Go's AI Prototyping Advantage: Dynamic Languages Outperform Static

PYTHON NO LONGER ENDS WITH CODE. It begins where the architecture of intelligence begins. For years, Python was seen as a programming language. A practical tool. A clean syntax. A fast way to build software. But that description is no longer enough. TODAY, PYTHON IS BECOMING SOMETHING FAR GREATER. It is turning into a language of orchestration: of models, of tools, of agents, of reasoning chains, of decision layers, of context, and of action. Not long ago, a developer wrote functions. NOW, MORE AND MORE OFTEN, A DEVELOPER DESIGNS BEHAVIOR. That is a profound shift. Because the real question is no longer: Can you write code? The real question is: CAN YOU BUILD A SYSTEM IN WHICH CODE, MODEL, DATA, MEMORY, AND CONTEXT BEGIN TO WORK AS ONE? This is exactly why Python is not disappearing in the age of AI. Quite the opposite. ITS STRATEGIC ROLE IS GROWING. Because very few languages combine so much at once: simplicity, abstraction, integration, automation, experimentation, and the ability to move from idea to working system with extraordinary speed. And that is why the future will not belong to those who merely write code. IT WILL BELONG TO THOSE WHO CAN DESIGN THE ARCHITECTURE OF DECISION. The engineer of the coming years will not be judged only by syntax. Not only by frameworks. Not only by whether a script runs. They will be judged by whether they can create structures in which intelligence becomes usable, directed, and real. PYTHON IS NO LONGER JUST A LANGUAGE OF SOFTWARE. IT IS BECOMING A LANGUAGE OF AGENCY. A language for building systems that do not merely execute instructions, but coordinate meaning, logic, memory, and response. So the real question is no longer: Should people still learn Python? The real question is: CAN YOU USE IT TO BUILD SYSTEMS THAT THINK WITH YOU, ACT WITH YOU, AND EXTEND HUMAN CAPABILITY? That is where the game is now. And many still do not see it. #Python #AI #LLM #MachineLearning #SoftwareArchitecture #Agents #Automation #FutureOfWork

With low-quality tests, you're paying tokens for fixing them with little to no benefit that tests should provide Let's be real. Most of the Python tests out there are a waste of time. They are there to make the manager happy, to pass the compliance review, or to exercise dominance. Talking about tests that: - break due to unrelated changes, - make you restart the CI/CD pipeline and hope that they pass on the next run, - take forever to run, - pass, but the production is broken. Back in the day, one complained about having to work with such tests. Nowadays, we're paying LLM tokens while Claude Code is fixing them over and over. Pure waste of time and money. In my latest article, I'm describing 7 qualities of highly valuable tests that every developer should know. Qualities of tests that help you ship faster with AI without losing confidence or turning your status page into a traffic light🚦 Don't forget to subscribe to not miss the next tip 🔔

🚀 From JavaScript to Python in 5 Minutes? Here’s What Happened… Today I worked on a personal project where I tried shifting my codebase from JavaScript to Python — and honestly, I was surprised by how smooth the process was. With the help of GitHub Copilot, I gave access to my existing codebase, and within minutes… boom 💥 Most of the JS code was converted into Python! It felt almost magical, but it also got me thinking 👇 ✅ Upside: If you already have a good understanding of programming concepts, tools like this can be a complete game changer. They can save hours of manual work and help you experiment faster. ⚠️ Downside: Giving full access to your codebase — especially in production — can be risky. There are concerns around security, data exposure, and unintended changes. 👉 Lesson learned: Use AI tools smartly. They’re powerful assistants, not replacements for careful decision-making. Would I use it again? Yes. Would I use it directly on production code? Definitely not. Curious to know — have you tried using AI tools like this in your workflow? 🤔 #AI #GitHubCopilot #Python #JavaScript #LearningInPublic #Tech

