Ahsan Sheraz’s Post

🚀 pyresilience — 7 resilience patterns in 1 decorator 💡 What is resilience? Your app keeps working even when dependencies fail, slow down, or overload. No crashes. No hanging. Just smart recovery. ⚠️ Pain point: Python teams often stitch together: • Retries ("tenacity") • Circuit breakers ("pybreaker") • Timeouts ("asyncio", "signal") • Rate limiting ("limits", "slowapi") • Fallbacks (custom code) 👉 These don’t coordinate → messy + inconsistent failure handling 📊 Existing tools: • "tenacity" (retries ~263.6M downloads/month) • "pybreaker" (circuit breaker ~9.6M downloads/month) 👉 Great individually, not unified ⚡pyresilience Benchmark: 🚀 pyresilience → 0.64 μs (🔥 ~10.4x faster) 🐢 tenacity → 6.64 μs 🛠️ What pyresilience does: One decorator with: ✅ Retry ✅ Circuit Breaker ✅ Timeout ✅ Fallback ✅ Bulkhead ✅ Rate Limiter ✅ Cache ➡️ Works together, not glued ➡️ Zero dependency ➡️ Sync + Async ➡️ High performance Frameworks: 🌐 FastAPI • Flask • Django 👨💻 For all Python developers 🔗 GitHub: https://lnkd.in/d-SRygNQ 🔗 PyPI: https://lnkd.in/dRg2H4D5 🔗 Docs: https://lnkd.in/dxZ4xYkw 💬 How are you handling resilience in Python today? #Python #SoftwareEngineering #BackendDevelopment #PythonDev #Microservices #DistributedSystems #FastAPI #Django #Flask #ResilienceEngineering #SystemDesign #FaultTolerance #OpenSource #DevTools #BuildInPublic

A lot of backend discussions today revolve around performance. One framework that impressed me recently while building APIs is FastAPI. What stands out is how quickly you can build clean, high-performance APIs without adding too much complexity. A few things I personally like while working with it: • Automatic API documentation without extra setup • Type hints that make code easier to maintain • Great performance for async workloads • Very simple to connect with existing Python services For projects that are API-first — microservices, integrations, or mobile backends — it feels very efficient. Sometimes the right tool isn’t the biggest framework… it’s the one that keeps things simple and fast. Curious to hear from other developers — Are you using FastAPI, or sticking with Django or Flask for APIs? #FastAPI #Python #BackendDevelopment #APIDevelopment #SoftwareEngineering #Developers

I kept writing Django APIs… but something felt off. The code was working. Responses were coming. But if someone asked me: 👉 “What actually happens when a request hits your API?” …I didn’t have a clear answer. That bothered me. So I went back to basics. Not tutorials. Not copying code. Just understanding one simple flow: User → request → view → model → database → response And suddenly, things started clicking: Patient.objects.all() is not just a line of code… it’s a query hitting the database and returning structured data. request is not just a parameter… it’s literally everything the user is sending to your backend. GET, POST, PUT, DELETE are not just methods… they define how your system behaves. The biggest realization? 👉 I was focusing on “how to write code” 👉 instead of “how things actually work” Now I approach backend differently: I don’t start with code. I start with flow. And that small shift is making a huge difference. Still learning. But now it feels real. #Django #BackendDevelopment #Python #LearningInPublic #SoftwareEngineering #BuildInPublic

🚀 **Day 29/30 – 30 Days of Python Project Challenge** Consistency builds skill. Skill builds confidence. 🚀 As part of my 30-day challenge, I’m focused on solving real-world problems while strengthening core development concepts. 🧠 Today’s Project: **Website Status Checker** I built a Python-based tool that monitors whether websites are **UP or DOWN** using HTTP requests, helping identify server issues quickly and efficiently. ✨ Why this project matters: In today’s digital world, uptime is critical. This project demonstrates how Python can be used to build simple monitoring tools that simulate real-world systems used in DevOps and backend operations. ⚙️ Key Features: 🌐 Multi-Website Monitoring: Check multiple URLs in one run 📊 Status Code Insights: Displays HTTP responses (200, 404, 500, etc.) 🎨 Colored Output: Uses Colorama for clear and readable terminal results ⚠️ Error Handling: Detects unreachable or invalid websites gracefully ⚡ Fast Execution: Lightweight and efficient with minimal setup 💡 Concepts Applied: HTTP Requests using Python (requests library) Exception Handling for robust error management Working with APIs and status codes Clean and readable terminal UI with color formatting Basic automation and monitoring concepts 🔗 GitHub: https://lnkd.in/dcDpkarZ 📌 Takeaway: Even simple scripts can solve real problems. Building tools that monitor systems is a powerful step toward understanding real-world software and infrastructure. On to Day 30. 🔥 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #Automation #DevOps #Backend #SoftwareDevelopment #Coding #Learning #OpenSource #Projects

