"TypeScript, Python, and AI feedback loop in software dev"

In an insightful interview with Idan Gazit, the leader of GitHub Next, we explore how TypeScript, Python, and the evolving AI feedback loop are reshaping the landscape of software development. What stood out to me was the potential these technologies have to streamline workflows and enhance collaboration in coding. How do you see these changes impacting your work or the industry as a whole?

TypeScript Just Overtook Python | The Developer Shift of 2025 For the first time ever, TypeScript has surpassed Python in GitHub repository activity, a moment that marks more than just a popularity contest. It’s a sign of how modern development is evolving: 💡 Developers are choosing typed safety over dynamic freedom. ⚙️ AI-assisted tools like Copilot thrive on predictable, type-aware code. 🌐 Enterprises are scaling large, complex projects and TypeScript’s reliability fits the bill. Python still reigns supreme in AI and data science, but when it comes to building the digital backbone of the web, TypeScript is becoming the new default. This isn’t the death of Python. It’s the rise of a new era of structured, AI-augmented development. What do you think? Are you sticking with Python’s flexibility or embracing TypeScript’s precision? #TypeScript #Python #GitHub #DeveloperTrends #AIinCoding #SoftwareDevelopment #BinateSolutions

Choosing between Go and Python isn’t about picking a “better” language, it’s about picking the right one for the job! Mario Šumiga, Sr. Software Engineer at Zartis, tests both languages side by side with a practical example - scraping and processing text from multiple URLs. The takeaway? 🧠 Python wins on simplicity and speed of development. ⚡ Go dominates in raw performance and concurrency. If you’re deciding what tech stack suits your next high-performance or data-heavy project, this one’s worth a read. 🔗Read the full comparison with practical examples here: https://lnkd.in/dnCTb52u

Came across this article arguing Java/Embabel outperforms LangGraph. I see it differently. https://lnkd.in/gAkE42Qr Why Python remains the better choice: 🚀 Faster iteration cycles - Ship and refine in days instead of weeks ⚡ Built on LangChain - The most established AI framework with proven integrations and extensive tooling Enterprise patterns have their place, but they can slow you down. In the rapidly evolving AI space, agility beats perfection.Build your agents where the AI community thrives: Python.

Why Performance Matters in Python Development Python is loved for its simplicity and flexibility, but when it comes to performance, it still poses challenges for real-world systems. Here are some key insights from JetBrains’ latest article: • Python’s interpreted nature introduces runtime overhead compared to compiled languages. • The Global Interpreter Lock (GIL) limits multithreading for CPU-bound workloads. • Poor data structures and lack of profiling often hurt performance more than the language itself. • Performance directly affects cost, scalability, and user experience. • Optimization helps developers work faster and keeps the code easier to maintain. Read the full article: https://lnkd.in/dP4WU5PS #Python #Performance #SoftwareEngineering #Optimization

Is Python 🐍 safe?" That's a fascinating question! 🤔 In one critical way, YES, absolutely! 👍 Python 🐍 is wonderfully memory-safe. It has a garbage collector 🗑️, which means developers don't manually manage memory. This completely prevents a whole class of terrifying bugs 👻 like buffer overflows or use-after-frees that plague older languages. You're safe from those! 🥳 However, "safe" has other meanings! Python's 🐍 main "risk" is its dynamic typing. Type errors (like TypeError) are only caught when the code runs 🏃♂️, which can sometimes mean in production! 💥 This is a totally different philosophy from a language like Rust 🦀. Rust 🦀 is also memory-safe, but it provides this guarantee in a unique way: at compile-time ⏳ with its famous Borrow Checker 🛡️. No garbage collector needed! This means Rust 🦀 catches memory and type errors before the program can even run. 🚫 When it comes to concurrent programming, Rust 🦀 also provides "fearless concurrency" 🚀, a powerful compile-time safety guarantee against data races that Python doesn't offer. So, is Python 🐍 safe? Yes, it protects you from memory chaos. Is Rust 🦀 safer? It offers a stricter, more comprehensive set of safety guarantees (memory, type, and thread safety) that are enforced by the compiler 🦾. It's all about trade-offs! ⚖️ #PythonSafety #RustLang #Programming #TechTalk #MemorySafety #FearlessConcurrency #CodingDebate #SoftwareDevelopment #DeveloperLife