↩️ I didn’t expect Python to send me straight back to 1999. A few weeks ago, I started diving deeper into Python.  What I didn’t expect was rediscovering patterns from the Smalltalk systems I worked on in 1999. Yes, Smalltalk. Back then I was knee‑deep in large, object‑heavy systems. I thought I had safely archived those memories in the “nice, but let’s not go back there” drawer.  But suddenly, Python’s dynamic nature and object model gave me a familiar déjà‑vu.  Did Python just wink at me? 🈹 My Vibe Coding Expedition  (a.k.a. Pair Programming with a Hyperactive Alien) Then I ventured into Vibe Coding — sketching code interactively with an LLM. Fun? Absolutely.  Predictable? Not quite. 😵💫 Ask for a design idea → get a chaotic buffet of functions you never ordered.  😵💫 As the project grows, the LLM starts to drift — forgetting its own methods like someone who walked into the kitchen and can’t remember why.  😵💫 Keeping the architecture clean requires long, surprisingly philosophical negotiations.  😵💫 And yes, sometimes the LLM becomes… stubborn. It happens. I learned quickly: CONTEXT IS EVERYTHING! So I started asking the LLM to write Markdown summaries of our decisions.  Otherwise, every new session felt like onboarding a colleague with complete amnesia. 🈴 And Then Came Skills, RAG, MCP & Tools  (a.k.a. “Congratulations, you’re now building agentic software.”) Somewhere along the way, I stumbled into the broader ecosystem: 👽Skills vs. RAG — realizing that sometimes the model doesn’t need more data; it needs a well‑defined capability.  👽MCP (Model Context Protocol) — suddenly I’m designing structured tool interfaces like I’m negotiating API contracts with an alien species.  👽Tools — because of course the LLM requires a toolbox now. Why wouldn’t it? At this point it became clear:  I’m not “just coding” anymore.  I’m orchestrating a small team of invisible interns with questionable attention spans. 📜 The Re‑Discovery: Spec‑Driven Development (SDD) Eventually I circled back to Spec‑Driven Development   (shoutout to the Martin Fowler article by Birgitta Böckeler: "Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl"). And I had to smile. Because in 1999, this was simply called: 😶 Doing your job properly. 😶 * You write the spec.   * You implement the spec.   * If you change the code, you change the spec first. 🙎♂️Back then it felt bureaucratic.  🤖 Today - in an AI‑assisted world - it suddenly feels like the future.   🧐The spec becomes the primary artifact.   🧐The code becomes the byproduct. A final realization about working with LLMs 👺 Their answers always make some sense — In Some Sense👺 #Python #AI #VibeCoding #SoftwareArchitecture #Smalltalk #AgenticAI #MCP #RAG #SpecDrivenDevelopment #TechHistory

Python's with statement makes one promise: "What happens inside this block, I will clean up after you". No matter if your code succeeds, crashes, or raises an exception halfway through, cleanup runs. Every time. That's a stronger guarantee than I realized. Once I understood why it works, I stopped writing code that quietly leaks resources. I just published Part 2 of my Python Context Managers series, where I break down: - How the with statement actually works under the hood - open() modes in full detail, and why "w" silently destroys your data - The file cursor, and why reading after writing gives you nothing - Why encoding="utf-8" is non-negotiable in cross-platform One keyword, one argument, zero resource leaks. Read here: https://lnkd.in/dGsHUCC4 Thank you for reading 😊. #Python #SoftwareEngineering #ContextManagers #Backend #WebDevelopement #BuildBreakLearn

