Python with AI: Build Real-World Apps

A year ago, learning Python meant writing scripts and building APIs. Today, it feels like I’m learning how to build systems that can think. That shift is real. With Agentic AI, Python is no longer just about: • functions • classes • frameworks It’s about creating workflows where: • an agent understands a problem • decides what to do next • calls APIs or tools • adapts based on results ⸻ I recently started exploring this space, and one thing stood out: 👉 You’re not just coding anymore 👉 You’re designing behavior ⸻ There are moments where: You write a piece of code… and the system responds in a way you didn’t explicitly program. That’s powerful. And honestly, a bit uncomfortable too. ⸻ Because now the challenge is not just: “How do I build this?” It becomes: • How do I guide this system? • How do I control its decisions? • How do I trust its output? ⸻ As someone working in integration and architecture, this feels like a major shift. We’re moving from: 👉 predictable systems to 👉 adaptive systems ⸻ And Python is right at the center of this change. ⸻ Curious — Are you still learning Python the traditional way, or exploring it through AI and agentic workflows? ⸻ #AgenticAI #Python #AI #SoftwareArchitecture #TechLearning #FutureOfTech

I have been incosistent with my learning and today I am holding myself accountable to the time I have lost, and what I shall do. I Day 1 of my AI/ML Journey. Today, I discovered python, or for that matter any programming language is not about writing code but also, reading it. The real skill is being able to look at a codebase not of your own and deciphering bugs, faults, and the code written with the mentality "If is works, dont fix it". I discovered that I have a lot of horizontal skills but no vertical integration of anything deeply, so I started today. I revisited python in detail but a little differently. I looked at from a perspective of what can I break, then learn why it broke. Do you know how python lags behind when considered from an ML production perspective and what are its fixes ?

Part 2: My Journey Transitioning to AI Hello, I'm Kiran Gundra, sharing my journey of transitioning to AI. I have 10 years of experience in the software industry, and learning Python fundamentals was relatively easy for me. I understood the editor, syntax, and basics. Moving forward on my journey is Machine Learning (ML). I don't know what ML is or how much I need to understand it to progress. For those following my journey and looking to get started: If you have a software background like me, focus on learning Python so you can understand the code and run programs. If you have no coding knowledge, I suggest taking a beginner Python course, either online or on YouTube. I'm not providing specific links, as you can easily find great resources. The key is to get comfortable with Python programming before diving into Machine Learning. Having a strong foundation in the basics will make your ML journey much smoother. Let me know if you have any other questions as I continue sharing my AI transition experience! #MachineLearning #Python #AIJourney #CareerTransition

🚀 10 Python Libraries That Make AI Agents Work 🤖 Building AI agents is exciting, but turning prototypes into reliable systems requires more than just intelligent models. Failures often stem from missing infrastructure, not the model itself. That’s where Python’s ecosystem shines! 🌟 Here are 10 essential Python libraries that help stabilize and scale AI agents: 1️⃣ LiteLLM: Simplifies interaction with multiple model providers via a single interface. 2️⃣ Instructor: Ensures structured outputs with schema-based responses using Pydantic. 3️⃣ Tenacity: Adds retry logic for handling temporary API failures. 4️⃣ Logfire: Provides tracing and searchable logs for easier debugging. 5️⃣ DiskCache: Enables local caching to reduce repeated expensive calls. 6️⃣ Tiktoken: Manages token awareness for context windows and cost optimization. 7️⃣ Rich: Enhances terminal output for better debugging and visualization. 8️⃣ Watchfiles: Speeds up development with hot reload workflows. 9️⃣ Guardrails: Validates agent outputs for safety and reliability. 🔟 Ragas/TruLens: Offers metrics for evaluating agent quality and performance. These libraries form the backbone of dependable AI systems, transforming experimental prototypes into production-ready solutions. 💡 Let’s shift our mindset: AI agents aren’t just prompts wrapped around models—they’re layered systems supported by robust infrastructure. Python makes this approach practical, which is why it’s the go-to language for building serious AI agents. 🛠️ What are your favorite Python libraries for AI development? Let’s discuss! 👇 #AI #Python #MachineLearning #ArtificialIntelligence #AIAgents #TechInnovation #DataScience #Programming

