MCP for .NET Devs: Universal Tools for AI-Powered Apps

Before explaining containers, it is important to get an idea about images. What is an image? A image is a complete blueprint of an application. An image contains all the components needed to run an application, such as: - Application source code - Runtime (Node.js, Java) - Libraries and dependencies - Configuration files As an example, suppose a developer wants to show developed software to a client without hosting it. To do this, the developer can create an image and upload it to a container registry. After that, the client can pull the image from the registry and run an instance of that image locally. That running instance is called a container. To run a container, you need a container runtime. A container runtime is software responsible for creating and managing containers. Some commonly used container runtimes include Docker Engine, containerd, Podman, and CRI-O. Why do we need containers? Containers are widely used in production environments because they are lightweight and consume fewer resources such as CPU, memory, and storage. Before containers became popular, applications were commonly deployed directly on servers or inside virtual machines(VM). The problem with virtual machines is that each VM runs its own guest operating system, which requires additional resources, increases startup time, and makes them more expensive to maintain. How do containers work? Running virtual machines requires software called a hypervisor, which allows multiple virtual machines to share the same physical hardware. It allocates CPU, memory, storage, and networking resources to each virtual machine while keeping them isolated. Unlike virtual machines, containers do not include a complete operating system. Instead, they share the host machine's Linux kernel and package Linux user-space components such as libraries, utilities, and application files. Even though containers share the same kernel, they remain isolated from one another. The Linux kernel uses namespaces to isolate resources such as processes, networking, file systems, and users, while cgroups control how much CPU and memory each container can use. When an application inside a container needs to access system resources, it makes Linux system calls that are handled by the host kernel. Because containers share the host kernel instead of running their own operating systems, they start much faster and use significantly fewer resources than virtual machines. #docker #containers

🚀 Built a Custom Active Directory MCP Server for Claude Desktop I recently built a custom Model Context Protocol (MCP) server that lets Claude Desktop securely manage Active Directory using natural language. With it, I can: - 👤 Create, update, enable/disable, and unlock users - 🔑 Reset passwords - 👥 Manage group memberships - 🔍 Search users, groups, computers, and OUs Security was a priority, with confirmation prompts for sensitive actions, audit logging, and secure PowerShell execution. Tech Stack: Node.js, TypeScript, MCP SDK, PowerShell, Active Directory. This project shows how AI can simplify everyday IT administration while keeping enterprise security in mind. #ActiveDirectory #MCP #NodeJS #TypeScript #PowerShell #ITAutomation #SystemAdministrator #AI

Automation Diaries #10 Building smarter IT with automation, AI and infrastructure. A 20-line Bash script that saves me hours. One of the biggest lessons I've learned is that the best automation isn't always the most complicated. Some of the tools I've built span hundreds—or even thousands—of lines of code. Others barely fill half a screen. One of my favourites is a simple Bash script that does one job exceptionally well. It: - Reads a list of Linux servers. - Connects to each host over SSH. - Checks disk utilisation. - Reports only the filesystems that have crossed a defined threshold. This isn't my production script—I've simplified it for readability—but it demonstrates the idea perfectly. Before I wrote it, checking disk usage meant logging into server after server, running the same commands, and scrolling through pages of healthy filesystems just to find the one that needed attention. Now I run one command and within seconds I know exactly where to look. That's the real value of automation. Not writing clever code. Removing repetitive work. Less logging in. Less scrolling. Less noise. More time solving problems that actually need an engineer. That's the philosophy I try to follow: Don't automate because you can. Automate because it gives you back time. Sometimes the smallest scripts deliver the biggest return on investment. What's the simplest script or automation you've built that ended up saving you the most time? #AutomationDiaries #Automation #Linux #Bash #PowerShell #Scripting #DevOps #PlatformEngineering #Infrastructure #SysAdmin

🚀 .NET 10 is changing the game! Microsoft's latest Long-Term Support (LTS) release focuses heavily on cloud-native speed, cleaner code, and deployment freedom. Top 5 updates you need to know: 🐳 No-Dockerfile Containers : Build container images natively from console apps. 💻 C# 14 Upgrades : Field-backed properties drastically reduce boilerplate code. 📦 Build Once, Run Anywhere : Enhanced file-based apps execute without local installs. ⚙️ One-Shot Execution : Run tools seamlessly via the new dotnet tool exec. 🐚 Native Tab-Completion : Smooth shell integration for PowerShell and Zsh. With 3 years of full support, .NET 10 makes development smarter and faster.

