Why Python Isn't Always the Best for Developers

Java ♨️ vs Python 🐍: Choosing the Right Language for Your Tech Journey 🖥 ✅️ Every developer, at some point, faces the classic question: Should I learn Java or Python? 🫡 Both are powerful. Both are widely used. But their strengths — and the opportunities they create — are very different. 🤔 Here’s the truth: the right choice depends on what kind of tech professional you want to become. 🐍 Python shines with its simplicity. It’s clean, beginner-friendly and incredibly versatile. From AI and machine learning to automation, scripting and rapid prototyping, Python lets you build faster and experiment more freely. It’s the favorite language for data scientists, AI researchers and anyone who thrives on solving complex problems with fewer lines of code. ♨️ Java, on the other hand, is built for scale and stability. It powers massive enterprise systems, banking platforms, Android apps and high-performance backend systems. Its strong type-safety and robustness make it a developer’s go-to language when reliability and security matter the most. If you want to work in enterprise tech, product engineering or large-scale systems — Java opens doors. ✨️ But here’s where it gets interesting: The future isn’t about choosing one over the other. It’s about understanding which language aligns with your goals — and mastering it deeply. 🐍 Python gives you speed. ♨️ Java gives you structure. 🤗 Both give you opportunity. So instead of chasing trends, choose the language that matches your ambitions — and commit. Great developers grow not by knowing every language, but by mastering one and thinking like an engineer. #Java #Python #Programming #SoftwareDevelopment #CareerGrowth #TechSkills #Developers #CodingJourney

State of Python 2025: Web Development Makes a Comeback! The latest Python Developers Survey, a collaboration between the Python Software Foundation and  JetBrains, has captured insights from over 30,000 developers worldwide — revealing some surprising  shifts in how Python is being used in 2025. Key Highlights: 50% of Python devs have less than 2 years of professional coding experience — showing Python’s unmatched accessibility for newcomers. Data science remains dominant, with 51% using Python for data exploration & processing. Web development is back! Usage jumped from 42% in 2023 to 46% in 2025, fueled by frameworks  like FastAPI, now adopted by 38% of developers. Outdated Python versions are costing businesses millions — upgrading could boost performance by up to 42%. Rust is now powering up Python: nearly 1/3 of new native code on PyPI uses Rust for speed and efficiency.  What’s next for Python: Free-threaded Python (v3.14) is coming — removing the GIL and unlocking true parallel processing. AI coding assistants are going mainstream — 49% of devs plan to use them soon.  Native mobile apps with Python are becoming a reality, with official iOS and Android support in the works! “Python’s future is being written by a new generation — one that’s curious, bold, and ready to take Python everywhere.” — Michael Kennedy, Python Software Foundation Fellow From AI and data to web and mobile, Python continues to evolve — proving once again why it remains the heartbeat of modern development. #Python #WebDevelopment #DataScience #AI #Developers #Programming #JetBrains #PythonSoftwareFoundation #Rust #Technology #Innovation #Coding

💻 *How to Learn Programming in 1 Year – Step by Step* 📝✨  ✅ *Tip 1: Start with a Single Language*  Choose one language (Python, JavaScript, or Java) and stick to it. Mastering one language deeply is better than learning many superficially.  ✅ *Tip 2: Learn the Basics First*  Focus on fundamental concepts:  • Variables & Data Types  • Loops & Conditionals  • Functions / Methods  • Lists, Arrays, Dictionaries / Objects  ✅ *Tip 3: Practice Small Projects*  Build small programs every week:  • Calculator  • To-do list app  • Simple web scraper  • Guess-the-number game  ✅ *Tip 4: Understand Problem-Solving & Logic*  Programming is mostly problem-solving:  • Break problems into steps  • Write pseudocode  • Debug carefully  ✅ *Tip 5: Learn Version Control*  Use Git to track code changes, collaborate, and avoid losing progress.  ✅ *Tip 6: Read Others’ Code*  Check open-source projects to see how experienced developers write code and structure projects.  ✅ *Tip 7: Practice Coding Challenges*  Use platforms like LeetCode, HackerRank, or Codewars to improve logic, algorithms, and speed.  ✅ *Tip 8: Understand Key Concepts Deeply*  • Object-Oriented Programming (OOP)  • Recursion  • Data Structures – Arrays, Lists, Stacks, Queues, Trees• Algorithms – Sorting, Searching  ✅ *Tip 9: Build Real Projects*  • Portfolio website  • Chatbot  • Data analysis with Python  • Simple game  ✅ *Tip 10: Be Consistent & Review*  Practice coding every day, even 30–60 minutes. Review old code to improve style and understanding.  💬 *Tap ❤️ for more!*

For a long time, I wrote almost everything in Rust, precise, strict, beautifully unforgiving. I did not build every system in Rust; I preferred it and delegated other stacks when it made sense. Over time, one thing became clear: real technical leadership requires breadth as much as depth. Every modern engineer should think fluently in at least three languages: 🦀 Rust: for building fast, reliable systems. Its strict type system and compiler discipline teach real engineering thinking. 🟦 TypeScript: once just a frontend tool, now the foundation of the growing AI SDK ecosystem (Claude, OpenAI, LangChain, and many others). 🐍 Python: the operating system of applied AI. Most agent frameworks are written in Python, which makes it the easiest path to assemble, run, and iterate on real agent workflows. It is also the default for model training and local ML. How to put this into practice this week: * Rust: ship a tiny CRUD service or CLI with Axum, SQLx, Serde. One health check, one write, one read. * TypeScript: use the Claude SDK to build a simple PDF text analyzer that returns structured JSON. * Python: deploy both using Pulumi for Python. Pulumi is an infrastructure-as-code tool like Terraform that supports many languages; use the Python SDK to define one stack, manage secrets cleanly, and get repeatable builds. Fluency across these three turns you from a single-instrument player into a conductor. You design the score, assign the parts, and ship on tempo. That is the skill set I hire for, develop in my teams, and hold myself accountable to.

