, I found myself both excited and skeptical. When tools like Claude.ai and ChatGPT started offering out-of-the-box RAG solutions, I wanted to build my own system with full control over the implementation.

Introduction # Large Language Models (LLMs) have become a cornerstone of modern AI applications, from chatbots that provide customer support to content generation tools for images and videos. They power virtual assistants, automated translation systems, and personalized recommendation engines, showcasing their versatility across industries. However, running these models on a local machine has traditionally been a complex and resource-intensive task, requiring significant configuration and technical expertise. This complexity often deters beginners and even intermediate users who are eager to explore the capabilities of LLMs in a private, local environment.

TL;DR # This article demonstrates how to build a REST API that converts natural language into SQL queries using multiple LLM providers (OpenAI, Azure OpenAI, Claude, and Gemini). The system dynamically selects the appropriate AI service based on configuration, executes the generated SQL against a database, and returns structured results. It includes a complete implementation with a service factory pattern, Docker setup, and example usage.

Introduction # Artificial Intelligence is transforming how we build applications, particularly in creating natural, conversational user experiences. This article guides you through building a full-stack AI chat application using .NET on the backend, Angular for the frontend, and Azure OpenAI for powerful language model capabilities, all connected through real-time SignalR communication.