Optimizing Memory & Concurrency in Python with Generators & Threads

A few weeks ago, a friend of mine who's a Math PhD told me he was completely stuck with his research. He's a genius at math, but coding isn't his thing. He was trying to use AI chatbots to help him turn complex formulas from academic PDFs into Python code so he could test his ideas. The problem? They kept hallucinating or just missing the logic in the math notation entirely. He was spending days trying to fix broken code that was supposed to save him time. He said: "I just want to test these ideas without getting stuck in the code every time." That stuck with me. I'm a software engineer, so I built him something. I called it AlgoMath, a specialized agent skill that sits on top of Claude Code and OpenCode. Instead of a generic chatbot, it follows a proper autonomous workflow to make sure the math actually stays accurate: It reads the PDF and pulls out the raw mathematical logic. Breaks it into structured steps. Turns those into clean, executable Python code. Runs it in a sandbox to catch errors. Then explains the results and checks everything against the original paper. A task that used to kill his whole week now takes about 30 seconds. He just tells his terminal agent to use the AlgoMath skill, and he's back to doing actual research. I open-sourced it and kept the setup simple: npm install, a small wizard walks you through the rest, and you're running it in your terminal agent immediately. Check it out: NPM: https://lnkd.in/d2TMKpjj GitHub: https://lnkd.in/dwWACnnH #SoftwareEngineering #AIAgents #ClaudeCode #Python #Math #AlgoMath #OpenSource

I keep wondering… why is almost every AI tool built on Python? It doesn’t really make sense at first. C++ is faster Rust is safer Java is built for scale So why did Python win? The answer is surprisingly simple. Because AI isn’t just an engineering problem. It’s an experimentation problem. When you’re building models, you’re not optimizing code first. You’re trying ideas. Breaking things. Testing again. Iterating constantly. Python just makes that easy. Less boilerplate Faster to write Easier to read A massive ecosystem ready to plug into And here’s the part most people miss. When you run an AI model, Python isn’t doing the heavy lifting. Underneath, it’s all highly optimized C++, CUDA, and hardware acceleration. Python is just the glue that holds everything together. So in a way, Python didn’t win because it’s the fastest. It won because it gets out of your way. And maybe that’s the bigger lesson beyond AI. Sometimes the best technology isn’t the most powerful one. It’s the one that lets more people build, faster. Curious how you see it. Do you think Python will still dominate AI in the long run, or are we heading toward something else? #ArtificialIntelligence #Python #MachineLearning #DataScience #SoftwareEngineering #TechLeadership #Innovation #AI #Programming #FutureOfWork

Processing large volumes of data—especially when they exceed the amount of RAM available—can be a real challenge. Fortunately, the Python ecosystem offers powerful tools to tackle this by applying a classic strategy: *divide and conquer*. In this post, I introduce the concept of **generators** and put it into practice with an example of raster image processing. This is the first article in a broader series on efficient data handling. I hope you enjoy it and find it useful! https://lnkd.in/e23c7Er9

🚀 Recently, I worked on a set of important problems. The challenge wasn’t about how hard the questions were… It was about applying every core concept in Python the right way. 💡 What I focused on: Understanding the problem before jumping into solutions Breaking down thinking into clear steps Writing clean, readable, and maintainable code Building logic instead of solving randomly 🔥 The most valuable part was working with: Different data types in Python (and understanding when to use each one) OOP concepts that helped me think in a structured way: • Class / Object • Encapsulation • Inheritance • Polymorphism • Abstraction 📚 What I gained: I started thinking like a problem solver, not just a coder My code became simpler, cleaner, and more organized 🎯 Next step: Applying the same mindset to larger projects, especially in Software Development and AI. Special thanks to Eng/ Mahmoud abdelnaby for the valuable workshop and guidance. I’d appreciate any feedback or advice 🙌 #Programming #ProblemSolving #Python #OOP #SoftwareDevelopment #AI #LearningJourney

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

Do you actually understand what Python is… or do you just know its definition?🐍 Most people say: “Python is a high-level, interpreted language created by Guido van Rossum in 1991.” That’s not understanding. That’s memorization. Python is not just a language. Python is a layer of abstraction. ⚙️ When early languages like C were designed, they stayed very close to the machine. 💻 You had to think about memory, pointers, and low-level details. That’s why C is fast—because it sits close to hardware. But here’s the trade-off: Closer to hardware → more control, more complexity Higher abstraction → less control, more productivity Python was built to move you away from the machine and toward problem-solving. Someone already did the hard work: Memory management? Handled. Complex system interactions? Hidden. Syntax complexity? Reduced. So instead of thinking: “How does the computer execute this?” You think: “What logic solves this problem?” 🚀 That’s why Python is widely used in: Machine Learning Web Development Automation Data Analysis Not because it’s the fastest — it’s not. But, because it allows you to build faster and think more clearly. Final point: 🎯 Python didn’t become popular by accident. It became popular because it removes friction between your idea and implementation. #python #pythonprogramming #learnpython #coding #programming #machinelearning #deeplearning #datascience #artificialintelligence #ai #ml #softwareengineering #systemdesign #computerscience #codinglife #programminglogic