🐍📰 Async IO in Python: A Complete Walkthrough This tutorial will give you a firm grasp of Python’s approach to async IO, which is a concurrent programming design that has received dedicated support in Python. #python

I’ve been reading and experimenting with Python 3.14 lately and kinda it’s a important update. Most Python updates give us a few syntax improvements or standard library tweaks. But this one? It changes how Python itself runs. While testing some concurrency-heavy scripts, I came across multiple interpreters (PEP 734) a new feature that lets you create independent Python interpreters inside the same process. Each interpreter has its own GIL, meaning for the first time, we can run Python code truly in parallel. no multiprocessing hacks, no GIL fights. Combine that with the new tail-call interpreter (a low-level CPython optimization that improves speed), and you can feel Python’s architecture evolving toward something far more scalable. It’s a glimpse of Python’s future more modular, more concurrent, and ready for multi-core systems. I wrote a deep dive about it with examples, diagrams, and how to get started here: https://lnkd.in/ehN43ECw #Python #Python314 #Concurrency #Performance #SoftwareEngineering #Developers #Programming

Everyone says "English is the new programming language." Yet every AI breakthrough still compiles down to Python. Not because Python is fastest. It's not. Not because Python is most efficient. It's not. Not because Python has the best syntax. Debatable. Python wins because it's the universal adapter: → Every ML framework has Python bindings first → Data pipelines default to Python infrastructure → Prototypes ship faster in Python than production-ready code in other languages → When LLMs generate code, they output Python because that's what actually works Other languages optimize for performance. Python optimizes for getting stuff done. In the AI era where shipping beats perfecting, Python's "good enough and works everywhere" philosophy is the competitive advantage. You can write faster code in Rust. You can write safer code in Java. You can write more elegant code in... okay maybe not. But you can ship AI products fastest in Python. Tuesday reminder: The best language isn't the most powerful. It's the one that connects to everything else. Python doesn't dominate because it's perfect. It dominates because it's practical.

100 Days of learninig challenge : Day 30 🔑 Unlock Your Code's Hidden Turbo Mode: The Simple Python Module That Executes C at Lightning Speed.(Uses strong, action-oriented language and promises immediate value/speed.) We love Python for its readability, vast ecosystem, and rapid development speed. But when performance bottlenecks hit—especially in deep computation or low-level systems—we inevitably look toward the raw power of languages like C. The struggle is deciding which to compromise on. The breakthrough moment in our coding journey is realizing we don't have to choose! We can leverage a powerful, built-in Python module to command C code execution, effectively granting our user-friendly Python applications a turbo-boost from C's machine-level efficiency. This integration is the hallmark of professional software architecture where every tool is used for its best purpose. The Turning Point: Mastering Python's Subprocess Module The secret to this seamless integration lies within Python's subprocess module. We learned that this tool allows our Python script to act as a system orchestrator, running any command we could execute in a terminal—including a pre-compiled C program. This approach gives us complete control over performance-critical operations while retaining Python for all the high-level logic, file handling, and application flow. Our Key Takeaways for C-Python Integration: The Performance Driver: We first write the performance-critical logic in C, ensuring it compiles into a standalone, optimized executable (using a tool like gcc). The Orchestrator (subprocess.run): We use Python's subprocess.run() function, passing the C executable's path as the primary command within a list. This command execution is where Python effectively "hands off" the heavy lifting to the C program. Passing Data Seamlessly: The real power is in data transfer. We demonstrated how to pass variables (such as user input) directly from our Python environment as command-line arguments to the C program's argv array, enabling C to perform operations on Python-provided data. Two-Way Power: This method allows us to harness C for computation and then read the results back into Python, combining the best features of both languages into a single, highly efficient application. By adding C execution to our Python skillset, we've upgraded our capability to build applications that are not only easy to write but are also blazing fast. We encourage everyone to explore the capabilities of the subprocess.run() function, as it is the gateway to unlocking multi-language power! #100DaysLearningChallenge #Python #CProgramming #PerformanceOptimization #ProgrammingSkills #SoftwareArchitecture #CodeIntegration #TechSkills Video link :- https://lnkd.in/dVQ7uPCz