AI can write code — but it can’t design your system Recently, I worked on a small project — a housing price prediction platform — to explore how different parts of a system come together. The setup was simple: 💠 A Python service running an ML model 💠 A Java backend handling APIs and market data 💠 A Next.js frontend combining everything into one portal While building this, I also experimented with using AI tools to generate parts of the code. But one thing became very clear: 👉 AI helps you build faster 👉 But it doesn’t replace thinking The real work was in: 💠 Deciding how services should communicate 💠 Separating responsibilities between layers 💠 Designing clean data flow across frontend and APIs For example, instead of calling the ML service directly from the frontend, I routed everything through the backend layer. That small decision made the system cleaner and easier to manage. So even though AI helped with implementation, the important part was still: understanding the system and making the right design decisions. I wrote a detailed breakdown of this project and what I learned here: 🔗 👉 https://lnkd.in/giAsVGt2 GitHub repo: https://lnkd.in/gMYbdCba Curious how others are using AI in development — especially when balancing code generation vs system design. #SoftwareArchitecture #AI #WebDevelopment #FullStack #DeveloperExperience

Built something that's been on my list for a while 👀 I built an open-source CLI tool called systemgen that lets you  save your entire project stack as a reusable profile and turn it into a  structured prompt any AI coding tool understands immediately. No need to constantly re-explain your setup EVERY single session. How it works: → Run systemgen init and answer a few questions about your stack → Your framework, folder structure, naming conventions + your prompt → Pipe it straight to Claude Terminal, Ollama, or copy to clipboard for Cursor Works for any language and stack: → Python / FastAPI → TypeScript / Next.js → Dart / Flutter → Go, Rust, JavaScript + more Useful when you want your AI to already know how you work before  you write a single line :) Pair it with mem0 = no starting from scratch, no re-explaining decisions you already made 🧠 w/ https://lnkd.in/eV7j5ScK Install 🔗: pip install systemgen Source 🔗: https://lnkd.in/e9FcDhdN Hope this helps ✅ Drop a Like if you found this useful! 👍 Follow me for more projects like this in the future💎 #python #opensource #ai #developertools

Everyone's generating code with LLMs. Almost nobody is systematically checking it. I ran into this while translating Python to C++. The translation part was easy. Knowing whether the output was correct? That was the real problem. I haven't written C++ for a while, so I might not even recognize a wrong answer. So instead of hoping the output is correct, I added a verification layer. 3 agents, each with a different job: → Agent 1 (Gemini 2.5 Flash): translates Python to C++ → Agent 2 (GPT-5 Mini): reads the original Python and generates test expectations → Agent 3 (GPT-5 Mini): evaluates the C++ against those expectations and flags issues If the evaluation fails, the issues get fed back to the translator. It retries (up to 3 rounds) until it passes or returns its best effort. One deliberate choice: the translator and the evaluator use different LLMs. If the same model translates and evaluates, it tends to confirm its own mistakes. Gemini translates, GPT evaluates. A genuine second opinion. The whole verification is static analysis. No compiler, no execution. The evaluator reads the C++ and reasons about correctness. For deterministic math code, this works surprisingly well. For anything more complex, the lack of execution is a real gap. And the obvious next step. It started as a course exercise where you translate Python to C++ and manually compile to verify. I wanted to automate the part where you stare at the output and hope. Repo: https://lnkd.in/g3tWFUPZ

Everyday I work with TypeScript, I see a very stark reminder of what Python could be if it prioritized static typing but that's story for another day. There's this new TypeScript book I'm reading: it's called Clean Code with TypeScript, written by Rukevwe Ojigbo and Sanjay Krishna Anbalagan and published by Packt. If you are a beginner to TypeScript or coding generally, I'll recommend you spend time in Chapter 2 of this book, titled 'Writing Clean Functions'. This is very important because even the best of generative AI agents like Claude Code and Codex make mistakes with this. Sometimes, these agents spawn functions that are like spaghetti: performing two or three tasks and creating big problems during debugging. Chapter 2 contains the holy grail for writing good, debuggable functions. I like this book because it does not obsess over code architecture like many clean code books I have read. Some clean code books leave your head spinning due to the sheer number of .ts or .py files that you need to create to maintain recommended clean architecture. Not this book! I think this is a super, beginner-friendly book that anyone that wants to learn TypeScript should read. Verdict: 5/5. Perfect for its audience. PS: A non-affiliate link to purchase a copy of the book on Amazon is in the comments section of this post.