Python 3.13's Optional GIL: Impact on Backend Engineers

🐍 Python Backend: 5 Practices That Scale 🐍 Python gets a bad rap for "not scaling." But after building Python backends serving millions of requests, I've learned: it's not the language, it's how you use it. Here's what makes Python backends production-ready: A real example from Chatbot Development Platform 👇 ➡️ Chatbot API handling 10M requests/day ➡️ Response times were 2-3 seconds ➡️ Memory usage growing over time (memory leaks) ➡️ Database connections exhausted On the surface, Python seemed "too slow." After implementing the right practices: 🔍 Async/await for I/O-bound operations (10x throughput improvement) 🔍 Connection pooling with SQLAlchemy (no more connection exhaustion) 🔍 Aggressive caching with Redis (40% latency reduction) 🔍 Type hints for maintainability (caught bugs before production) 🔍 Memory profiling and generators (fixed memory leaks) The fixes: ✅ FastAPI with async/await for I/O operations ✅ SQLAlchemy connection pooling (pool_size=20, max_overflow=10) ✅ Redis caching for API responses and DB queries ✅ Type hints throughout the codebase ✅ Memory profiling and generator-based data processing Result: Response time dropped from 2-3 seconds to 200ms, handling 50M requests/day. Python can absolutely scale. Instagram, Spotify, Dropbox—all Python backends. The key is using the right patterns: 1️⃣ Use async/await (when it makes sense) 2️⃣ Connection pooling is critical 3️⃣ Cache aggressively 4️⃣ Use type hints 5️⃣ Monitor memory usage See the carousel for details 👇 What Python backend patterns have worked for you? Share below 👇 #Python #BackendEngineering #SoftwareEngineering #FastAPI #Django #TechTips #Programming #TechLeadership

🌶️ Python is NOT ready for the agentic era of software engineering. And that's an existential risk for teams who ship Python in production. Why so? It's all about... 👏 FEEDBACK LOOPS 👏 FEEDBACK LOOPS 👏 FEEDBACK LOOPS 👏 The #AgenticAI workflows of today heavily rely on strong feedback loops to steer agents in the right direction. Formatters, linters, type checkers, LSP diagnostics, test runners... All of these tools play a critical role in repelling code slop. 💡 Yet, type safety in Python remains an afterthought. In practice you get `dict[str, Any]`, `Unknown` return types, or no type stubs at all even among the mainstream packages in the ecosystem. The preference for defensive duck typing over robust type safety is culturally pervasive. 💡 Many modern typing features feel bolted-on and inconsistent. A far cry from the Zen of Python: `if TYPE_CHECKING`, quoted "type expressions", and runtime typing incantations are fragile and non-cohesive. 💡 Worse, many of these type-safety features aren’t reliably in current model knowledge cut-offs. Agents burn context web-searching for the latest PEPs instead of reasoning about the problem. That is, if you're lucky that the model even decides to do that... 💡 Static analysis and control-flow narrowing are also primitive compared to their TypeScript counterparts. Tools like Pyright struggle to collapse unions without blunt tools like `isinstance` and `assert`. Agents burn precious context looping on `Unknown`, retrying type trickery, and spending tokens web-searching PEPs for edge-case features. 💡 TypeScript, by contrast, offers a far stricter and more intelligent harness for coding agents. When coupled with an ecosystem that cares about end-to-end type safety, the difference in developer (and agent!) experience is night and day! If you must use Python in production, the only defensible exception is ecosystem lock-in. But even then, we should treat that as technical debt, not a default. Moving forward, new greenfield projects should *strongly* reconsider using Python. To say the least, there are far more productive options nowadays. #Python #TypeScript #SoftwareEngineering #TypeSafety

