Idempotency Keys in Distributed Systems

Most pipelines test code. Mine tests security at every layer. Just finished building a full DevSecOps pipeline from scratch and the difference between this and a regular CI/CD pipeline is massive. Here's what runs automatically on every single push: - 10 automated tests with coverage reporting - Snyk dependency scan catches vulnerable packages before they ship - Snyk SAST scan finds security bugs inside my actual Python code - Docker build containerizes the app automatically - Snyk container scan scans the Docker image itself for vulnerabilities 3 layers of security. Zero manual steps. Everything automated. The thing that clicked for me building this: security can't be bolted on at the end. By the time a vulnerability reaches production, it's already too late. The fix is to catch it at the dependency level, the code level, AND the container level automatically, on every push. That's what DevSecOps actually means in practice. Tech stack: Python · Flask · Docker · GitHub Actions · Snyk · pytest Repo is live if you want to see the pipeline in action https://lnkd.in/eVD9KPDS #DevSecOps #Snyk #Docker #GitHubActions #QAEngineering #SecurityTesting #Python #Automation #OpenToWork

💻 Build cross-language integrations with ease! Watch the demo of a .NET producer and Python consumer using RabbitMQ transport with NServiceBus. ➡️ https://lnkd.in/dMiCy74u #RabbitMQ #Integration #NServiceBus #Microservices

Rate limiting shouldn't come with a side of dependency hell. 🐍 Most Python rate limiters force a trade-off: either use a basic "fixed-window" script or pull in a heavy framework with a dozen sub-dependencies. This library was built to provide production-grade primitives with an emphasis on architectural flexibility and a zero-dependency footprint. The Technical Breakdown: ✅ 6 Algorithms: Beyond the standard Token Bucket. Includes Fixed/Sliding Windows and ADAPTIVE logic for dynamic scaling. ✅ 3 Backends: Native support for Memory and Redis, supporting both local-first and distributed environments. ✅ Zero Dependencies: Designed for high-security environments and lean builds. No requirements.txt bloat. ✅ Implementation: Clean integration via decorators for functions or middleware for web frameworks. If you are managing API quotas or protecting services from traffic spikes, the implementation details and performance focus are worth a look. https://lnkd.in/gnikRUHa #Python #SoftwareEngineering #SystemDesign #OpenSource #Backend #DistributedSystems

🚀 Microsoft Agent Framework 1.0 just shipped. From demos to production. The best of AutoGen + Semantic Kernel. ✅ Multi-agent workflows built-in  ✅ MCP + A2A cross-runtime collaboration  ✅ First-class C# and Python — same day, same features For the .NET developers — Time to turn your .NET strengths into AI-powered agents. 1.0 is just the start. Start building stable, scalable, and sustainable agent systems — today. 🔗 aka.ms/AgentFramework #MicrosoftAgentFramework #dotNET #CSharp #Python #AIAgents #AzureAI

I spent a few weeks migrating 5 microservices from C# + gRPC to F# + gRPC. Personal project, no deadlines, no pressure.... only fun!! I have a background in functional programming. But I had not touched anything in that paradigm for years. Day-to-day Python, C/C++, C# is imperative by default, and that default goes deep. The good part first: some things in F# are genuinely simpler. Discriminated unions for domain modeling, function composition with |>, pattern matching that covers every case and the compiler holds you accountable. Things that exist in C# too, but cost more code to express. The beginning was harder than I expected, precisely because of that background.... I knew what a pure function was. I knew what immutability meant. The problem is that knowing something conceptually does not mean your brain will reach for it first when a problem shows up. Years of writing loops and mutations build a reflex. My first instinct was still: create a variable, iterate, mutate. In F#, that path exists, but you are swimming against the current. The language pushes you to think about what the function receives, what it returns, how to compose that with the next step. It took me a while to stop mentally translating C# into F# and start formulating the problem in F# directly. Another real point: the compiler is strict, which is good, but error messages can be cryptic when you are still reactivating that way of thinking. You know something is wrong, but the message does not always point directly at it. In the end, it worked. The services are running, gRPC integrated without major surprises, and the code got leaner in several places. The most useful thing I learned was not technical. It was realizing that knowledge rusts when left untouched, and that years on the imperative autopilot leave a deeper mark than it seems. And on that note: AI is making this worse. Every time you let it write the code, debug the problem, or think through the design for you, you are not just saving time. You are letting your own knowledge rust a little more. It does not matter if it is functional programming, system design, algorithms, or anything else in tech. The muscle atrophies when it stops being used. #fsharp #dotnet #grpc #functionalprogramming

