Python API Design: Embracing the Language's Dynamic Nature

Understanding Python: Interpreted vs. Compiled Languages I've been working with Python lately, and it's made me think about the fundamental differences between interpreted and compiled languages. Compiled Languages (C++, Java, Go): - Code gets translated into machine code before you run it - Faster execution - Catches errors during compilation - Creates platform-specific executable files Interpreted Languages (Python, JavaScript, Ruby): - Code runs line-by-line in real-time - More flexible for development - Works across different platforms without changes - Easier to test and debug quickly What I've learned using Python: The interpreted approach means slightly slower execution, but you gain a lot in return. Development is faster, debugging is straightforward, and you can run the same code on different systems without modification. For most projects, the development time you save far outweighs any performance concerns. And when you do need speed, there are tools like Cython or you can integrate C libraries. It's all about choosing the right tool for the job. Python's flexibility has made it my go-to for most tasks.

Python vs Java — It’s not just syntax, it’s how they THINK Most beginners compare these two based on ease or popularity… But the real difference lies in how your code actually runs behind the scenes. 🔹 Python → Interpreted, flexible, fast to build 🔹 Java → Compiled + JVM, structured, performance-focused 👉 Python converts code to bytecode and executes it via an interpreter 👉 Java compiles first, then runs on JVM with JIT optimization Same goal. Different journey. 💡 So the real question isn’t “Which is better?” It’s “Which one fits your use case?” – Want quick development & AI/ML? → Python – Building scalable systems & apps? → Java 🎯 Smart developers don’t pick sides. They pick the right tool. 🚀 Follow Skillected for more real-world tech breakdowns 💬 Comment below: Python or Java — what’s your pick and why?

hey, big release today. Release v0.2: - Migrated backend from Java Spring Boot to Python FastAPI - did this to prepare the backend for AI integrations since Python has a strong ecosystem for AI/ML libraries - Replaced Session based authentication with JWT based authentication - Authentication is still handled through Auth0 as an identity broker - Backend APIs now validate JWT access tokens instead of server side sessions - Also updated the diary front end to support JWT auth instead of session based auth - Created a new pipeline on AWS for the Python FastAPI backend This was the post I had made 2 years ago on the implementation of session based authentication https://lnkd.in/ghyScXbQ , but why the change to JWT based authentication now? Previously, the backend used session based authentication, where the server created a session and stored it in the database, and the client (diary front end) sent a session cookie with every request. Now the backend uses JWT tokens, which means authentication is stateless. The benefits are: - No session storage required, no database lookup needed to validate authentication on each request. This is the main reason for the switch, fewer calls to the database to validate API requests for authenticated endpoints. - Faster API authentication flow since token validation happens locally using the token's signature, without hitting the database. Auth flow remains the same conceptually: 1. User authenticates via Auth0 2. Auth0 issues a JWT access token 3. Client sends the JWT with API requests 4. FastAPI backend validates the token before allowing access to protected routes FastAPI also makes it easier to integrate AI features in the future. Most modern AI/ML libraries are Python first (PyTorch, TensorFlow, HuggingFace, OpenAI, etc), so moving the backend to Python makes it much easier to experiment with AI features around the diary. FastAPI is also really nice to work with: - High performance - Native async support (what i saw whilst writing the backend is that, when you write functions, they are natively working as async) - Automatic API documentation, this is also really cool… OpenAPI/Swagger are automatically implemented for all the APIs we are writing. This was really useful when i was doing the development. Because of this, Python + FastAPI feels like the right stack for where the diary is going and where AI is heading. [7:47pm Sunday, March 15th, 2026]

Converting Python Dictionaries to JSON: A Simple Guide Working with JSON in Python is crucial, especially when integrating with web APIs or handling configuration files. JSON (JavaScript Object Notation) is a lightweight data interchange format that is both human-readable and machine-readable. In the example, we start by importing the JSON module, which is part of Python’s standard library. Next, we define a simple Python dictionary. This data structure is versatile and easy to manipulate, making it a perfect candidate for JSON conversion. The function `json.dumps()` is then used to convert the dictionary into a JSON string. This serialization process transforms the dictionary into a format that can be easily transmitted or stored. When printed, the JSON string appears as expected, structured to facilitate data exchange. To convert the JSON string back into a Python dictionary, we use `json.loads()`, which deserializes the JSON, allowing us to manipulate the data in its original form. JSON's simplicity and flexibility make it an essential tool for data exchange in web development, where compatibility and efficiency are critical. Understanding its serialization and deserialization processes opens doors to various applications, from handling API responses to saving configurations. Quick challenge: How would you modify the code to handle a nested dictionary for JSON conversion? #WhatImReadingToday #Python #PythonProgramming #JSON #WebDevelopment #Programming

