Debugging FastAPI with Logs and Syntax Checks

Docker made me rethink how backend applications should be built and run. Until now, my projects worked because my system was configured correctly. That’s fragile — and not how real backend systems operate. So I Dockerized a Flask application to understand how applications are packaged, isolated, and run consistently across environments. What I worked on: - Writing a clean, cache-efficient Dockerfile - Using python:slim images to avoid unnecessary bloat - Proper port mapping to expose services - Managing build context with .dockerignore - Understanding the difference between images, containers, and build cache Key takeaway: Docker isn’t about convenience commands. It’s about controlling the runtime environment so the application behaves the same everywhere. Building with this mindset feels much closer to how backend systems are actually deployed. On to the next layer of the stack. #Docker #BackendDevelopment #Flask #Python #LearningByBuilding

🚀 Mini Project: Incident → Runbook using Vector Database Built a small system where vague incidents like 👉 “Jenkins issue” are matched to the right runbook using a Vector Database (FAISS) — not keyword search. 🧠 How it works Incident text → Embeddings → Vector DB (FAISS) → Best runbook ✅ Semantic intent matching ✅ Persistent vector DB ✅ Confidence-based runbook suggestion 🔗 GitHub: https://lnkd.in/gKqcreQY Python · LangChain · FAISS · OpenAI Embeddings Learning how Vector DBs power RAG & AIOps systems in real production scenarios 🔥 #VectorDatabase #FAISS #DevOps #MLOps #LangChain #RAG #AIOps #LearningInPublic

🚀 The Happy Path: The "Magic" of Deleting Code There is no cleaner code than the code that no longer exists. We often think "progress" means adding more: more features, more abstractions, more lines. But one of the most pleasant "wins" in a developer's day is finding a way to solve a problem by removing complexity. Replacing a custom, buggy function with a single built-in library call? Pure bliss. Deleting a 100-line workaround because a dependency finally updated? Chef’s kiss. 👨🍳 It’s lighter, it’s faster, and it’s one less thing for your future self to debug. What’s the most satisfying chunk of code you’ve ever been able to delete? Let’s hear your "deletion wins" in the comments! 👇 #TheHappyPath #SoftwareEngineering #CleanCode #ProgrammingLife #Python #Django

Day 47/100 – #100DaysOfLeetCode 🚀 🧩 Problem: LeetCode 191 - Number of 1 Bits (Easy) 🧠 Approach 1: Using Brian Kernighan’s Algorithm. 💻 Solution: class Solution:   def hammingWeight(self, n: int) -> int:     count = 0     while n:       n &= (n - 1)        count += 1     return count ⏱ Time | Space: O(k) | O(1) 📌 Key Takeaway: Using n & (n - 1) efficiently removes one set bit at a time, making it faster than checking every bit individually. 🧠 Approach 2: Python’s built-in binary conversion makes counting set bits simple 💻 Solution: class Solution:   def hammingWeight(self, n: int) -> int:     return bin(n).count('1') #leetcode #dsa #development #problemSolving #CodingChallenge

Just discovered "Pydantic" today and it's a game-changer! If you're building APIs in Python or dealing with data management, this library makes life so much easier. What I learned: -> Automatic data validation - no more manual checks -> Works perfectly with FastAPI -> Clean settings and config management You get validation, error handling, and type safety automatically. If you work with APIs(FastAPI) or external data, definitely check it out. Perfect Source: Chai Aur Code-Pydantic Crash Course(YouTube) #Python #Pydantic #API

🚀 LeetCode Practice: Problem #145 – Binary Tree Postorder Traversal I recently solved LeetCode 145, a classic tree traversal problem that strengthens understanding of Depth-First Search (DFS) and recursion. 📌 Problem Statement Given the root of a binary tree, return the postorder traversal of its nodes’ values. Postorder Traversal Order: 👉 Left → Right → Root 🧠 Technique Used: Depth-First Search (Recursive Approach) Approach Explanation: If the current node is None, return. Recursively traverse the left subtree. Recursively traverse the right subtree. Visit (append) the root node value at the end. This traversal ensures that child nodes are processed before their parent node. Time Complexity: O(n) Space Complexity: O(h) (Where h is the height of the tree due to recursion stack) #LeetCode #Python #DSA #ValidAnagram #ProblemSolving #Algorithms #DataStructures #CodingPractice #100DaysOfCode #InterviewPreparation #SoftwareEngineering #DeveloperJourney #TechCommunity #LearningByDoing