Python was the first programming language I learned, but for me it fell by the wayside years ago. I’m now re-learning it specifically because it seems to be a required skill in the new generation of “AI” companies. So - genuine question for technical folks building AI companies: If your backend is just routing prompts to Anthropic or OpenAI — you're not doing ML. You're doing API calls. So why Python? If you're not training models, if you're not running local inference, you have no NumPy pipelines or CUDA kernels…why on earth Python? Golang gives you compiled performance, tiny binaries, and dead-simple concurrency. Node/TypeScript unifies your entire engineering team under one language and toolchain. There are plenty of other options. Python made sense when once upon a time but now? Not so sure. If your company adds value while still being essentially an AI passthrough - is your stack a technical decision? 

For experienced developers, AI tools like JetBrains AI and PyCharm's code completion features are workflow game-changers. But for Python beginners, are these powerful tools creating a dependency that hinders real skill acquisition? We explore the paradox of features like local code completion and next edit suggestions, and discuss why stepping back from AI assistance might be the most effective way to learn Python. Great learning often comes from identifying – and fixing – mistakes. Read our take and find out how to customize your PyCharm experience for better learning: https://jb.gg/90ime3 #PythonLearning #DeveloperSkills #TechEducation #AIinCoding

🚀 Day 1: Python Basics for Gen AI Revision – The Foundation! Stepping into my "Python – Gen AI Revision" journey today with a sharp focus: Mastering the core fundamentals required for Generative AI development and aiming for a role in an MNC within 90 days. It’s easy to get excited about LLMs and Diffusion models, but without a rock-solid Python foundation, those complex structures can't stand. That's why Day 1 is dedicated to the core. 🧠 What I Re-covered/Focused On Today: PEP 8 Standards & Syntax: Emphasizing readable, professional code structure from the start. Essential Data Types & Flow Control: Revisiting loops, if/else logic, and efficient variable management. Advanced Fundamentals: Getting hands-on practice with lambda functions, list comprehensions, and proper docstring usage—critical for real-world development. I’ve compiled all concepts, code examples, and best-practice notes into a comprehensive Google Colab Notebook and pushed it to my new repository: python-genai-journey. This isn't just theory; it’s about preparing myself to write industry-standard Python for the future of AI. 💻 Check my progress & the code here: 🔗 https://lnkd.in/gUfc6Ky6 One day down, many more to go. Follow along as I build my way to a Gen AI career! #Python #GenAI #GenerativeAI #100DaysOfCode #AIDevelopment #TechJourney #MNCGoal #RevisionSeries

Tell your AI that you are beginner when learning Python! I recently asked AI to generate some simple code to reverse two virtual spreadsheet columns called ‘Name’ and ‘Type’. Instead of simply suggesting code in which the column list [‘Name’, ‘Type] is replaced by [‘Type’, ‘Name’], AI suggested a complex indexing trick that looks like [::-1]. Succinct coding, to be sure, but I could not decipher it without additional prompts! In another case, my AI provided some sample code in which a variable was defined after that variable was used! The Horror! When I asked why it made such a fundamental mistake, the AI complimented me on my “good eye” for catching the error and that it did not necessarily provide code in execution order. So, if you are just learning Python, always start your session with a good prompt to set the stage such as the following: "I am a total beginner learning Python. Please follow these rules for ALL Python code you write for me: 1. Write code in execution order. 2. Break every task into small, discrete steps. 3. Use simple, obvious variable names that describe what they contain 4. Add plenty of comments explaining what it happening in plain English 5. Avoid shortcuts, clever one-liners, or condensed syntax that experienced coders use 6. If there are multiple ways to do something, choose the most readable one, not the most efficient one 7. Before showing me code, double-check it runs in the correct order from top to bottom" For more tips, consider my new book "Automate Excel with Python". My publisher @No Starch Press is offering a free review chapter in case you were interested: https://lnkd.in/eS-WAVyV Good luck and good coding! #Excel, #Python, #pandas, #dataframes,#Productivity, #DataAnalysis, #Automation