Python is a high-level, general-purpose, and versatile programming language known for its readability and beginner-friendliness. It was created by Guido van Rossum and first released in 1991. Its design philosophy emphasizes code readability through the use of significant indentation. Key Features and Characteristics: Simplicity and Readability: Python's syntax is designed to be clear and concise, often described as similar to the English language, making it easier to learn and understand compared to many other programming languages. Versatility and General Purpose: Python is not specialized for a particular domain and can be used for a wide range of applications, including web development (server-side), software development, data analysis, machine learning, artificial intelligence, automation, scripting, and scientific computing. Multi-paradigm: Python supports multiple programming paradigms, including object-oriented, procedural, and functional programming. Extensive Libraries and Frameworks: Python boasts a vast ecosystem of libraries and frameworks that extend its functionality and accelerate development in various areas. Examples include Django and Flask for web development, NumPy and Pandas for data analysis, and TensorFlow and PyTorch for machine learning. Cross-platform Compatibility: Python code can be written and run on various operating systems, including Windows, macOS, and Linux, without significant modifications. Interpreted Language: Python is an interpreted language, meaning code can be executed line by line without a separate compilation step, which facilitates rapid prototyping and debugging. Common Uses of Python: Web Development: Building web applications and APIs using frameworks like Django and Flask. Data Science and Machine Learning: Performing data analysis, visualization, and developing machine learning models using libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. Automation and Scripting: Automating repetitive tasks, system administration, and creating utility scripts. Software Development: Developing desktop applications, games, and various software tools. Scientific Computing: Used in research and scientific applications for complex calculations and simulations. Python's ease of use, extensive capabilities, and large, active community have made it one of the most popular and in-demand programming languages today. #snsinstitutions #snsdesignthinkers #designthinking

Python package management is a pain. It becomes obvious when one moves to Go, where it’s a much better experience. One way to solve the pain, especially in larger teams, is to start from the very first thing: package manager. Homebrew is a good start. In the past I’ve successfully used pkgsrc. Then there are others like Conda, nix, & Macports. I’m sure there are many others I don’t even know about. After you choose a package manager, you build your own base Python. You start building dependencies from requirements.txt or pyproject.toml or whatever as native packages. It’s not an easy solution at all. But it gives you the most control to serve your target developers. You can then choose escape hatches to lower your maintenance burden. For example, don’t build your own base Python but choose the one built by your package manager maintainers. Once you have a reliable build pipeline that can continuously build newer releases & a team to shepherd them, it becomes an internal service. This idea is a variation of the “use Docker containers to package & ship your application”. Only in this case you start from a level lower than containers (which can also use your package manager of choice). Is Python worth all this effort? A resounding yes! Once you start thinking this way, you can even move upstream & contribute to official packages, helping not just your team but others, too. Maybe I’m the lone warrior who loves to build packages. I’ve done that in various roles throughout my career. Maybe I enjoy it too much to propose such a heavy investment as a “simple” solution to others. But if you have already invested your team in the Python ecosystem, it’s worth a look at doing it better than what’s available to you.

✨If you're stepping into the world of programming, choosing your first language can feel overwhelming. Python shines with its simple syntax and is perfect for careers in AI, Machine Learning, and Data Science. On the other side, JavaScript powers almost everything you see on the web — making it the go-to language for Web Development and Full-Stack roles. Both languages offer huge career opportunities, but the “right” choice depends on your goals. Want to build websites? Start with JavaScript. Interested in data, automation or AI? Go for Python. No matter what you pick, both paths open doors to amazing tech careers. If you want to know more, read this article here.👇

Python Dev Heads are Mentally Retarded I can use science to prove that Python software developers are in fact mentally retarded. You see, once you're "mentally challenged", the first thing others starts noticing about you, is that you make "bad decisions". Hence, your inability to make good decisions becomes a good metric to use when trying to measure your cognitive abilities. The more bad decisions you make basically, the larger the statistical probability of that you're a retard becomes ... Since roughly 80% of all software developers in the world reaches for Python "by default" once confronted with a software development problem, this implies all we need to do in order to classify these individuals as imbeciles, is to prove that Python is not the optimal tool for the job at hand. In this video I've got three examples; 1. CRUD read endpoint 2. Send email endpoint 3. Integrate with 3rd party HTTP API In all 3 examples Python produces on average 3 to 4 times as much code as Hyperlambda counting "tokens". Tokens again is a already used by LLMs to measure "cognitive complexity", and is therefore for all practical concerns a very good metric to use to also measure "human resource requirements" to solve some particular software development problem. Hence, if I need 1 week to do something in Hyperlambda, you'll need 3 to 4 weeks in Python, and 6 to 8 weeks in C# to implement a functionally similar solution. Notice, my references are in my video. Since Python seems to be consistently using about 300%+ as many tokens as Hyperlambda, and in addition literally needs roughly 500 to 700 percent the hardware requirements during runtime - It is therefore safe to claim the following ... "All Python software developers are mentally retarded, and should not be allowed to make decisions for obvious reasons" ... Hence, if you've got a Python software developer in your software development department, you should prevent him from being able to influence your tool choices in the future - At least until he "grows up" and starts using Hyperlambda ... Alternatively simply fire him, and sue him for damages claiming "gross negligence" ...

Java or Python for Building Agents? AI success isn’t about picking the trendiest language — it’s about enabling your people. Python, Java, C#, JavaScript… what matters most is letting your team build with the tools they know best. Talent + pragmatic tech decisions = competitive advantage.