Pybgworker v0.3.0 Released on PyPI with Improved Task Handling

A client once told me: “Everything was working perfectly in development.” Production disagreed. The issue? Timezone handling. Locally, everything was UTC. On the server, it wasn’t. Scheduled jobs started running at the wrong time. Reports were inconsistent. Nothing crashed — which made it worse. Silent failure. We fixed it by: --Enforcing UTC at database level --Normalizing all datetime inputs --Adding logging specifically for time-based triggers --Writing one integration test that simulated production timezone The lesson wasn’t about Django or Python. It was about assuming environments behave the same. They don’t. If you build backend systems: Always treat environment differences as a first-class risk. #BackendDevelopment #Django #ProductionIssues #SoftwareEngineering

While exploring the LangChain open-source repository, one thing became very clear: tests are first-class citizens in production codebases. So I spent some time understanding how testing works in Python using Pytest. Here are a few things I learned: • How Pytest discovers tests - it automatically tracks files starting with test_ and functions named test_*. • Writing unit tests - the typical flow follows Arrange => Act => Assert, keeping tests simple and readable. • Fixtures - reusable setup logic that prepares objects or environments needed by tests. • Advanced fixtures - explored yield fixtures, fixture scopes, fixture dependencies, and some built-in fixtures provided by Pytest. • Parametrized tests - a clean way to run the same test logic with multiple inputs. • Mocks (high level) - useful when isolating units of code that depend on external systems or services. • Testing specific scenarios - validating different edge cases and behaviors of a function. • Test design principles - things like test isolation and the single-assertion principle to keep tests reliable and maintainable. One interesting realization: Sometimes, reading tests are the fastest way to understand how a system is supposed to behave. Open-source repositories are an incredible place to learn real engineering practices. #opensource #python #pytest #testing #softwareengineering

pip install gone?? It’s kind of crazy when you think about how many tools and packages exist in the Python ecosystem. Yet we’re always looking for the same thing: something efficient, simple, and easy to set up. That’s where I’ve found uv to be useful compared to pip. Most of us are used to the traditional workflow - pip install, - manage a requirements.txt, - run pip freeze, deal with version drift later. uv simplifies that. Instead of manually maintaining requirements.txt, it uses: - pyproject.toml to define your dependencies - uv.lock to automatically lock exact versions Much faster installs. Cleaner virtual environment management. It feels like a modern upgrade to the old pip workflow. It’s newer, so teams should align before switching. But if you’re looking for a smoother Python setup experience, it’s definitely worth exploring. Have you used uv? What’s your experience? #uv #pip #moderndatascience

Gunicorn 25.1.0 Released Excited to announce a new release of Gunicorn, the Python WSGI HTTP Server. Key highlights: gunicornc - Control Interface Introducing an interactive CLI for managing running Gunicorn instances, inspired by birdc (BIRD routing daemon). Connect via Unix socket to view workers, scale dynamically, reload config, and gracefully shutdown - all without restarting. Dirty Stash New shared key-value store accessible across all dirty workers. Perfect for feature flags, rate limiting, or caching shared state. ASGI Now Stable The native asyncio-based ASGI worker for FastAPI, Starlette, and other async frameworks is now production-ready. Install: pip install gunicorn==25.1.0 Release notes: https://lnkd.in/eqGnSuqD Documentation: https://gunicorn.org #python #gunicorn #wsgi #asgi #webdevelopment #opensource #devops #backend

As a person in tech I don't want to remember the commands every time my tech stack changes. So I built dashb, a CLI tool that auto detects your projects and gives the right commands instantly. Once you download it just run db in any directory. No config and no setup. A few features I am proud of- 1) db doc- checks your project health. 2) db add- add custom shortcuts for your workflow. 3) db stats- see which commands you use the most. Works across Python, Node.js, Rust, Go, Docker and many more. Almost 300 downloads. Download now: npm install -g dashb PS- If you have any suggestions or want to add new features feel free to reach out. #CLI #DeveloperTools

Recently, I implemented chunked file upload functionality in Django using django-chunked-upload, and it significantly improved performance and reliability. In one of my recent projects, I needed to upload large video files. Instead of sending the entire file in a single request (which often causes timeout or server limits), I implemented chunk-based uploading. Using chunked upload: ✔ Reduced upload failures ✔ Improved overall system stability ✔ Enabled accurate frontend progress tracking ✔ Successfully handled large video uploads without Cloudflare request size restrictions By splitting large files into smaller chunks, the application can process uploads more efficiently and avoid common limitations like request timeouts or proxy size limits. #Django #Python #BackendDevelopment #DRF #WebDevelopment #SoftwareEngineering #ScalableSystems

Source Code 👉 https://lnkd.in/gb34T76M . . Just built a Weather App using Python! I challenged myself to create a simple but reliable weather application that can fetch weather data for any city worldwide. Instead of only focusing on functionality, I also focused on handling real-world errors properly, such as: 👉Invalid API keys 👉City not found 👉Server issues 👉Network connection problems 👉Request timeouts The goal was to build something minimal, clean, and resilient. Through this project I practiced: ✔ API integration ✔ Python exception handling ✔ HTTP status code management ✔ Writing cleaner and safer code Projects like this remind me that good software isn’t just about features — it’s about handling failures gracefully. #PythonDeveloper #Programming #BuildInPublic #CodingJourney #SoftwareEngineering

I recently posted about "Agentic Workflows" and separately on "How to train your program verifier" (a3-python based on the z3 theorem prover). You can use a3-python from Agentic workflows. Here is how: From your repository, install AW. Then add the a3-python workflow using: gh aw add https://github.com/ (avoid link shortening, so adding newline here) Z3Prover/z3/blob/master/a3/a3-python.md see gh.io/gh-aw for instructions on installing AW. Run the action (fx. from GitHub portal). It will scan your python files, post-process them (with your copilot tokens) and creates a GitHub issue if it finds issues with your python files.

Flight&Balance version 11 is live. Not vibecoded but with support of AI. 51 UI pages. 67 API endpoints across 41 controllers. This completely replaces a 5 year old Python Django based system. The code quality is good; fixes are easy. Docker and Playwright based end-to-end tests. Unit tests of course. 95% fully generated code. I handled the tickets (user stories), code reviews and the pull requests.

Understanding Gunicorn, Uvicorn, and Supervisor in Production Deployments. In production environments, running a Python application involves more than just writing code. It requires the right combination of servers and process management. 🔹 Gunicorn A production-grade WSGI server commonly used with Django and Flask. It handles multiple worker processes to efficiently manage concurrent requests. 🔹 Uvicorn A high-performance ASGI server designed for async frameworks like FastAPI. Built to handle high concurrency using asynchronous capabilities. 🔹 Supervisor A process control system that ensures services stay alive. It automatically restarts applications if they crash and helps maintain uptime in production environments. Understanding how application servers and process managers work together brings clarity to backend architecture and deployment workflows. Engineering is not just about building features — it’s about ensuring systems run reliably in real-world environments. #DevOps #Python #BackendDevelopment #FastAPI #Django #Production #SystemDesign #LearningJourney