Mastering Async Programming in Python with Nicolas Bohorquez

🌶️ 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

Top Python Libraries in 2025: General‑Use Tools That Raise the Bar Python’s general‑purpose tooling in 2025 shows a clear push toward speed, clarity, and production safety. A new wave of Rust‑powered tools like ty and complexipy focuses on making everyday development feedback fast enough to feel invisible, while grounding quality metrics in how humans actually read and understand code. The result is tooling that helps teams move faster without sacrificing maintainability. Developer productivity and correctness are a strong theme. ty rethinks Python type checking with fine‑grained incremental analysis and a “gradual guarantee” that makes typing easier to adopt at scale. Complexipy complements this by measuring cognitive complexity instead of abstract execution paths, helping teams identify code that’s genuinely hard to understand rather than just mathematically complex. Several tools address long‑standing infrastructure pain points. Throttled‑py modernizes rate limiting with multiple algorithms, async support, and strong performance characteristics, while Httptap makes HTTP performance debugging concrete with waterfall views that reveal where latency actually comes from. These libraries focus on observability and control where production systems usually hurt the most. Security, code health, and extensibility also get serious attention. FastAPI Guard consolidates common API security concerns into a single middleware, while Skylos tackles dead code and potential vulnerabilities with confidence scoring that respects Python’s dynamic nature. Modshim offers a powerful alternative to monkey‑patching, allowing teams to extend third‑party libraries cleanly without forking or global side effects. Finally, there’s a clear move toward better interfaces and specifications. Spec Kit reframes AI‑assisted coding around executable specs instead of vague prompts, while FastOpenAPI brings FastAPI‑style documentation and validation to multiple frameworks without forcing a rewrite. Together, these libraries show a Python ecosystem that’s maturing—not by adding more abstractions, but by making the fundamentals faster, safer, and easier to reason about. Read https://lnkd.in/dwUShkiZ #python #softwareengineering #developertools #productivity #opensource

Have you ever been frustrated at the Pyright’s performance? I’ve been working on a long-term systems project: building a Python language server from scratch in Go. Over the past few weeks, I implemented the full frontend pipeline required for a real LSP, without relying on existing compiler frameworks. This includes a hand-written lexer with Python-style INDENT/DEDENT handling, a recursive-descent parser that produces a structured AST, and a complete semantic analysis layer. The semantic phase constructs lexical scopes from builtin → global → function, maintains explicit symbol tables with scope ownership, and resolves names using Python’s LEGB rules. It correctly handles parameters, defaults, shadowing, loops, and control flow, while modeling built-in functions like print and range as first-class symbols. Every identifier in a file can now be deterministically resolved, with accurate source spans suitable for editor tooling. The goal is to understand how language servers work under the hood and to build the core infrastructure needed for features like go-to-definition, hover, and diagnostics. This has been a deep dive into parsing theory, static analysis, and language tooling architecture, which has significantly sharpened my understanding of compilers, IDEs, and large-scale developer systems. If you’re interested in language tooling, compilers, or LSP internals, I’m happy to discuss! Feel free to check out the project at: https://lnkd.in/epB9CaVm

🚀 Your Complete Python Programming Roadmap – From Beginner to Pro in 2025/2026 🐍 Python continues to dominate in 2026 — whether you're aiming for Data Science, Machine Learning, Web Development, Automation, or just building powerful scripts. I created/curated this detailed mind map to give you a clear, structured path: Start with the Basics → Installation, Syntax, Variables, Data Types, Control Structures (If-Else, Loops), Functions Master Data Structures → Lists, Tuples, Dictionaries, Sets, Strings + comprehensions Dive into File Handling, Exception Handling, and OOP (Classes, Inheritance, Polymorphism, Encapsulation) Explore Advanced Topics → Decorators, Generators, Context Managers, Regular Expressions, Multithreading/Multiprocessing Get hands-on with essential Libraries → NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow/PyTorch Choose your path: Data Science & ML → Data cleaning, Supervised/Unsupervised Learning, Model Deployment Web Development → Flask, Django, REST APIs Automation & Scripting → Web scraping (BeautifulSoup + Selenium), OS module, Task schedulers Don't forget Testing, Version Control (Git), CI/CD, and Deployment (Docker) This roadmap covers everything you need to go from zero to building real-world projects and landing opportunities in high-demand fields. Which branch excites you the most right now — Data Science/ML, Web Dev, Automation, or something else? Save this post + the image for your learning journey, and drop a 🐍 or "PYTHON" in the comments if you're committing to leveling up this year! #Python #Programming #DataScience #MachineLearning #WebDevelopment #CodingRoadmap #TechCareer #LearnToCode #PythonDeveloper