Our new weekly video demonstrates how .NET MCP server can expose a skill as a resource. Once exposed, the AI agent in the client uses the skill to answer a question. LLM runs locally in Ollama. #dotnet #csharp #programming #softwareengineering #mcp https://lnkd.in/eMe2MNkV

🐧 Linux `find` Command Cheat Sheet ```bash # Basic Search find .             # Everything find . -type f         # Files only find . -type d         # Directories only find . -name "*.txt"      # Find .txt files find . -iname "*.TXT"      # Case-insensitive search # Search by Size & Time find . -size +10M        # Files >10MB find . -mtime -7        # Modified in last 7 days find . -empty          # Empty files/directories # Permissions & Owner find . -perm 644        # Permission 644 find / -user root        # Owned by root # Execute Actions find . -name "*.log" -delete find . -name "*.sh" -exec chmod +x {} \; # Optimization find . -maxdepth 2 find . -path "*/.*" -prune -o -print find . -printf "%f\n" ``` 💡 Tip: Always use quotes with wildcards. ✅ `find . -name "*.txt"` ❌ `find . -name *.txt` #Linux #DevOps #AWS #SysAdmin #LinuxCommands #Cloud CloudDevOpsHub

Less than 1% of public GitHub is bare-metal C or RTOS firmware. And the real number is worse than that, because the firmware actually worth learning from (the code shipping in cars, infusion pumps, telecom infrastructure) is exactly the code that will never be public. Nobody running critical infrastructure is going to let a model train on their firmware and expose its internals to the world. That makes a general "firmware expert" model nearly impossible to fine-tune. You can build one for a single company, but then it doesn't generalize past their IP. So what do you do? The answer we've bet the company on: stop trying to make the model smarter, and make the loop tighter. Everything a model outputs is a prediction — how to implement this driver, how to fix this bug. You can spend enormous inference on one sophisticated prediction, analyzing the entire codebase and every datasheet at once. Or you can make a cheap, shallow prediction and immediately check it against something that cannot be wrong. Then another. Each cycle throws away most of the search space, because you're debugging what's actually on the board instead of everything that might be. The analogy I keep coming back to is binary search. You don't find the value by reasoning harder about the array. You guess, you check, you discard half the space, and you repeat. Each iteration bounds the answer tighter, and the probability the output is still wrong shrinks toward zero — but only if the thing doing the bounding is deterministic. In pure software the verifier is usually more code: tests that mock the world and can be wrong in the same ways the code is. In embedded, the verifier can be the hardware itself. Compile it, flash it, run it on real silicon with test equipment watching. The board doesn't hallucinate. Either the peripheral came up or it didn't. Either sleep current is 2 µA or it's 40. Atoms are the ground truth. That's the whole thesis behind Embedder: build, flash, test, and debug in a closed loop that runs until the evidence says the code works — then show the engineer that evidence instead of asking them to trust an output.

⚡ What's New in .NET 11? Here's Everything You Need to Know. Microsoft is making .NET smarter, faster, and AI-ready with .NET 11 Preview. From AI-first development and runtime performance improvements to cloud-native enhancements and a better developer experience, .NET 11 is built for the next generation of applications. 📌 Here's a visual summary of the Top 10 features every .NET developers should explore. 💬 Which feature are you most excited to try in .NET 11? 👇 Let me know in the comments! #DotNet #DotNet11 #AspNetCore #CSharp #Microsoft #AI #GenerativeAI #SoftwareArchitecture #SoftwareEngineering #Developer #CloudNative #Azure #Programming #TechInnovation #TechCommunity #AjmalChavan

Your server is writing logs 24/7. Every monitoring script. Every backup. Every report. All writing to log files. Without rotation your disk fills up and everything crashes. Here is how I solved it 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Log files grow forever. 1 month of logs → gigabytes of data. Disk fills up → server stops working. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Two stage rotation system. 𝗦𝘁𝗮𝗴𝗲 𝟭 — Compress after 30 days find $LOG_DIR -name "*.log" -mtime +30 | while read FILE; do   gzip $FILE done cpu.log → cpu.log.gz gzip reduces file size by 60%+ 𝗦𝘁𝗮𝗴𝗲 𝟮 — Delete after 60 days find $LOG_DIR -name "*.gz" -mtime +60 -delete Compressed logs older than 60 days are permanently removed. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁: Day 0-30 → readable log files Day 30-60 → compressed .gz files Day 60+ → permanently deleted 60 day audit trail. 60% disk space saved. Zero manual intervention. All automated via cron at midnight. This is exactly what logrotate does under the hood. Now I know why it exists. #DevOps #Linux #Bash #Automation #CloudComputing #DevOpsEngineer #OpenToWork

🐧 Why Linux Engineers Use "grep" Every Day Imagine searching through a log file with 2 million lines. Scrolling isn't an option. This is where "grep" becomes indispensable. 👇 ⚙️ What is "grep"? "grep" searches text for lines that match a pattern. It works with: • Files • Command output • Logs • Configuration files 🚀 Find a Specific Word grep "ERROR" app.log Displays only the lines containing ERROR. 🔍 Search Recursively grep -r "Listen 80" /etc Searches all files under "/etc" for the specified text. ❌ Exclude Matches grep -v "^#" sshd_config Shows all lines except comments. 🧠 Why Engineers Use It ✔ Analyze log files ✔ Search configuration files ✔ Filter command output ✔ Debug production issues 📈 Real-World Scenario Users report login failures. Instead of reading the entire authentication log, you run: grep "Failed" /var/log/auth.log Within seconds, you can focus only on the relevant entries. ⚠️ Important Insight "grep" supports regular expressions, making it far more powerful than a simple text search. Learning regex unlocks much of "grep"'s full potential. 💡 Key Insight In Linux, the fastest way to find an answer is often not to read everything—it's to filter out everything you don't need. #Linux #DevOps #SystemAdmin #LinuxCommands #Automation #Troubleshooting #CloudEngineer #SRE