Ahsan Sheraz’s Post

🚀 pyresilience — 7 resilience patterns in 1 decorator (~1000 downloads last month 🔥) 💡 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 ♻️ Resharing to support the Python community 🤝 💬 How are you handling resilience in Python today? #Python #PythonDeveloper #PythonDev #BackendDevelopment #SoftwareEngineering #DistributedSystems #Microservices #SystemDesign #ResilienceEngineering #FaultTolerance #HighAvailability #ScalableSystems #PerformanceEngineering #AsyncIO #FastAPI #Django #Flask #APIEngineering #CloudNative #DevOps #SRE #OpenSource #OpenSourceProject #PyPI #PythonLibraries #DeveloperTools #TechInnovation #BuildInPublic #Programming

🚀 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

🚀 **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

🚨 𝟱𝟬𝟬,𝟬𝟬𝟬+ 𝗹𝗶𝗻𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗰𝗼𝗱𝗲... 𝗹𝗲𝗮𝗸𝗲𝗱 𝗯𝘆 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝗶𝘁𝘀 Claude 𝗖𝗼𝗱𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝘃𝗶𝗮 𝗮 `.𝗺𝗮𝗽` 𝗳𝗶𝗹𝗲 𝗶𝗻 𝗮𝗻 𝗻𝗽𝗺 𝗿𝗲𝗹𝗲𝗮𝘀𝗲 — 𝗿𝗲𝘃𝗲𝗮𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. (Axios) Within hours, the internet did what it does best: → mirrored it on GitHub → analyzed it → rebuilt it One repo stood out: 👉 https://lnkd.in/gEJzYmXx But the real twist? Developers moved beyond copying. They created clean-room reimplementations in Rust & Python (Claw Code) — replicating the architecture without using the original code. (Claw Code) https://lnkd.in/guiUu3Ch This is classic software history repeating itself. 💡 Lesson: It’s not always hacks that break systems — sometimes it’s a single config mistake. And sometimes, that mistake teaches the whole industry how your system works. #AI #DevOps #Security #OpenSource #SoftwareEngineering

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

Yoooo guys, I delivered as promised. 😀 Told you guys I read a long blog on building multi agent on langchain blog, so I thought about simplifying it... If you’ve ever tried to put an AI agent into a real production backend, you know it usually ends in a mess of bloated context windows, blocking synchronous code, or agents getting amnesia the second a serverless function dies. The heavy enterprise frameworks feel like wrestling an octopus just to make a simple API call. So, I built Swarm Agent Kit 🐝. It’s a minimalist, state-aware multi-agent orchestration framework for Python. I built it to handle the actual heavy lifting of production: ⚡ Native async/await execution (FastAPI ready) 🧠 Global state management (No more token-bloat) 💾 Bring-Your-Own-Database (BYOD) persistence hooks (Pause and resume agents days later) I just dropped a full blog post breaking down exactly why I built it, how the routing engine works under the hood, and how you can use it to build agents that actually survive in production. Check out the blog and the source code below. Would love to hear what you guys think or if you want to contribute! 📖 Read the full story here: https://lnkd.in/eWFbtY27 Star the repo / check the code: https://lnkd.in/eQ7pCWdA 📦 Try it out: pip install swarm-agent-kit Let's build! #Python #AI #MachineLearning #OpenSource #LangChain #Agents #SoftwareEngineering

🚀 From “It Works” to “Production-Ready” Python — Here’s What I Learned Most Python tutorials teach you how to make things work. But in real-world systems, “working code” ≠ “production-ready code.” After building and experimenting with multiple projects (Flask apps, APIs, automation workflows), I realized there’s a big gap between: 👉 Writing code 👉 And writing reliable, scalable, production-grade code So I created a Production-Ready Python Guide to bridge that gap. 📘 What this guide covers: ✔ Writing clean, maintainable Python code (beyond basics) ✔ Structuring real-world projects (Flask, APIs, services) ✔ Error handling, logging, and debugging strategies ✔ Performance optimization & best practices ✔ Writing code that actually survives in production 💡 Who this is for: - Developers stuck at “tutorial level” - Backend engineers leveling up to production systems - Anyone preparing for real-world tech roles This isn’t just theory — it’s based on practical implementation experience. If you’re serious about moving from “learning Python” → “building real systems”, this might help. 👇 Guide link : https://lnkd.in/gGV3J4m3 YouTube video link : https://lnkd.in/geHNuyFj Would love your feedback and thoughts 🙌 #Python #BackendDevelopment #SoftwareEngineering #Coding #Developers #Flask #APIs #Programming #TechCareers #LearnToCode

I started learning Rust recently, using LLMs to help me along the way. At first, it was just curiosity. But something felt different. The code was stricter, clearer, and surprisingly… easier to trust. Strong typing and clean memory management made LLM outputs feel more reliable. Then I stumbled on something unexpected. A Python package manager called uv (https://lnkd.in/diP_EW-2). It replaces pip, pip-tools, pipx, poetry, pyenv, twine, virtualenv and more. What caught my eye wasn’t just that it’s written in Rust. It was this : ~83k GitHub stars Still not even version 1.0 (0.11.4 Currently) pip is around 10k. npm is under 5k. pnpm and yarn is around 40k, brew at 47k: Even Python itself is close - 72k. So why is this new tool growing so fast? It didn’t feel like hype. It felt like a signal. Maybe developers are getting tired of “good enough.” Maybe speed, simplicity, and better design are finally winning. And maybe… tools built with more care are getting noticed faster than ever. Feels like something is changing. Are we seeing the beginning of a shift in how developers choose tools? #Rust #Python #OpenSource #DeveloperTools #LLM

A lot of businesses I speak to have the same problem: Their operations depend on manual work, scattered tools, and repeated effort. • Reports created manually every week • Data copied between systems • APIs that don’t talk to each other properly • Slow backend systems affecting user experience And over time, this starts costing time, money, and growth. This is exactly where I’ve been helping teams using Python, Django, and FastAPI. Instead of adding more tools, the focus is on: ✔ Automating repetitive workflows ✔ Building clean and scalable backend systems ✔ Connecting systems through reliable APIs ✔ Making processes faster and more efficient Sometimes small changes lead to huge time savings. If you’re facing similar challenges or planning to improve your systems, feel free to reach out — always open to discussing ideas. #Python #Django #FastAPI #Automation #BackendDevelopment #SoftwareSolutions #TechConsulting

🚀 From Learning Python to Understanding Backend Systems When I first started learning Python, my focus was mainly on syntax and basic problem-solving. But as I explored more, I realized that backend development is where logic meets real-world application. That’s when I began diving deeper into the Python backend ecosystem. Instead of just learning tools, I started understanding how backend systems actually work—how requests are processed, how data flows between the server and database, and how APIs connect everything together. 🔧 Tools & Technologies I’m Exploring: • Python for core logic • Django for structured and scalable applications • Flask / FastAPI for lightweight API development • Relational Databases for data management • REST APIs for communication between systems • Git & GitHub for version control • JWT for authentication • Basic backend security practices • Deployment fundamentals 💡 What Changed in My Approach: Earlier, I focused on “what to learn.” Now, I focus on “how things work.” This shift helped me: • Understand backend architecture more clearly • Write better and cleaner code • Think like a developer instead of just a learner I’m still at the beginning of this journey, but I’m consistently building, experimenting, and improving every day. The goal is simple — to become a backend developer who not only writes code, but understands the system behind it. Excited for what’s ahead 🚀 #Python #BackendDevelopment #Django #Flask #FastAPI #RESTAPI #LearningJourney #SoftwareDevelopment #snsinstitutions #snsdesignthinkers #designthinking