Async in Python: When to Use It for Efficient Concurrency

I’ve been diving into Go lately, and it’s basically ruined other languages for me when it comes to API work. For a long time, my go-to for automation has been Python. It’s great, it’s readable, and it has a million libraries. But as I’ve started moving some of those workflows into Go, the difference in efficiency, especially around concurrency, is night and day. In library systems, we’re often orchestrating a lot of moving parts. If I’m hitting the Alma API to update 500 records or pulling data from Primo to sync with another system, doing that sequentially is a bottleneck, especially doing so safely with rate limiting, etc. Go’s goroutines make handling those concurrent requests feel almost free. There's no Global Interpreter Lock, you’re just writing fast, type-specific code that compiles to a single binary(!).  The big moment for me was seeing how much cleaner the error handling and deployment feel. Last time I wrote a Python script to automate widescale changes to a user database, I feel like I spent as much time troubleshooting the virtual environment and dealing with tmux challenges as I did actually developing the Python program. With Go, there are no virtual environments to manage, no need to containerize anything, no dependency hell, and an extremely fast, predictable tool for the specific kind of systems architecture I’m building. I’m still very much in the learning phase, but the more I use it, the more it feels like the right tool for a lot of things I'm doing and plan to do. Curious to know if anybody else in higher ed/libraries is using Go and, if so, what you're working on.  #Go  #Golang  #Alma  #libraries  #highered

How We Designed a Python Backend That Handles Millions of Requests A lot of developers believe scaling starts when traffic grows. That's wrong. Scalability starts the moment you design your architecture. In one system I worked on, we had to design a backend expected to handle millions of requests daily. The approach wasn't complicated — but the discipline was. Key decisions: • FastAPI for async I/O workloads • Stateless API services • Horizontal scaling behind a load balancer • Redis for caching hot queries • Background processing with workers The biggest mistake teams make is building synchronous systems for asynchronous problems. Scaling Python isn't about rewriting everything in Go. It's about understanding: • I/O bottlenecks • concurrency • architecture boundaries Most performance problems in backend systems are not language problems. They are architecture problems.

Python Development in 2026: It’s no longer just about the code. If you still see Python as just a scripting language, you’re looking at it through a 2015 lens. Backend work now feels closer to system design than feature coding. Writing logic is only part of the job. The real value is in how systems talk to each other, scale, and stay secure. Here’s what I’m seeing shift: 1. APIs → Orchestrated Systems: -It’s not just about wiring a REST endpoint. We’re defining workflows using OpenAPI 3.1 and machine-readable specs so services and AI agents can move through systems without human hand-holding. 2. Async by default: -If you’re not comfortable with asyncio or FastAPI, you’re behind. Concurrency isn’t an optimization anymore. It’s the baseline. Thousands of parallel requests should feel normal. 3. Infrastructure is part of the role: -Code is half the work. The other half is Docker, CI/CD, Kubernetes YAML, and making sure your data layer scales when traffic doubles overnight. If you can’t read deployment configs, you’re limiting yourself. What actually matters right now: Strong Python 3.x with real type usage, not decorative hints Security beyond basic JWTs. OAuth 2.1 and PKCE should not sound exotic Integration tests that hit real external APIs, not just mocks The industry doesn’t need more people who can write functions. It needs engineers who understand flow, failure modes, performance, and trust boundaries. Focus less on syntax. Focus more on system integrity. #Python #BackendEngineering #FastAPI #CloudNative #SoftwareArchitecture #APIDesign

OpenAI acquiring Astral to pull Python’s most popular dev tools into Codex is a classic platform move: stop being “a model that writes code” and start owning the toolchain developers live in. When you control formatting, dependencies, builds, and the agent that edits code, you control the workflow - and workflows are where switching costs are born. This matters because the coding layer is the gateway to enterprise agents. Today it’s Python tooling. Tomorrow it’s CI/CD, ticketing, infrastructure-as-code, and deployment permissions. That’s enormous leverage - and enormous risk if governance doesn’t keep up: supply chain attacks, license contamination, insecure defaults, and “agent drift” that quietly changes production behavior. My controversial view: this acquisition isn’t about developer happiness - it’s about lock-in. OpenAI wants to be the IDE + build system + agent brain, so leaving becomes as painful as migrating clouds. Best practices for teams adopting Codex-style platforms: require signed outputs, enforce human review on critical paths, run dependency scanning, keep reproducible builds, and maintain an escape hatch (toolchain portability, data export, model routing). If one vendor owns your coding agent and your Python toolchain, who really owns your software supply chain - and what happens when that vendor’s incentives shift? #DevTools #SoftwareSupplyChain #AIGovernance https://lnkd.in/gq7GHJg5