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜 Is learning Python "easier" in 2026? Yes. But it’s also different. 🐍✨ For a beginner like me, AI isn't just a "cheat code"—it’s a 24/7 personal tutor. Here is how AI is fundamentally changing the way we learn Python today: 🧠 𝗧𝗵𝗲 𝗦𝗼𝗰𝗿𝗮𝘁𝗶𝗰 𝗧𝘂𝘁𝗼𝗿: Instead of just giving the answer, modern AI assistants (like the latest Gemini or Socratic AI tutors) now ask: "I see a syntax error on line 5—what do you think is missing in your function call?" It forces me to think, not just copy. 🔍 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 "𝗕𝗹𝗮𝗰𝗸 𝗕𝗼𝘅": When I hit a complex concept like 𝗗𝗲𝗰𝗼𝗿𝗮𝘁𝗼𝗿𝘀 or 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻, I can ask AI to "Explain this like I'm 5 years old using a LEGO analogy." Turning abstract code into relatable stories is a learning game-changer. 🛠️ 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗘𝗮𝘀𝗲: Tools like Google Antigravity or browser-based AI labs have removed the "setup headache." I can focus on logic immediately without getting stuck on path variables or environment installs. 𝗠𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿'𝘀 𝗥𝘂𝗹𝗲 𝗳𝗼𝗿 𝟮𝟬𝟮𝟲: Use AI to explain the "𝗪𝗵𝘆", but always write the "𝗛𝗼𝘄" yourself. Master the logic first, and the tools will follow. 𝗠𝘆 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆:💡 I use AI to understand the logic behind any concept of Python, and it saves me hours of confusion. Instead of just getting an answer, I get a clear explanation that helps me move forward with confidence. 𝘔𝘢𝘴𝘵𝘦𝘳 𝘵𝘩𝘦 𝘭𝘰𝘨𝘪𝘤 𝘧𝘪𝘳𝘴𝘵, 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘵𝘰𝘰𝘭𝘴 𝘸𝘪𝘭𝘭 𝘧𝘰𝘭𝘭𝘰𝘸. 🚀 In the modern tech stack, Python serves as the critical engine for back-end logic, data processing, and AI integration. By mastering Python's core principles first, a developer isn't just writing scripts; they are building the architectural foundation required for the complex, intelligent systems found in a professional Web Dev Lab. The logic learned today is the infrastructure for the web applications of tomorrow. #PythonForBeginners #AIinEducation #LearningToCode #WomenInTech #Python2026 #FutureOfLearning #PythonLearning #AIinEducation #WomenInTech

If you’re learning Python for AI… there’s a high chance you’ve felt this: Confused. Overwhelmed. Jumping between tutorials. But still not building anything real. That’s exactly where Dave Ebbelaar stands out. He doesn’t just teach Python. He teaches how to think like a builder in AI. No fluff. No overcomplication. Just clean, structured learning that actually helps you move forward. What I personally like about his approach: → He breaks complex concepts into simple steps → Focuses on projects, not just theory → Helps you understand the “why”, not just the “how” Because in AI and Data… Knowing syntax won’t get you paid. Building things will. If you’re a quiet learner trying to enter AI or Data, you don’t need 50 courses. You need 1–2 solid mentors and the discipline to execute. Dave can be one of them. Key Takeaway: Don’t just consume content. Follow people who help you build clarity + capability. Have you come across someone who genuinely simplified AI or Python for you? Drop their name below 👇 Let’s help each other learn smarter.