Florin Lungu’s Post

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

🚀 Piton v0.5.0: Modernizing the Bridge Between Elixir & Python I'm excited to announce a major upgrade to Piton, the open-source library that lets you run Python code from Elixir while bypassing the GIL! After months of work, v0.5.0 is here with a completely modernized stack. 🎉 🔧 The Modernization: We've brought Piton into 2025 with: ✅ Elixir 1.19 + OTP 27 support ✅ Python 3 only (Python 2 retired) ✅ Built-in JSON - removed Poison dependency ✅ GitHub Actions CI/CD - automated testing & publishing ✅ Latest dependencies - erlport 0.11, ex_doc 0.39 All 13 tests passing ✅ | Fully automated | Production ready 💡 Why This Matters: The real power isn't just the tech stack - it's what you can build with it. Real-world scenarios where Piton shines: 🔹 ML/AI in Phoenix Apps Run TensorFlow or PyTorch models directly from your LiveView without blocking the BEAM 🔹 Data Science Pipelines Leverage NumPy, Pandas, and SciPy while maintaining Elixir's fault-tolerance 🔹 Legacy Python Integration Migrate to Elixir gradually - wrap existing Python services without rewriting everything 🔹 Parallel Processing True parallelism - run multiple Python algorithms concurrently, bypassing the GIL using Erlang's process model 🔹 API Enrichment Call Python NLP libraries, image processing tools, or scientific computing packages from your Phoenix APIs 🎯 The Elixir + Python Sweet Spot: You get: •🏃♂️ Elixir's concurrency without the GIL limitation •🐍 Python's rich ecosystem (350K+ packages) •🛡️ Fault tolerance - Python crashes won't take down your app •⚡ Performance - modern OTP 27 optimizations •🤖 DevOps ready - full CI/CD automation Whether you're building ML-powered Phoenix apps, migrating Python workloads, or just want the best of both worlds - Piton v0.5.0 is ready. 📦 Get it: https://lnkd.in/ecarHYk 📚 Docs: https://hexdocs.pm/piton 💻 GitHub: https://lnkd.in/dkk9W8M #Elixir #Python #OpenSource #MachineLearning #AI #WebDevelopment #Phoenix #DataScience #SoftwareDevelopment #DevOps #ElixirLang #FunctionalProgramming

LangChain in the wild — Python vs. TypeScript Over the past few weeks, I’ve been experimenting with both LangChain (Python) and LangChain.js (TypeScript) to see which direction developers should bet on for the long run. Here’s what stands out: - LangChain (Python) is miles ahead in ecosystem maturity — richer integrations, faster updates, and a huge community around RAG, vector stores, and agent frameworks. - LangChain.js feels leaner and more developer-friendly for web integrations, especially if you’re already deep in Node or React. But it still lags behind when you want to do serious AI orchestration or model evaluation. My takeaway: build with LangChain.js if you want fast web deployment, but learn the Python version if you want to go deep into GenAI systems. The future probably belongs to teams that can leverage both — Python for intelligence, TypeScript for delivery. #AI #LangChain #TechLeadership #DeveloperExperience

Ever wondered why your backend APIs sometimes feel sluggish even though your code looks clean? One game-changing approach gaining traction for boosting API performance is **Asynchronous Python with asyncio**—and it’s time more developers gave it a serious look. Traditionally, Python’s synchronous code handles one task at a time. This works fine for simple scripts, but APIs that deal with multiple I/O operations—calling databases, external services, or complex file handling—can bottleneck your throughput. The server waits for each task to finish before moving on. Enter asyncio: Python’s native solution for async programming. It lets you write code that can handle multiple operations seemingly at once, without spinning up multiple threads or processes. Instead, it uses event loops and coroutines that pause work where waiting is necessary (like fetching data) and switch to other tasks meanwhile. The result? More efficient resource utilization and snappier responses under load. Why should backend engineers care about asyncio right now? Because modern frameworks like FastAPI and even Django (with recent updates) support asyncio natively. Even better: the community tools and libraries are catching up fast, making integration smooth and practical. Here’s a quick practical tip if you want to play around: - Use async def for your endpoint functions. - Leverage async database drivers (e.g., asyncpg for Postgres). - Await external API calls inside coroutines instead of blocking calls. You might hit a learning curve—async can be different from standard sequential thinking—but once you get the hang of it, your backend can handle far more requests simultaneously without the cost and complexity of threading. By embracing asynchronous Python today, you're not only writing cleaner code but future-proofing your services to handle scale efficiently. Have you experimented with asyncio yet? What challenges or wins did you experience? Let’s chat about the async revolution happening quietly in Python backends. #Python #AsynchronousProgramming #BackendDevelopment #APIPerformance #FastAPI #TechTrends #SoftwareEngineering #DeveloperTips

"Typed languages reduce hallucination surface area..." TypeScript, Python, and the AI feedback loop changing software development - The GitHub Blog https://lnkd.in/gpz7PZ-y

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 Functions: The Secret Ingredient Behind Code That Feels Effortless🐍⚡ Have you ever opened someone’s Python project and thought: “Damn… this code is smoother than butter.”😌 That’s not luck. That’s not magic. 👉 That’s the power of functions. In Python, functions aren’t just reusable blocks of code — they’re the reason great developers write code that is: ✨ Clean ✨ Predictable ✨ Scalable ✨ And insanely easy to read Here’s why top engineers swear by them: 🔹Organize everything — break big problems into tiny, elegant pieces 🔹Reuse logic endlessly — write once, use everywhere 🔹Improve collaboration — your teammates understand your work instantly 🔹Reduce bugs — small functions = fewer headaches And Python gives you extra superpowers like: ⚡ Lambda functions → write logic in a single line 🎨 Decorators → add features without rewriting code 🔁 Higher-order functions → treat functions like data and unlock crazy flexibility 🧠 Closures & scopes → make your code smarter and state-aware Whether you're building a recommender system, a fintech backend, or an AI agent mastering functions is the fastest way to level up your code quality. 🚀

Loraine Lawson shares Laurie Lay’s insights on why JavaScript is emerging as a strong alternative to Python for machine learning, blending AI with frontend development.