I just finished building a fully asynchronous FinTech Fraud Detection Pipeline, and here is a demo of it catching an anomalous transaction in real-time. Instead of making the user wait for a slow Machine Learning prediction, the Spring Boot API pushes the transaction to a Kafka topic and immediately frees up the thread. A decoupled Python worker analyzes the payload using an Isolation Forest model, detects the anomaly, and pushes the decision back to the frontend instantly via WebSockets. The Tech Stack: ☕ Java 21 & Spring Boot (REST API & WebSockets) 📨 Apache Kafka (Message Broker) 🐍 Python & Scikit-Learn (Isolation Forest ML Model) 🐘 PostgreSQL & Redis (Persistence & Caching) 🐳 Docker (Fully containerized multi-stage build) #Java #SpringBoot #Kafka #MachineLearning #BackendEngineering #SystemDesign

I’ve been working on a personal project to bridge the gap between "it works in dev" and "it's secure in prod." I’ve open-sourced a reference architecture that demonstrates how to harden a Kubernetes microservices stack. It’s not just a hello-world web demo.... It’s a lab for testing Zero-Trust principles, such as default-deny NetworkPolicies and TLS 1.3 termination. The main highlights: - Network Isolation: Using native K8s NetworkPolicies to block lateral movement. - Image Hardening: Moving away from legacy Ubuntu images to non-privileged, slim Python environments. - Security Gates: I integrated SAST and container scanning directly into the GitLab pipeline logic. I also included an "Engineering Log" in the README. Hopefully, it saves someone else a few hours of debugging. 🔗 Explore the Lab here: https://lnkd.in/eF86CJ68 #Kubernetes #DevSecOps #CloudSecurity #K8s #ZeroTrust #InfoSec

Full Stack U: What is a Script? In computing, a script is a structured sequence of instructions written in a scripting language—such as Python, Bash, JavaScript, or PowerShell—that is interpreted and executed by a host environment at runtime rather than compiled into machine code beforehand. Scripts are designed to automate tasks, control software behavior, manipulate data, or coordinate system operations, often serving as glue between components or as lightweight tools for repetitive workflows....

𝗨𝗡𝗟𝗘𝗔𝗦𝗛𝗜𝗡𝗚 𝗧𝗛𝗘 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗣𝗢𝗪𝗘𝗥𝗛𝗢𝗨𝗦𝗘: 𝗧𝗛𝗘 𝗦𝗘𝗖𝗥𝗘𝗧 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗𝗜𝗡𝗚 𝗟𝗜𝗚𝗛𝗧𝗡𝗜𝗡𝗚 𝗙𝗔𝗦𝗧 𝗔𝗣𝗜𝗦 𝗥𝗘𝗩𝗘𝗔𝗟𝗘𝗗 As we head into 2026, building scalable microservices requires a rock-solid understanding of how your code is organized from the first line. This guide demystifies the structural foundations of FastAPI to ensure your backend remains maintainable as your codebase grows. THE FASTAPI INSTANCE The core of every application begins with initializing the FastAPI class. This instance acts as the central router and configuration hub, serving as the bridge between your incoming HTTP requests and your internal application logic. Understanding how to instantiate this object correctly is the first step toward managing middleware, dependencies, and route decorators effectively. ROUTING AND PATH OPERATIONS Path operations are the fundamental building blocks of your API. By using decorators linked to your FastAPI instance, you define how the server responds to specific HTTP methods like GET or POST. This video breaks down how these functions map directly to URL paths, allowing for clean, readable code that handles client communication without unnecessary complexity. PARAMETER HANDLING AND TYPE HINTS FastAPI leverages Python type hints to perform automatic data validation and documentation. By defining expected types for path parameters, query parameters, and request bodies, you enable the framework to enforce data integrity before your logic even executes. This approach significantly reduces the surface area for bugs while providing built-in Swagger UI documentation for your endpoints. As a Senior Engineer, I cannot overstate that the structure of your application in 2026 is just as important as the logic inside it. FastAPI enforces good habits early by requiring explicit type definitions and clear route mapping, which saves hundreds of hours in debugging and refactoring down the line. Treat your project structure as your primary documentation. Tags: #FastAPI #Python #API #BackendDevelopment 📺 Watch the full breakdown here: https://lnkd.in/d7YeSp75

Your Python today is whatever PATH hands you. Which python3 tells the truth. 🔎 `which python3` It searches PATH and prints the first match. If you use pyenv, asdf, or virtualenv, you may see a shim, not the real interpreter. Fresh angle: treat this as a tiny audit across machines to guarantee reproducible environments. Before tests on a new CI runner, check which python3 and confirm with `python3 --version`. Small habit, big impact: you stop chasing strange failures caused by interpreter drift. 🐧 Run it right now. Tell me what you find. #linux #terminal #bash #commandline #devops #sysadmin #pythonprogramming #programming #softwareengineering #developer #coding #opensource #productivity #automation #tooling