Python: The Second-Best Language for Everything? In the world of software engineering, we often say that Python is the "second-best" language for almost every specific task. • Need raw speed? You’d choose C++. • Need low-level system control? Rust is your friend. • Need interactive front-ends? JavaScript rules the web. But here is the secret: Python is the "Glue" that holds all those things together. Why Python Wins (Even When It's "Slower") Python’s real superpower isn't execution speed—it’s "speed of thought." It allows you to move from a complex business idea to a working prototype faster than almost any other language. 1. The Ultimate Integrator: Most high-performance libraries (like NumPy or TensorFlow) are actually written in C or C++. Python just provides a beautiful, readable wrapper that allows us to tap into that power without the headache of manual memory management. 2. The Ecosystem Advantage: Whether you are doing Data Science (Pandas), AI (PyTorch), Web Dev (Django), or DevOps (Ansible), the community has already built the "heavy lifting" for you. 3. The "Readability" ROI: Code is read far more often than it is written. Python’s clean syntax reduces the cognitive load on teams, making onboarding and maintenance significantly cheaper. The Shift: From Programmer to Orchestrator Today, being a Python developer is less about writing every single line of logic and more about orchestration. We are building bridges between massive data stores, AI models, and cloud infrastructure. In a world where AI is generating code, Python’s readability makes it the perfect "specification language" to guide and verify what the machines are building. What’s your take? Do you prefer Python for its ease of use, or do you find yourself reaching for lower-level languages when performance is on the line? #Python #SoftwareEngineering #DataScience #Coding #TechTrends #AI

🚀 OOPS Concepts in Python – Explained Simply! Object-Oriented Programming (OOPS) helps us design programs using real-world concepts, making code modular, reusable, and easy to maintain by using classes and objects. 🔑 Core OOPS Concepts in Python: 1️⃣ Class A blueprint for creating objects. 👉 Defines attributes and methods. 2️⃣ Object An instance of a class that represents a real-world entity. 👉 Example: student = Student(). 3️⃣ Attributes Variables that store object data. 👉 Example: name, age, salary ✔ Describe the state of an object. 4️⃣ Constructor (__init__) A special method that runs automatically when an object is created. 👉 Used to initialize attributes. ✔ Ensures objects start with valid data. 5️⃣ Encapsulation Wrapping data (attributes) and methods into a single unit (class). ✔ Improves security and control. 6️⃣ Inheritance Allows one class to inherit properties and methods from another class. ✔ Promotes code reusability. 7️⃣ Polymorphism Same method name, different behavior. ✔ Increases flexibility in programs. 8️⃣ Abstraction Hides implementation details and shows only essential features. ✔ Focus on what the object does, not how. 💡 Why OOPS in Python? ✔ Cleaner code ✔ Easy maintenance ✔ Scalable applications ✔ Real-world problem solving 📌 tomorrow post about inheritance and its types with solved examples. #Python #PythonBasics #LearnPython #CodingJourney #ProgrammingForBeginners #LinkedInLearning #10000coders #ManivardhanJakka

Python's GIL is Finally Optional: What This Means for Backend Engineers Python 3.13 brought something the community has debated for decades: optional GIL. While most teams are still on 3.10 or 3.11, now is the time to understand what's coming. For years, Python's GIL has been the invisible ceiling on CPU-bound parallelism. We've worked around it with multiprocessing, async I/O, and clever architecture. But true multi-threaded performance? That required stepping outside Python entirely. And that is changing. What becomes possible: • CPU-intensive tasks (data processing, encoding, complex calculations) can finally use threading effectively • Simpler code patterns - no more multiprocessing complexity for parallel workloads • Better resource utilization in containerized environments where spawning processes is expensive What stays the same: • I/O-bound workloads (most web services) already perform well with async I/O • The GIL can still be enabled for compatibility • Existing codebases won't break What I'm Watching The interesting question isn't "will this make Python faster?" - it's "how will this reshape our architectural decisions?" Consider: today, we often reach for Go or Rust when we need true parallel processing. We architect around Python's limitations. When those limitations disappear, how do our tradeoffs change? A few predictions: More Python in data pipelines that currently use JVM languages Simpler deployment models (fewer workers, more threads) New categories of Python-native tools that were previously impractical This isn't a silver bullet. Free-threaded Python has overhead - initial benchmarks show 5-10% slowdown for single-threaded code. Library compatibility will take time. Production adoption? Hopefully 2026-2027 for innovative startups. But the trajectory is clear: Python is evolving from a "fast enough" language into one that can compete on raw performance. Your thoughts? If you're building backend systems today: what would you architect differently if Python offered true parallelism? What problems are you currently solving with other languages purely because of the GIL? The transition to optional GIL is one of the most significant changes in Python's 30+ year history. Whether you adopt it in 2025 or 2028, understanding the implications now helps you make better architectural decisions tomorrow. #Python #BackendEngineering #DistributedSystems #SoftwareArchitecture