The Era of High-Performance Python APIs is Here ⚡ For years, building APIs in Python meant accepting a trade-off: you got legendary developer ergonomics, but you sacrificed raw throughput to the Global Interpreter Lock (GIL). By 2026, that trade-off no longer exists. 🚀 We have shifted out of the monolithic, synchronous age into an era of high-performance asynchronous execution and strict type safety. The modern Python stack now rivals compiled languages in speed without losing the simplicity that makes Python great. 💨 We just published the definitive Guide to Modern Python API Development (2026 Edition). This isn't about writing a "Hello World" app in Flask; it's a technical practicum on engineering production-grade systems. 🛠️📖 Key paradigm shifts covered in this guide: 🔹 The Runtime Revolution: How Python 3.14 (free-threaded) finally kills the GIL, unlocking true multicore parallelism for CPU-bound tasks. 🧠 🔹 The Rust-Powered Toolchain: Why uv has replaced pip, poetry, and virtualenv as the single, lightning-fast lifecycle manager. 🛠️ 🔹 The New Standard: Why FastAPI and AsyncIO are now the default for handling thousands of concurrent connections. 🌐 🔹 Unified Data Modeling: Using SQLModel to stop repeating yourself between SQLAlchemy table definitions and Pydantic validation schemas. 🎯 If your backend stack is still relying on requirements.txt and synchronous routes, it's time to upgrade your workflow. 🔄 Read the full technical practicum here: 🔗 https://lnkd.in/ddxpdjiM #PythonDeveloper #APIdevelopment #FastAPI #BackendEngineering #SoftwareArchitecture #AsyncIO #DevOps #TechTrends2026 #RustLang #Voxfor

Why I’m Decommissioning Python (to Strengthen the Foundation) I spent the last few weeks back in the stack, hands on keyboard, expecting to shake off some rust. The rust wasn’t mine. What I found instead was something more interesting: the ecosystem itself is carrying more weight than it realizes. This isn’t a complaint about Python, and it’s not a teardown of modern tooling. Python has been extraordinary — it’s the glue that helped bring AI to the world. I still have Python environments running in staging, and I’ll continue to share fixes and patterns that work there. But this post isn’t about surface friction. It’s about where execution actually happens — and why that distinction starts to matter once you move from notebooks to infrastructure. Most people already know this implicitly, but don’t often say it out loud: when you write AI systems in Python, you’re not really executing in Python. You’re dispatching into compiled machinery underneath. The real work — memory layout, parallelism, hardware scheduling — lives lower in the stack. That’s the layer I’m spending more time in now. Not because Python is “bad,” but because abstraction has a cost, and at scale those costs show up as compute bills, latency gaps, and systems that feel harder to reason about than they should. When that happens repeatedly, the fix isn’t another workaround — it’s foundation. So I’m shifting more of my core architecture work into C++. Not to be exclusive. Not to be contrarian. But to work directly in the layer where execution is explicit, deterministic, and accountable. To newer builders: keep experimenting and shipping in Python. The ecosystem is unmatched for learning and iteration. But if you ever find yourself wondering why deployments feel heavier than benchmarks, or why production doesn’t behave like your experiments, pay attention to where Python ends and the machinery underneath begins. That boundary is where a lot of the signal lives. That’s where I’ll be working for a while — reinforcing the steel so the rest of the structure can keep growing.

Python 3.14 is already here—and it’s finally tackling the "Performance Tax." 🐍 After 6 years in the Python ecosystem, I’ve seen my fair share of ‘slow code’ debates. But the 3.14 release (and the 2026 roadmap) is a game-changer for those of us building high-scale backends. Three features I’m currently digging into: 1. Zero-Overhead Debugging (PEP 768): We can finally attach profilers to production processes without the usual "performance hit." This is huge for diagnosing those "it only happens in prod" spikes. 2. Deferred Annotation Evaluation: Faster startup times and cleaner type-hinting without the string-quote hacks. 3. The "No-GIL" Era: We’re moving closer to true multi-core Python. If you’re still writing synchronous, single-threaded code for heavy tasks, it’s time to rethink your architecture. The takeaway? Python isn't just the "easy" language anymore; it’s becoming a performance powerhouse.

𝘼𝙨𝙮𝙣𝙘 𝙞𝙣 𝙋𝙮𝙩𝙝𝙤𝙣 𝙖𝙣𝙙 𝙍𝙪𝙨𝙩: 𝙎𝙖𝙢𝙚 𝙆𝙚𝙮𝙬𝙤𝙧𝙙, 𝘿𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩 𝙒𝙤𝙧𝙡𝙙𝙨 I just published a new article exploring why async/await looks identical in Python and Rust but hides radically different execution models. The piece covers: → How asyncio's event loop differs from Tokio's work-stealing scheduler → The hidden costs of Python's "generous runtime" vs Rust's compile-time state machines → When to choose one over the other based on your actual problem This, as usual, wasn't just theoretical research. While working on this article, I contributed to both ecosystems: documentation improvements for Tokio's time module, and a fix for a common async anti-pattern in a Python GenAI framework, where async def without any await was silently blocking the entire event loop. Neither approach is universally "better". Python excels when domain complexity matters more than system complexity. Rust shines when execution predictability is your product. Full article in both EN/ IT below: https://lnkd.in/dqpaKCBe #AsyncProgramming #Rust #Python #SoftwareEngineering

