4 Common AI Agent Mistakes to Avoid in Python Development

6 Python libraries that quietly replaced half my toolkit this year: Polars — I switched from pandas for anything over 50k rows. 10-50x faster. The learning curve is real but worth it. DuckDB — SQL on local files without spinning up a database. I use it for ad-hoc analysis almost daily now. Instructor — Forces LLMs to return structured Pydantic objects instead of raw text. Solved the “unpredictable LLM output” problem for every pipeline I’ve built this year. LiteLLM — One API for OpenAI, Anthropic, Mistral, Llama. Switch providers by changing one string. Built-in cost tracking. Pydantic — If you’re still passing raw dicts between functions, please stop. Your future self will thank you. LanceDB — Local vector database. No Docker, no server. Perfect for RAG prototypes that might actually go to production. The pattern: every tool I kept this year is something that removed friction, not something that added features. Which of these haven’t you tried yet? #Python #DataScience #GenAI

I just published my first open source Python package — and I want to share what I built and why. It's called paner — a terminal-based PDF analyzer powered by AI. The idea is simple: instead of uploading your documents to some cloud service and hoping they stay private, paner runs entirely on your local machine. You drop a PDF into your terminal, ask questions about it conversationally, and get intelligent answers — all without your files ever leaving your computer. Under the hood it uses: → Groq - for fast AI responses → ChromaDB for local vector storage → Sentence Transformers for embeddings → Python cmd module for the interactive CLI experience This project taught me a lot about RAG (Retrieval Augmented Generation), vector databases, Python packaging, and shipping a real product end to end. You can install it right now with: pip install paner-cli And the full source code is on GitHub: https://lnkd.in/emZZAHvt This is just the beginning. If you try it out, I'd love your feedback. #Python #OpenSource #AI #RAG #BuildingInPublic #SoftwareDevelopment #MachineLearning

Most LLM agents struggle with limited context windows and can’t handle large documents effectively. I built an agentic RAG assistant for large PDF Q&A that overcomes this by retrieving only the most relevant context from large PDFs before generating answers. ⚙️ Tech: Python, LangChain, OpenAI Embeddings, Qdrant 🔹 Features: Handles large PDFs via chunking + vector search Semantic retrieval for precise context Hallucination-resistant responses 🔗 GitHub: https://lnkd.in/gZd3wHgP #AI #RAG #LangChain #OpenAI

1 month of Python nearly broke me. Not because it was hard. Because for a week nothing felt like progress. You know that phase? Where you've moved past  the beginner wins but you're not yet doing  Anything impressive? That's the valley. And almost everyone quits there. I was learning NumPy—arrays, indexing, slicing,  boolean masking. Dry stuff. No obvious output.  Just concepts building on concepts. Then I connected it to something real. I took port congestion data from Apapa.  Ran np.mean() and np.max(). Boolean masking  to flag HIGH RISK weeks. Suddenly it wasn't NumPy practice.  It was a supply chain analysis. The valley didn't disappear.  But I found the path through it. 1 month in. Still here. Still building. If you're in the valley right now, That's not a sign to stop.  That's the sign you're close to the other side. #Python #NumPy #LearningInPublic #SupplyChain #Nigeria #GLOBAL

Day 6 of my Python learning journey: Focusing on writing code that's not just functional, but also dependable. Highlights from today: - Warmed up by practicing iterators and generators. - Solved the "First Non-Repeating Character" problem using a clean two-pass frequency approach with O(n) complexity. - Merged two sorted arrays efficiently through the two-pointer technique (O(n + m))—avoiding any redundant sorting. - Developed a FastAPI endpoint for the above problem, complete with schema validation. Observations: Initially, I attempted a one-pass solution for the non-repeating character problem, but it ended up feeling convoluted. Switching to a two-pass strategy made the solution much more straightforward and maintainable. Current focus areas: - Maintaining input order integrity. - Performing input validation early on. - Keeping logic as simple as possible. - Thoroughly testing edge cases beforehand. Major takeaway: Define inputs/outputs upfront → validate data early → then prioritize optimization. Starting to grasp how even minor design decisions can significantly impact code quality. GitHub link: https://lnkd.in/gGPw8_js #Python #FastAPI #ProblemSolving #SoftwareEngineering #CleanCode #LearningInPublic #OOP #Testing