I thought I wrapped up my Python Object-Oriented Programming (OOP) exercise... until the terminal told me otherwise. 😅 🐍 I’ve been building a Company class structure to manage employees and departments (see the screenshot below). The logic seemed straightforward, but I stumbled into a few classic "gotchas" that turned into great learning moments. Here is what went wrong: ❌ The Recursion Trap (Line 12): I wrote [emp.get_details() for emp in self.list_of_employees]. I was trying to iterate over the method itself instead of the actual list (self.employees). This resulted in an immediate infinite recursion error. ❌ Naming Confusion (Line 14): I defined a method called list_of_departments, but if you look closely, its actual job was to create and append a new department. In software engineering, clarity is king—functions should do exactly what their name suggests. ❌ Over-Nesting Structure: I nested the Employees and Departments classes directly inside the main Company class. While Python allows this, it made the structure unnecessarily complex and harder to test later on. The biggest takeaways from this debugging session: ✅ Watch your iterables: Always double-check if you are looping through a list or calling a function. ✅ Keep it Flat: Flat class structures are usually easier to maintain and scale than deeply nested ones. ✅ Bugs mean growth: Every error message is just a lesson in disguise. Progress over perfection. Time to refactor and fix that recursion! 🚀 What’s a "simple" bug that took you way too long to spot recently? Let me know in the comments. 👇 #PythonProgramming #OOP #CodeNewbie #SoftwareEngineering #LearningInPublic

💡 Stop Overthinking Function Arguments — Bottom-Up Design Works! A lot of new programmers worry: “Do I need to know all the arguments before I write a function?” Here’s the truth: you don’t. Many great devs actually write logic first, then decide what inputs the function needs — it’s called bottom-up design, and it’s totally valid. Here’s how it works in Python: 1️⃣ Write the logic first Copy code Python if calc_op == "+": return operators["+"](num, num2) elif calc_op == "-": return operators["-"](num, num2) You notice: num, num2, calc_op — those are your inputs. 2️⃣ Wrap it in a function Copy code Python def calc(num, num2, calc_op): if calc_op == "+": return operators["+"](num, num2) elif calc_op == "-": return operators["-"](num, num2) The logic hasn’t changed. You just formalized the inputs. ✅ 🔑 Golden Rule: If a variable is used inside a function but not created inside it → it must be an argument. ⚠️ What NOT to do: Copy code Python def calc(): return operators["+"](num, num2) # ❌ num and num2 are undefined Python doesn’t know where these values come from. Your bottom-up approach naturally avoids this. 💡 Why this matters: It aligns with how engineers actually think Scales to bigger programs Makes spotting dependencies intuitive TL;DR: Write logic → identify inputs → promote to arguments → call the function. That’s real-world coding. #Python #Coding #Programming #SoftwareDevelopment #DeveloperLife #ProblemSolving #LearnToCode #CodingMindset #EngineerMindset #TechTips #BottomUpDesign #CareerInTech #ProfessionalGrowth #CodingJourney #LinkedInLearning

"Python is too slow for production." We hear this myth all the time, especially at the start of new projects. The concern is usually associated with performance challenges arising under increased load, scaling efforts becoming complex or a substantial rewrite that may be necessary in the future. Well, these worries are well justified as Python can indeed become a limiting factor in certain cases: 1. Highly CPU-bound workloads where every millisecond matters. 2. Strict real-time systems. 3. Low-level programming and direct hardware control. In these situations, Python isn't the best tool, and knowing that early on is an important part of good engineering. But this is where people start to ignore the full picture. At Pynest, we utilize Python in our production systems. In practice, performance issues are rarely caused by Python's speed limitations. Instead, Python tends to perform effectively because: 1. Most backend systems are primarily I/O-bound rather than CPU-bound. 2. Performance bottlenecks typically occur in databases, network interactions or external services. 3. Python facilitates rapid iteration on architecture and enables early identification and resolution of potential bottlenecks. 4. When necessary, performance-critical components can be optimized or integrated with other technologies outside of Python to enhance efficiency. At Pynest, we focus on identifying real bottlenecks instead of assuming that the language choice is the problem. This combination exemplifies why Python remains a reliable choice for production environments. That’s why thousands of companies continue to use it successfully, including Instagram, Netflix, Spotify and Dropbox. The takeaway is simple: while Python may present limitations for certain CPU-intensive tasks, these challenges are generally well-understood and manageable for most backend systems. What’s your experience? Have you seen Python cause real performance issues in production or is it just a myth? #Python #Pynest #SoftwareEngineering #BackendDevelopment #TechMyths