Recently, I was working on a problem that required dynamically constructing a string. My initial implementation was straightforward and functionally correct. At first glance, it seemed perfectly acceptable. However, upon reviewing the logic more carefully, I revisited how Python handles strings internally. Since strings in Python are immutable, each concatenation inside a loop creates a new string object. This means that with every iteration, memory is reallocated and the existing content is copied over. As input size increases, this results in repeated allocations and copying — leading to unnecessary overhead and potential quadratic time complexity. While this may not be noticeable for small inputs, it becomes increasingly inefficient in production environments where code runs frequently or processes large datasets. To optimize the solution, I refactored the implementation to accumulate values in a list and join them at the end. This approach avoids repeated string creation and achieves linear time complexity, improving both performance and memory efficiency. It was a small refactor, but a meaningful one. Moments like this are a good reminder that understanding language internals — even for simple operations — can significantly impact the quality and efficiency of the code we write. #Python3 #Performance #CleanCode #SoftwareEngineering #Optimization

🚀 My Python Playground is back, and it’s better than ever! 🐍 They say you can’t keep a good project down. After a short hiatus (and a little "adventure" migrating the backend after Shuttle.dev shut down), I’ve spent the last few days working with AI to self-host the infrastructure. The AsyncMove Python Playground is officially live with a working URL shortener! 🛠️✨ If you need a fast, browser-based Playground to test ideas, teach, or share code, this is built for you. Here’s what’s under the hood: ⚡ Ruff Linting: Lightning-fast linting to catch bugs instantly. 🧪 Pytest Support: Built-in testing to ensure your logic is bulletproof. 📦 Package Management: Install and import Python packages on the fly. 🔗 Short URLs: Share your entire workspace with one clean, portable link. 💅 Auto-Formatting: Keep your code PEP 8 compliant with a single click. 📖 README.md: Annotate your snippets with full Markdown support. 💯 Type hints and Autocomplete: Hover over your codes to see the docstrings. It feels great to have this back in the wild. I’d love for you to give it a spin and share a snippet! Link in the Comments section. #Python #BuildInPublic #OpenSource #Coding #PythonProgramming #WebDev #SoftwareEngineering

Docker for AI Agents is officially over.. Pydantic just dropped Monty. It's a python interpreter written in rust that lets agents run code safely in microseconds. no containers. no sandboxes. no latency. 100% open source. LINK: github.com/pydantic/monty

“𝗔 𝗴𝗼𝗼𝗱 𝗔𝗣𝗜 𝗺𝗲𝗮𝗻𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗼𝗼𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀.” When we asked Jonathan Mercier-Ganady why PyO3 stood out to him, this was the real takeaway 👇 It wasn’t just about performance. Or even Rust ↔ Python interoperability. 𝗜𝘁 𝘄𝗮𝘀 𝗮𝗯𝗼𝘂𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗱𝗼𝗻𝗲 𝗿𝗶𝗴𝗵𝘁. He’d worked on a project where most of the core logic lived in Rust, but some pieces still needed to run in Python. PyO3 was the bridge, and unlike many “glue” tools between languages, it didn’t fight back. What made the difference? 👉 A clean, well-thought-out API 👉 Clear documentation 👉 And crucially: real examples that make learning fast Because even the best-designed API slows you down if you’re left guessing how to use it in practice. Mixing languages is rarely trivial, especially when you’re dealing with Python’s garbage collection alongside Rust’s ownership model. Tools like PyO3 matter because they smooth over those sharp edges and let engineers focus on shipping, not wrestling abstractions. It’s a good reminder that developer experience isn’t a “nice to have”. It’s the difference between: 👉 “This looks powerful” and 👉 “I can actually use this tomorrow.” You can watch the full interview here: https://lnkd.in/e4ab5Sgx If you’ve worked across languages, what’s the best integration tooling you’ve used? #RustLang #Python #PyO3 #OpenSource #DevExperience #EngineeringCulture