Docker Cleanup Pro: Smart Image Management for Developers

Building a Backend System: Day 1 Kicking off FlowState, a workflow automation API (not generic todo app). Day 1 Focus: Modern Tooling & Optimization ✅ Python 3.13 & Django 6.0 (Latest Stable) ✅ uv for dependency management (Blazing fast) ✅ Multi-stage Docker build (Alpine based) ✅ Image size optimized to < 250MB 🐳 Why uv? It's Rust-based and solves Python packaging speed issues. Why Django 6? Leveraging the latest async capabilities and security features. Starting with a lean, modern foundation is key to scalable system design. Stack: Python 3.13, Django 6.0, PostgreSQL 17, Docker, UV. #BackendEngineering #Django #Python #Docker #BuildInPublic #SystemDesign #DevOps #UV

Today I faced a real issue while building a Docker image — and learned something important from it. Problem: While building the image, I got an error: “You must give at least one requirement to install” Root Cause: I was running "pip install" without specifying any requirements or missing the "requirements.txt" file in the Dockerfile. Solution: - Added a proper "requirements.txt" file - Updated Dockerfile with: "RUN pip install --no-cache-dir -r requirements.txt" - Rebuilt the image successfully Key Learning: Small mistakes in Dockerfiles can break the entire build process. Understanding error messages is a crucial DevOps skill. Tools Used: Docker | Python | Flask Every bug I solve makes me stronger 💪 #DevOps #Docker #Debugging #Python #javafullstack

We’ve all been there… “Code works perfectly on local” Production: 💥 I wanted to fix that gap — not with theory, but with something practical and repeatable. So I built a production-ready Python service setup: • Docker for consistency • systemd for reliability • Nginx + Blue-Green for zero-downtime deployments The idea was simple: 👉 Your service should never go down during deployment This blog is what I wish I had when I started building real systems. If you’re moving from scripts to production systems, this will help: https://lnkd.in/gZThnvK8 Would love your thoughts 🙌 #DevOps #Python #SystemDesign #Backend #Tech

I got tired of typing the same Docker commands every day. So I built something. 🐳 Introducing dockops — a Python CLI package I built and published on PyPI that simplifies common Docker tasks for developers. Instead of this: docker rm $(docker ps -aq) docker rmi $(docker images -f "dangling=true" -q) docker ps --format "table {{.Names}}\t{{.Ports}}" You just do this: ✅ dockops clean  → removes stopped containers, dangling images, unused volumes ✅ dockops ports  → shows all ports and which container owns them ✅ dockops health → CPU%, memory, uptime for all running containers Install it in one command: pip install dockops This was my first open-source Python package. Building and publishing it taught me more about the Python ecosystem than years of just using it. Full story on how I built it 👉 https://lnkd.in/gHzZS3B3 ⭐ GitHub: https://lnkd.in/gPVr5sUT 📦 PyPI: pypi.org/project/dockops #Python #Docker #OpenSource #DevOps #PyPI #BackendDevelopment

As a #Python developer, there’s a lot more beyond writing code i.e. Isolation -> packaging -> publishing -> distribution. Still using PYTHONPATH and sys.path and calling it a package…? Is virtual environments your only packaging strategy…? Still zipping files and calling it distribution…? You are not alone — most of us have been there. #Python #PythonPackaging #PyPI #PreCommit #DevOps #SoftwareEngineering #CodeQuality #Docker #CI #CD #Ruff #uv

Write code that doesn't break in production...! When building end-to-end pipelines, reading data from GitHub or external URLs is common. But relying on a "happy path" is a mistake. For robust development, always implement: Logging: To track the flow and capture specific error details. Exception Handling: To prevent the entire app from crashing and get clear "Unable to load" alerts. It’s a simple habit, but it’s what separates a beginner from a Pro Developer. #Python #MLOps #CleanCode #SoftwareEngineering #DataScience #CodingTips

The useful part of most AI code review tools fits in about 300 lines of Python. The other 100,000 are SaaS overhead. Fetch the diff. Chunk by file. One prompt per chunk with the PR description for context. One LLM call. Structured output posted as comments. Three cents. Three dependencies. Runs as a GitHub Action. A generic tool reviews generic code. Yours should know your conventions, your edge cases, your architecture. I open-sourced a reference implementation. Fork it, strip it, own it. Stop buying LLM wrappers. https://lnkd.in/gqgqY2Sk #AIEngineering #MultiAgentSystems #SoftwareArchitecture #OpenSource

Stop shipping massive, bloated Python containers. 🐳🐍 As a DevOps engineer, one of the easiest wins for performance and security is optimizing your FastAPI Dockerfiles. Moving from a single-stage "heavy" build to a multi-stage workflow isn't just about saving disk space—it’s about: ✅ Security: Removing compilers, pip, and OS packages in the final image. ✅ Speed: Faster CI/CD pipelines and quicker scaling during deployments. ✅ Efficiency: Using non-root users and slim base images to reduce the attack surface. Check out this breakdown: 1.2 GB (Bad) ➡️ 150 MB (Good) How are you optimizing your Python builds? Let's discuss in the comments! 👇 #DevOps #Docker #Python #FastAPI #CloudNative #ProgrammingTips

⚠️ Stop Using Just pip for Everything in Python pip install something # pray nothing breaks 🙏😅 If this is your workflow… you’re missing out. 🧱 OLD STACK (Fragmented) pip → install packages virtualenv → isolate environments pyenv → manage Python versions 👉 Works… but feels stitched together. ⚡ MODERN STACK (Clean & Fast) poetry → dependencies + packaging 📦 uv → installs + env management ⚡ 🚀 Why uv is blowing up: ⚡ 10–100x faster than pip 🦀 Built in Rust 🔁 Replaces pip + venv ✨ Minimal, clean workflow 💡 REALITY CHECK pip is not going anywhere ❗ But uv is redefining speed + DX 🚀 👀 MY TAKE Beginner → pip + virtualenv Serious Project → poetry Future-ready → uv 👀 💬 What’s your stack? #Python #DevOps #Cloud #BackendDevelopment #TechTrends

📓 Just published a FREE production-ready FastAPI authentication system on Docker Hub! ✅ JWT tokens ✅ Role-based access control ✅ Argon2 password hashing ✅ Docker containerized ✅ Pull & run in 2 commands Perfect for devs who need auth FAST without building from scratch. 🔗 Docker Image: https://lnkd.in/eb7_dHKr 💻 Source Code: https://lnkd.in/eKshJ5gz What would you build with this? Comment below! 👇 #FastAPI #Python #Docker #Authentication #Backend #OpenSource #100DaysOfCode