🐍 Why Python for Obsidian Intelligence? While building Obsidian Intelligence, I decided to go all-in with Python as the core development language. Not just because it’s popular—but because it fits perfectly for building LLM-based, multi-agent systems. Here’s how Python is being used in the system: • Data Processing Layer Handling job descriptions, resumes, and text cleaning using libraries like pandas and regex. • Embedding & Similarity Layer Using Python-based libraries (Sentence Transformers) to convert text into vector embeddings and compute semantic similarity. • RAG Pipeline Implementing Retrieval-Augmented Generation by integrating vector search (FAISS) with contextual data retrieval. • LLM Integration Using Python APIs to interact with LLMs for reasoning, scoring, and generating outputs (resume, cover letters, decisions). • Agent Orchestration Structuring the system into modular components (analyze → score → decide → act) using Python functions and workflow logic. • Automation & Workflow Control Python acts as the glue that connects all parts—from input processing to final decision-making. What I find interesting is how Python enables: from simple scripts → to building full intelligent systems with decision-making capability. Still building, but the foundation is getting stronger. #Python #AI #LLM #RAG #AIAgents #BuildInPublic

🔧 Building AI Agents from Scratch – Part 10: AI Agent Python Library Packaging is live! In this post, I explore how agents can be packaged and shared like any other Python library: ✨ From Scripts to Libraries – agents move beyond ad‑hoc scripts into structured, reusable packages. ✨ Packaging with setup.py / pyproject.toml – standard Python packaging ensures agents can be installed via pip. ✨ Wheel Files (.whl) – agents are compiled into distributable wheels, making installation fast and dependency‑safe. ✨ Distribution via Git – teams can version, share, and collaborate on agents across repositories. ✨ FastAPI Discovery Integration – packaged agents can register themselves automatically, enabling plug‑and‑play orchestration. This series continues to be based entirely on my work experience. It’s not about frameworks—it’s about learning the fundamentals and understanding what they’re built on. 👉 Read Part 10: https://lnkd.in/gAsxewjw If you’re curious about how packaging transforms agents into modular, reusable components, I’d love for you to follow along. #AI #Agents #Python #Packaging #AgenticAI #LearningByDoing

Day 12: Magic Methods & Data Protection in Python OOP 🐍⚙️ As I continue building my AI engineering foundation, today was all about taking complete control over custom objects—how they behave, how they interact, and how they protect their data. Here are the core engineering concepts I leveled up today: ✨ Magic Methods (Dunder Methods): Learned how to build fully custom data types from scratch by overriding core Python operators (using __add__, __str__, etc.). This is exactly how powerful ML libraries like NumPy define custom matrix and tensor operations! 🛡️ Encapsulation & Safety: In production, you can't leave data exposed. I practiced making variables "private" using double underscores (__) and built Getters and Setters to strictly control how data is accessed or modified, preventing unintended pipeline crashes. 🔗 Pass-by-Reference & Mutability: A huge 'Aha!' moment today. Custom objects in Python are mutable (just like Lists). If you pass an object into a function and modify it, the original object in memory is permanently changed. 📦 Collections of Objects: Scaled things up by storing multiple custom objects inside Lists and Dictionaries. This allows for clean iteration and bulk processing of complex data entities. #Python #MachineLearning #ArtificialIntelligence #DataEngineering #OOP #100DaysOfCode

Is Python finally getting a real competitor? For years, Python programming language has dominated everything from AI to backend to scripting — largely because of its simplicity, readability, and massive ecosystem But something interesting is happening… 👀 A new wave of languages and tools are emerging that challenge Python’s biggest weakness: 👉 Performance vs productivity trade-off The idea isn’t to “kill Python” — it’s to reimagine what a modern language should feel like: ✔️ As easy as Python ✔️ As fast as C/C++ ✔️ Built for AI-first workflows ✔️ Better developer ergonomics And honestly… this shift was inevitable. Python was designed in the late 80s to be fun and easy to use But today’s world demands: ⚡ Real-time AI systems ⚡ High-performance computing ⚡ Massive-scale data pipelines So the big question is: 👉 Will Python evolve fast enough? 👉 Or will the next-gen language take over the developer mindshare? 💡 My take: Python isn’t going anywhere. But the monopoly? That might be ending. We’re entering a multi-language era, where developers pick tools based on: Speed Scalability Developer experience And that’s actually a good thing. Because competition doesn’t kill ecosystems… 👉 It makes them better. 🔥 Curious to hear your thoughts: Do you think Python will still dominate in 5 years? #Python #Programming #AI #SoftwareDevelopment #TechTrends #Developers #Coding #MachineLearning #FutureOfWork #Innovation