GitVoyant v0.3.0. Temporal code intelligence now runs across Python, JavaScript, Java, and Go. Four language-specific analyzers behind a single protocol. Tree-sitter for unified AST parsing. Automatic language detection. Static analysis tells you a file is complex. GitVoyant tells you whether that complexity is growing, shrinking, or stable, and at what rate. https://lnkd.in/g4fNbdRg

Stop writing Python like Java/C++. Most backend developers, coming from languages like Java or C++, tend to think about performance in terms of threading and blocking I/O. While that's a valid concern, Python offers a more idiomatic and often simpler approach to managing background tasks that can significantly boost backend performance. The 'Pythonic' way to think about background workers isn't about squeezing more out of a single process with complex threading. Instead, it's about offloading work that doesn't need to happen right now from the main request-response cycle. This allows your web server to respond to users faster, while the heavy lifting happens asynchronously in the background. Think of it like a restaurant: the waiter (your web server) takes your order quickly, and the chefs (background workers) prepare your meal without you waiting at the counter. Here's a quick example of the core idea: Okay (Blocking): from flask import Flask import time app = Flask(name) def longrunningtask(): time.sleep(5) # Simulates a slow operation print("Task finished!") @app.route('/') def index(): longrunningtask() # This blocks the entire request return "Request processed!" Best (Async with a Worker Queue - conceptual): from flask import Flask import time from yourworkerlibrary import enqueue_task # e.g., Celery, RQ app = Flask(name) def longrunningtask(): time.sleep(5) print("Task finished!") @app.route('/') def index(): enqueuetask(longrunning_task) # This returns immediately return "Request received! Task is processing in the background." The key insight here is that your main application should focus on quickly serving user requests. Anything that takes time and doesn't need to be in the immediate response – like sending emails, processing images, or generating reports – should be handled by a separate background worker process. This decoupling keeps your web server responsive and your users happy. Background workers improve backend performance by removing slow, non-essential tasks from the critical request-response path, allowing the main application to serve users much faster. #Python #CodingTips

DotNetPy is the only .NET–Python interop library with Native AOT support — call Python from C# in 4 lines, manage Python versions and dependencies directly from C# via uv, and get compile-time injection warnings from a built-in Roslyn analyzer. { author: 남정현 } https://lnkd.in/eP3y8R5k

Python has at least eight(?!) ways to execute system commands, and most of them are injectable if you pass user input. We maintain a command injection cheat sheet that covers the common patterns and their fixes. A few highlights: Vulnerable: subprocess.call("grep -R {} .".format(user_input), shell=True) os.system("grep -R {} .".format(user_input)) Safe: subprocess.run(["grep", "-R", user_input, "."]) The fix is almost always the same: use array-based arguments instead of string formatting, and keep `shell=False` (which is the default). When you pass an array, each element becomes a separate argument. The shell never interprets the input, so injection is structurally impossible. Semgrep has pre-built rules for all of these: - python.lang.security.audit.dangerous-subprocess-use - python.lang.security.audit.subprocess-shell-true - python.lang.security.audit.dangerous-system-call Run semgrep --config "p/python" on your codebase and these rules are included. Or browse the full cheat sheet at https://lnkd.in/gYRS2SyS to see all the patterns covered for Python, Java, JavaScript, Go, and Ruby. #SAST #AppSec

🔥 Mastering JSON Parsing in Python! 🐍 Ever wondered how to work with JSON in Python? JSON (JavaScript Object Notation) is a popular format for data exchange due to its simplicity and readability. For developers, understanding JSON parsing is essential as it allows you to interact with APIs, handle configuration files, and exchange data between different systems effortlessly. Here are the steps to parse JSON in Python: 1️⃣ Load the JSON data using the `json.loads()` function. 2️⃣ Access the values using keys just like a Python dictionary. 3️⃣ Iterate through JSON arrays to extract multiple values. 👉 Pro Tip: Use `json.dumps()` to convert Python objects back to JSON. ❌ Common Mistake: Forgetting to handle exceptions when parsing JSON can lead to runtime errors. 🚀 Ready to level up your Python skills? Try parsing JSON with this code snippet: ``` import json # Sample JSON data json_data = '{"name": "John", "age": 30, "city": "New York"}' # Parse JSON data = json.loads(json_data) # Access values name = data['name'] age = data['age'] city = data['city'] print(name, age, city) ``` 🤔 What's your favorite way to work with JSON data in Python? 🌐 View my full portfolio and more dev resources at tharindunipun.lk #JSON #Python #APIs #DataExchange #CodingTips #JSONParsing #Programming #DeveloperCommunity #TechSkills

Convert HTML tables to #PDF in Python. Use Aspose HTML for #Python via .NET to convert #HTML content, including complex tables. https://lnkd.in/eKkEEFyS