How to Build an MCP Server in Python — Step by Step Everyone’s talking about Agentic AI. Very few explain how the plumbing actually works. So I wrote a practical, end-to-end guide on building an MCP (Model Context Protocol) server in Python — no hand-waving, no vendor fluff. In this post, I walk through: - What an MCP server really is (beyond the buzzwords) - How tools, resources, and prompts actually fit together - A minimal but production-ready Python MCP server - The mental model you need to extend it for real systems (Redmine, legacy APIs, internal platforms) If you’re serious about moving from RAG → agentic workflows, this is the missing piece. #AgenticAI #MCP #LLM #Python #AIEngineering #DeveloperTools

Built Zetten - a Rust-powered task runner for Python backends. I was tired of glue code (venvs, env vars, duplicated CI scripts), so I built a tool to make running and orchestrating tasks boring again. One cool surprise: standardized task definitions make AI coding much more reliable. No guessing - it just reads the config and runs correctly. I wrote more about the journey, tradeoffs, and lessons on Substack: https://lnkd.in/gitWHmfJ If you’re into Rust, Python, or developer tooling, I’d love your feedback. Repo: https://lnkd.in/gTrzzkn4 #Rust #Python #OpenSource #BuildInPublic #DevTools

“Why are you looking at Rust 🦀 ?” 🤔 I get that question a lot. Python 🐍 is still the dominant language in network automation — and for good reason. But when you start building long-running, scalable automation systems, the runtime matters just as much as the logic. That’s why I’ve been looking at how Rust and Python work together, not in competition, but as complementary strengths. I wrote an article on how Rust can act as a safe, high-performance automation runtime, while Python remains the language for intent, logic, and extensibility — connected cleanly using PyO3. 👉 Python as a plugin, not a process 👉 Rust for safety, performance, and control If you’re curious why more Python tooling is powered by Rust — and what that means for automation platforms — have a read 👇 🔗 https://lnkd.in/eh5herY4

🔥 15 Days Python Series – Day 1 🎯 From Today: Focus on Consistency. Build Strong Python Foundation. 🚀 Why Python? Why Now? Tech world is not just “digital” anymore — it’s becoming AI-driven. Today, everything runs on Python: 🤖 AI 📊 Data Science 📈 Data Analytics 🧠 Machine Learning 🌐 Web Development ⚙ Automation The reason? ✅ Simple & Readable ✅ Beginner Friendly ✅ Powerful Libraries ✅ Huge Community ✅ Used by companies like Google, Netflix, Instagram Python is like English of programming – easy to read, easy to write, easy to scale. 📅 Day 1 – How Python Works? Most people use Python. But do you know what happens internally? 🔁 Python Execution Flow: Source Code → Compiler → PVM → Machine Code 🧩 Step-by-Step Explanation: 1️⃣ Source Code The code you write in .py file. 2️⃣ Compiler Time Python converts source code into Bytecode (.pyc file). This process happens before execution. 👉 Source Code + Compiler = Compile Time 3️⃣ PVM (Python Virtual Machine) PVM converts bytecode into machine code and executes it. 👉 PVM + Machine Code = Run Time ❌ What is Compile Time Error? A compile time error happens before execution, when Python checks your code structure. 💻 Example: if 5 > 2 print("Hello") ❌ Missing colon : 👉 Python will stop immediately and show SyntaxError 🧠 Real-Life Example: Imagine you are filling a job application form. If you forget to fill a mandatory field, the system won’t let you submit. That is Compile Time Error – mistake before processing. ⚠ What is Runtime Error? A runtime error happens after program starts executing. The code structure is correct, but problem occurs during execution. 💻 Example: a = 10 b = 0 print(a / b) ❌ ZeroDivisionError Program starts, but crashes while running. 🧠 Real-Life Example: You start driving a bike 🏍️ Everything is correct initially. But suddenly fuel becomes empty in the middle of the road. That is Runtime Error – issue during execution. more information Prem chandar #Python #PythonDeveloper #30DaysOfPython #AI #MachineLearning #DataScience #CodingJourney #TechCareer #LearnToCode #SoftwareDeveloper #LinkedInLearning