Python DSA and Node.js Development Progress

Small build. Big learning. 🚀 While learning how real-world applications handle JSON data and APIs in Python, I built a simple CLI Currency Converter. The program fetches live data from public APIs to: 💱 Detect currency codes based on country names 🌍 Fetch real-time exchange rates 💻 Convert currencies directly from the terminal It’s a very small project, but in my opinion, learning by actually coding is much better than hours of tutorials. Writing the code, debugging errors, and working with actual API responses taught me far more than just theory. This project was my first step into working with real-world JSON data and external APIs using Python, and it’s encouraging to see how quickly small experiments turn into meaningful learning. Looking forward to building many more small tools while exploring Python, APIs, and real-world data processing. #Python #LearningByBuilding #APIs #JSON #CLI #Programming #DeveloperJourney

Python 3.15 might be shipping one of the most exciting performance tools Python has added in a while, that too right after the GIL-free release. It is called Tachyon. Python is adding a new statistical sampling profiler under `profiling.sampling` called Tachyon, and the positioning is what caught my attention: - attach to a running Python process - no code changes - no restart - designed for low-overhead profiling - supports outputs like flamegraphs, speedscope-compatible stacks, Firefox Profiler, heatmaps, live TUI, async-aware views, and even opcode-level profiling The docs go even further and describe it as capable of sampling at up to 1,000,000 Hz and suitable for production performance debugging. With ML pipeline domination and RESTful servers now written increasingly in Python, this profiler will be a great addition. No longer will the notion of “run a profiler locally and hope the issue reproduces” carry on, and profiling becomes a first-class citizen, as in languages like Java. This is from the Python 3.15 alpha docs, so details may change before final release. But this is absolutely worth watching. A big thanks to the Python Software Foundation and Hugo van Kemenade for the updates and articles. Read more in the link below: https://lnkd.in/eUJ3C4N7 #Python #Python315 #Performance #Profiling #SystemsEngineering #Observability #Backend #SoftwareEngineering

Python is hard. Nobody tells you this. Here's what 4 years actually taught me: 🔍 1. Type Hinting saves you from pain In big codebases, mypy is a lifesaver. Trust me on this one. ⚡ 2. Database indexing beats code tricks Your SQL query is slow? Check indexes first. 10ms vs 2 seconds happens there, not in your loops. 📝 3. Logging is better than debugging You can't debug production. Write logs that tell the full story. Your future self will thank you. ✅ 4. Pydantic catches errors early Validate data at the entry point. Not deep inside your code. This changed everything for me. Python looks easy at first. But mastering it? That's a different game. What's one thing you wish someone told you when you started backend development? Drop your biggest Python lesson below 👇 #PythonDev #BackendDevelopment #CodingTips #TechCareer #Python #SoftwareEngineering #WebDevelopment #Django #Programming #DeveloperLife #TechTips #Backend #SoftwareDeveloper #LearnPython #CodingJourney

Day 13/100: Leveling Up APIs with Validation, Error Handling & Authentication 🚀 This week has been all about making my APIs smarter, safer, and more robust. Here’s a quick recap of what I accomplished: Python (FastAPI) – Input Validation & Error Handling Added request validation using Pydantic Implemented global error handling Result: Python API now validates inputs and handles errors gracefully. Node.js (Express) – Input Validation & Error Handling Used Joi for request validation Built global error-handling middleware Result: Node.js API now validates requests consistently and reports errors reliably. Python JWT Authentication Created register/login endpoints with password hashing via Passlib Generated and verified JWT tokens to secure endpoints Result: Python API endpoints are now secured with authentication. Every day, my APIs are becoming stronger. Excited to keep building and stacking these skills! Chris Nyeche #100DaysOfCode #Python #NodeJS #FastAPI #ExpressJS #JWT #BackendDevelopment #APIDevelopment #BuildInPublic

Today I expanded more on what I learned yesterday, and that includes learning from the mistake of not being able to notice a misplaced variable. Today all my python scripts ran successfully and here is what was added on my Github portfolio under Subdomain Emuneration; - Adding larger wordlist, which was an extension of the word list that was created yesterday. This was nano script with .txt - DNS resolution for each subdomain - Identify valid subdomains - save results to a file for analysis - Multithreading, which focuses on speeding up the scanning process Furthermore, I noticed that since I started writing in plain english what each line/variable in the script means, it is becoming easier to understand what each line/variable means in the script, what the difference from one script to another is, especially with the saving of results scripts where it is important to notice the "output.write( result + "\n) variable. This exercise taught me an important aspect of everything that I am doing and that is to debug and follow the rules of scripting, especially on a foundational level.

🚀 Excited to share a small milestone! Last week I open-sourced a Python tool I’ve been working on: AsyncAPI Payload Validator. It helps validate message payloads against AsyncAPI specifications, making it easier to ensure that event-driven systems publish and consume messages that match their contracts. 🔧 Built for teams working with event-driven architectures who want stronger schema validation and better reliability in their messaging systems. In just one week, the package has already reached 900+ downloads, which is incredibly encouraging! 🙌 Open source is all about sharing, learning, and improving together — and I’m excited to see how others might use or contribute to it. If you're working with AsyncAPI, event-driven systems, or message validation in Python, feel free to check it out: https://lnkd.in/eUQKr3RU Feedback and contributions are very welcome! #opensource #python #asyncapi #eventdriven #softwareengineering

Python Starters Day 17 Foundation Nugget Comparison is logical ==, !=, >, <, >=, <= These operators drive decision-making. Computers don’t understand almost or maybe as they compare exact values. Strong programmers write clear comparison statements, while weak comparisons create hidden bugs. When something behaves strangely, check your condition. Follow the Python 🐍 Starters Hub: WhatsApp: https://lnkd.in/dbjAFv52 LinkedIn: https://lnkd.in/dkJE3tZq

Write C Code Without Learning C: The Magic of PythoC an interesting library the other day that I hadn’t heard of before.  PythoC is a Domain-Specific Language (DSL) compiler that allows developers to write C programs using standard Python syntax. It takes a statically-typed subset of Python code and compiles it directly down to native machine code via LLVM IR (Low Level Virtual Machine Intermediate Representation). LLVM IR is a platform-independent code format used internally by the LLVM compiler framework....

Python is the best for reporting. It gives full control to validate every subprocess. Version control enables rollback, and reuse of logic. It scales to larger data, it integrates databases, Excels, emails, and even PDFs. All for free without the pain of no-code.

Your Python code is leaking resources. Here's how to fix it. In a recent project, we were processing large files and noticed our application was consuming more memory than expected. The issue was that we weren't properly managing file handles and database connections. This led to resource leaks, which in turn caused our application to slow down and eventually crash under heavy load. The impact was significant, with our application becoming unresponsive during peak usage times. Python Context Managers provide a elegant way to manage resources. They ensure that resources are properly acquired and released, even if an error occurs. Context Managers use the 'with' statement and the __enter__ and __exit__ methods to handle setup and teardown. This is better than manually opening and closing resources because it's more readable, less error-prone, and ensures resources are always released. The __exit__ method can also handle exceptions, making error handling more robust. 💡 Key Takeaway: Use Context Managers for resource management in Python. They provide a clean and efficient way to handle resources, ensuring they are properly released. This can significantly improve the performance and stability of your application, especially when dealing with large files or multiple connections. 🐍 Have you used Context Managers in your projects? Share your experiences and tips in the comments! #Backend #Python #PythonProgramming #FastAPI #Programming #Coding