Most Python codebases rely on dynamic typing — until they scale. At scale, silent bugs, fragile refactors, and unclear contracts become real productivity killers. One of the most powerful (and underused) tools in modern Python for building robust, production-grade systems is: Protocols + Generics These features bring interface-driven design and compile-time safety to Python — without sacrificing flexibility. 🔹 Protocols enable structural typing (“if it behaves like X, it is X”) 🔹 Generics allow reusable, type-safe abstractions 🔹 No inheritance required — just the correct shape 🔹 Perfect for Clean Architecture, DI, and testable systems Example use cases: ✅ Repository patterns (DB / API / Cache interchangeable) ✅ Plugin systems ✅ SDK & library design ✅ Service layer decoupling ✅ Mocking without brittle test doubles ✅ Large-scale refactoring with confidence By depending on capabilities instead of concrete classes, your business logic becomes storage-agnostic, test-friendly, and future-proof. In modern Python (3.11+), combining strong typing + static analysis (Pyright/mypy) delivers many benefits traditionally associated with statically typed languages — while retaining Python’s developer velocity. If you’re building serious backend systems, this is no longer optional knowledge — it’s a force multiplier. Dynamic language. Static guarantees. Clean architecture. Read More: https://lnkd.in/gRtdPtP2 #Python #SoftwareEngineering #BackendDevelopment #CleanArchitecture #TypeSafety #StaticTyping #Programming #Developers #TechLeadership #SystemDesign #APIDevelopment #CodeQuality #ScalableSystems #DesignPatterns #ProgrammingLanguages #PythonDeveloper #SoftwareDevelopment #TechInnovation #EngineeringExcellence #CodingBestPractices

🚀 Microsoft Agent Framework is now Release Candidate 🤖 If you’re building agents with Semantic Kernel or AutoGen, now’s the time to migrate. Agent Framework unifies both into a single, stable framework for .NET and Python, with a consistent model for building and orchestrating AI agents 🔧🧠 ✅ Stable APIs ✅ Unified agent model ✅ Built for production 🔗 Read more: https://lnkd.in/eBQuxxKv #AI #Agents #SemanticKernel #AutoGen #Microsoft #GenAI

The gap between throwing prompts at Claude Code and actually getting reliable results is wide, and basic programming concepts end up mattering a lot. You need a grounding in systems thinking, because most failures come from brittle systems that aren’t designed to produce consistently accurate outputs. It helps to understand APIs so you can influence the tools models use and integrate differentiated data to generate better results. And familiarity with the ecosystem around GitHub, AWS, Python, etc. lets you extend what tools like Claude Code can do far beyond just sitting there and prompting. These skills compound. A lot of people sign up for AI tools, generate a website landing page, and don’t make it much further unless they’re genuinely curious and willing to think like an engineer. Claude Code will nail the hardest technical problem and completely miss the most obvious one. You have to assume nothing and design around that, and that takes a certain kind of mind.

Most Python developers don’t actually spend their day building new ideas. They spend it: • fixing small bugs • writing boilerplate tests • updating documentation • refactoring code • answering questions about the repository Important work — but not the work that moves systems forward. A shift is happening. With OpenAI agent skills, developers can move from writing code to delegating engineering tasks. Instead of doing everything manually, you can give the agent a task: “Add caching to the API layer and write unit tests.” The agent can then: • read the repository • plan the changes • modify the relevant files • run tests • prepare a pull request for review Your role changes. You are no longer just producing code. You are orchestrating work. The most valuable developer skill is no longer typing faster. It is defining clear tasks, reviewing outputs, and guiding system architecture. We are entering the era where developers don’t just build software. They lead AI engineering agents that build it with them.

It is 2026 and one of the most widely used SDKs in the world, AWS Python SDK, still does not natively support async and await. This issue was opened 11 years ago: https://lnkd.in/ddNdJxXm Let that sink in. During this time, Python evolved. Async programming became standard. Modern applications increasingly depend on non blocking IO. Yet developers still rely on wrappers, thread pools, or unofficial solutions to fill this gap. And this is not an isolated case. Celery is another example. It is one of the most important task queue frameworks in Python. It is robust and widely adopted, but native async support is still tied to the version 6 milestone: https://lnkd.in/dJDqQ7FU Looking at its progress, it is hard not to feel like this milestone may never actually be completed. Many developers still rely on older approaches like gevent or move to alternative tools. This is not criticism. These projects are fundamental to the software industry and represent years of incredible work. But they reveal something important. Open source does not evolve by itself. We are now in a moment where building software has never been more accessible. AI has dramatically increased developer productivity. Tasks that once took days can now take hours. The barrier to contributing has never been lower. And yet, critical parts of our ecosystem are still waiting for contributors. Maybe the problem is no longer complexity. Maybe it is initiative. We all depend on open source every day. That should come with a sense of responsibility for its future. I have been reflecting on this myself. It is easy to point out what is missing. It is harder, and more meaningful, to step in and help build it. Open source is the foundation of modern software. If we want a better future, we need to start building it ourselves.