Week 1 of my SWE → AI transition. Here's what actually surprised me. I've been writing production code for years. Clean functions, tests, CI pipelines. So I expected data analysis to feel familiar. It didn't. Not because the code is harder - it isn't. But because the thinking is completely different. In application code, I write functions that transform one object into another. In data work, I write transformations across millions of rows simultaneously. Loops are a code smell. Shape mismatches are your runtime errors. And 'does this look right' is a legitimate debugging strategy. This week I analysed a real dataset using only Python's standard library - no pandas, no NumPy. Not because those tools are bad. But because I wanted to understand what they're actually doing for me before I let them do it. GitHub: https://lnkd.in/eeK8H4Sg #Python #DataScience #BuildingInPublic #Chapter 1

I've just shared the next step in my MLIR journey: How do you handle if-else logic in MLIR and call it from Python? I just posted Part 5 of my MLIR Track! This one covers: ✅ SCFDialect implementation in C++ 😀 ✅ Building .so libraries for Python ctypes Full guide here: https://lnkd.in/gcxAFHbw #LearningInPublic #Compiler #MLIR #SCF #if #C++ #HighPerformanceComputing

💡 A Simple Recursive Way to Check Power of Two Today I revisited a basic but interesting problem: Check if a number is a power of two. Instead of jumping straight to bit manipulation, I tried solving it using recursion — and it turned out to be a neat approach. 🔁 Idea: A number is a power of 2 if: It keeps dividing cleanly by 2 And eventually becomes 1 ⚙️ Approach: If n == 1 → ✅ True If n <= 0 or n is odd → ❌ False Otherwise → recursively check n // 2 ✨ What I liked about this: Very intuitive Mirrors the mathematical definition Easy to understand and implement 📊 Complexity: Time: O(log n) Space: O(log n) due to recursion stack 🧠 Takeaway: Sometimes, going back to the mathematical intuition behind a problem leads to the simplest solution. Of course, there’s also a more optimized bit manipulation trick: 👉 n & (n - 1) == 0 But recursion helps in building strong fundamentals. python code https://lnkd.in/giwyBUiQ How would you approach this — recursion or bit manipulation? 👇 #Algorithms #Recursion #Python #CodingInterview #ProblemSolving #LeetCode Rajan Arora

My first trained model sat in a Jupyter notebook for two weeks. I had no idea how to let anyone else use it. That is the gap between knowing ML and doing ML engineering. Knowing how to serve a model is a different skill from knowing how to train one. Here is how to go from a saved model file to a live REST API in under 30 lines of Python. The key insight that took me too long to learn: never load the model inside the endpoint function. Load it once on startup. Every call after that is instant. FastAPI also generates an interactive docs page automatically at /docs. Zero extra work. Point anyone at the URL and they can test your API from the browser. Four things to add before real traffic: input validation beyond types, request logging, structured error handling, and a /health endpoint for your load balancer. Swipe through for the complete code. What was your first production ML deployment? Flask, FastAPI, something else? #Python #FastAPI #MLOps #MachineLearning

🚀 Python Series – Day 19: Polymorphism (One Name, Many Forms!) Yesterday, we learned Inheritance 🔁 Today, let’s understand another powerful OOP concept — 👉 Polymorphism 🧠 What is Polymorphism? 👉 The word Polymorphism means: 📌 Poly = Many 📌 Morph = Forms So, One method / function behaves differently in different situations 🔹 Real-Life Example Think of the word Run 🏃 Human runs 🚗 Car runs 💻 Software runs 👉 Same word run, different meanings. That is Polymorphism 🔥 💻 Example 1: Same Method, Different Classes class Dog: def sound(self): print("Dog barks") class Cat: def sound(self): print("Cat meows") for animal in (Dog(), Cat()): animal.sound() Output: Dog barks Cat meows 🔹 Example 2: Built-in Polymorphism print(len("Python")) print(len([1,2,3,4])) Output: 6 4 👉 Same len() function works for string and list. 🎯 Why Polymorphism is Important? ✔️ Cleaner code ✔️ Flexible programs ✔️ Easy to extend features ✔️ Used in real-world software development Pro Tip 👉 Write generic code that works with many object types. 🔥 One-Line Summary 👉 Polymorphism = Same method name, different behavior 📌 Tomorrow: Encapsulation (Protect Your Data Like a Pro!) Follow me to master Python step-by-step 🚀 #Python #Coding #Programming #OOP #DataScience #LearnPython #100DaysOfCode #Tech #MustaqeemSiddiqui