🚀 Python backend is easy to learn… but HARD to master 👀 Most devs stop at basics. Few actually build production-ready systems. Here’s what you’re probably missing 👇 . ⚡ FastAPI isn’t just another framework It gives you auto docs, speed & clean APIs out of the box  ⚡ Async/Await is no longer optional If your code blocks → your app dies under load 💀  ⚡ Type hints = not “extra” They’re your safety net in real-world projects ⚡ Dependency Injection = clean architecture Write scalable code, not messy hacks 🔥 Real backend dev stack (2026): FastAPI + Pydantic + SQLAlchemy + Alembic + pytest  . ❌ Stop writing only scripts ✅ Start building scalable systems 💬 Beginner writes code 💡 Pro designs systems 📌 Save this if you’re serious about backend development . #Python #FastAPI #Backend #Coding #Developers #Programming #Tech #SoftwareEngineering

Choosing the wrong Python framework can cost you months of development time. Django, FastAPI, and Flask are all powerful, but choosing the right one depends entirely on your product’s requirements, scalability needs, and long-term vision. At Acquaint Softtech, we help businesses make the right architectural decisions from day one to avoid rework, delays, and performance bottlenecks later. Here’s a quick breakdown: • Django – Ideal for full-scale, structured applications where speed of development and built-in features matter • FastAPI – Best for high-performance APIs that demand speed, scalability, and modern async capabilities • Flask – Perfect for lightweight projects, prototypes, and applications that need flexibility with minimal structure Choosing the wrong framework often leads to unnecessary complexity, performance issues, or costly migrations down the line. We focus on aligning the framework with your product goals, ensuring your Python system is scalable, efficient, and production-ready from the start. Looking to build the right Python architecture for your next product? 📩 sales@acquaintsoft.com 📞 +1 773 377 6499 🌐 https://acquaintsoft.com #pythondevelopment #django #fastapi #flask #softwarearchitecture #backenddevelopment #scalableapplications #offshoredevelopment #acquaintsofttech

Zapier costs $100/month. Make costs $100/month. n8n costs $0 if you self-host — and it can write Python. I've been using n8n for AI automation workflows and the thing that surprised me most: it's not a dumbed-down no-code tool. It's a proper automation platform that happens to have a visual interface. When the GUI isn't enough — you write JavaScript or Python directly in the node. No plugins. No workarounds. 5 things that make it different: ⚡ AI-native: LangChain-based agent nodes built in ⚡ 400+ integrations, 900+ templates ready to use ⚡ Custom code (JS or Python) in any node ⚡ Full self-hosting — your data never leaves your server ⚡ Enterprise SSO + air-gapped deployment for regulated industries 181k GitHub stars. This is what Zapier should have been. Full guide in comments 👇 #n8n #WorkflowAutomation #SelfHosted #AIAgents #NoCode #OpenSource #DevOps

🔗 Working with APIs in Real Projects Taught Me This While working on backend development, I got hands-on experience integrating APIs into a Django application. And that experience changed how I look at backend systems. 💡 What I learned: APIs don’t always return clean or expected data Validating responses before using them is important Proper error handling avoids unexpected failures Understanding request–response flow makes debugging easier ⚡ What stood out to me: Working with APIs is not just about connecting them — it’s about handling real-world scenarios properly. This gave me a much clearer understanding of how systems communicate in backend development. Still learning and improving every day 🚀 How has your experience been while working with APIs? #Python #Django #BackendDevelopment #API #SoftwareEngineering #LearningInPublic

Python finally has a backend framework that feels… complete. A lot of developers are still choosing between Flask and Django… But there’s another framework quietly gaining serious momentum. 👉 FastAPI. Here’s why it’s getting so much attention: ⚡ Insanely fast (comparable to Node.js) 🧠 Built-in data validation (no more messy manual checks) 📄 Automatic API docs (Swagger, out of the box) 🔄 Async support = scalability by default This is not just “another Python framework.” It feels like what modern backend development in Python was always meant to be. If you’re building: 🔹 SaaS products 🔹 AI tools 🔹 Scalable APIs FastAPI is definitely worth exploring. I’ve started using it in my projects and honestly, the developer experience is on another level. Clean code. Less debugging. Faster development. #FastAPI #Python #WebDevelopment #SaaS #Backend

Docker Image Optimization - Smaller Images, Faster Builds (~70% in my case) My image size went down by 70%. Here's what worked out for me. I'm starting something small. Whenever I learn something worth sharing, I'll post it here - no theory, no fluff. Just a real problem and what I did about it. First up: Docker image optimization. These apply to almost any containerized app - Spring Boot, Python, Node, etc. What made the difference 1. Multi-stage builds Separate build environment from runtime image. The final container only needs the built app, not compilers, build tools, or dev dependencies. Biggest size reduction. 2. Layer caching Copy dependency files first (package.json, pom.xml, requirements.txt), install dependencies, then copy source. If dependencies don't change, Docker skips reinstalling them - builds get significantly faster. 3. Combine RUN commands + cleanup Each RUN creates a new layer. Install and clean in the same RUN or the size is already baked into the previous layer. 4. Slim / Alpine base images Switching from node:20 to node:20-alpine takes the base image from ~1GB to ~170MB. Most official images have an alpine or slim variant - use it unless you have a reason not to. 5. .dockerignore Exclude node_modules, .git, logs, env files, build artifacts so they don't get sent in the build context. None of these are advanced. But they compound fast - and most projects I've seen skip at least two of them. What would you add? Anything I missed - drop it below. #Docker #Backend #LearningInPublic #SWE