Building Data Systems with Django and Python

Day 46/60 of #60DaysOfMiniProjects Built a Smart Study & Mood Tracker using Flask! Excited to share my latest project where I combined productivity tracking with a touch of intelligent suggestions Features: • Track daily study sessions with mood & notes • Smart suggestions based on mood and activity • Productivity score calculation • Daily streak tracking • Search, edit, and manage past sessions • Clean and simple user interface Tech Stack: Python | Flask | JSON | HTML/CSS This project helped me understand how small data insights can improve consistency and focus in daily routines. Would love your feedback and suggestions to improve it further! #Python #Flask #WebDevelopment #StudentProjects #Productivity #CodingJourney #OpenToLearn

Most FastAPI codebases look clean at first glance. Until you try to change something. I’ve noticed a pattern — a lot of complexity doesn’t come from the problem itself, but from where the logic lives. When routes start handling more than just request/response, things get harder to reason about. Lately, I’ve been keeping one constraint: Routes should stay thin. They handle the HTTP layer. All business logic moves to services. It’s a small shift, but it changes a lot: 1) Clearer separation of concerns 2) Easier testing 3) Fewer side effects when making changes Also started appreciating dependency injection more. Not as a framework feature, but as a way to keep things decoupled and predictable. Nothing groundbreaking here. But in a time where a lot of code is being generated faster than it’s being designed, maintainability comes down to how consistently we apply these basics — not whether we know them. Curious how others approach structuring FastAPI projects at scale. #FastAPI #BackendDevelopment #CleanCode #SoftwareEngineering #Python

When you start working with APIs in Python 🧪 one of the most common ways to see results is with Flask. Just like in a laboratory, you experiment with flasks and ampoules. Here, the Flask is your container. You pour in routes, logic, requests. You observe what comes out. Simple. Lightweight. Immediate feedback. No heavy setup. No complex structure at the beginning. Just you… testing ideas in real time. And that’s exactly why it works so well early on. Because before scaling, before architecture, before optimization… you need a place to experiment. Flask is that place. #Python #Flask #APIs #SoftwareEngineering #BackendDevelopment #DeveloperLife #ContinuousLearning #RotterdamTech

📝 Why I deliberately write "boring" code: Fancy code is impressive. Boring code is reliable. What boring code looks like: ✅ Clear variable names (customer_count not cc) ✅ Small functions that do one thing ✅ Comments that explain WHY, not WHAT ✅ Consistent formatting ✅ Error handling for edge cases Who benefits? → Future me (6 months from now, I won't remember) → My teammates (they can actually read it) → Production (less surprises at 2 AM) Clever code makes you feel smart. Boring code makes you effective. Which do you prefer to maintain? #CodeQuality #Python #DataEngineering #CleanCode

This week I spent 2 hours debugging a pipeline that broke because of a subtle mutable default argument. Last week I finished DataCamp's "Intermediate Python for Developers" - and guess what chapter was in there. Funny how that works sometimes. A few takeaways that'll stick with me: • Mutable defaults are a trap, even for people who "know Python" • Decorators aren't magic - they're just functions returning functions (but the mental model matters) • Comprehensions > loops, until they don't fit on one screen anymore Working with Python daily on dbt models, and data transformations, it's easy to get comfortable in a narrow slice of the language. Stepping back to revisit the fundamentals consistently makes my production code cleaner. What's your approach - do you block time for structured learning, or learn purely on the job? #Python #DataEngineering #LearningInPublic

💡 Solved “Remove Nodes From Linked List” Using a Stack — But Can It Be Better? Today I worked on the Remove Nodes From Linked List problem, and it was a fun one. 🔍 Problem Statement: Remove every node that has a node with a greater value somewhere to its right. Example: 5 → 2 → 13 → 3 → 8 ✅ Output: 13 → 8 ⚙️ My Approach: Stack I used a stack to maintain values in decreasing order: Traverse the linked list If current value is greater than stack top → pop smaller values Push current value Rebuild the linked list from remaining values ✨ Why it works: The stack keeps only values that are valid survivors after checking future nodes. 💻 🐍 Python code👍 🤩 👍: https://lnkd.in/gcAaycjY 📊 Complexity: Time: O(n) Space: O(n) 🧠 Takeaway: Sometimes using extra space makes the logic cleaner and easier to implement. But now I’m curious... 👉 Can you give the best solution for this problem? (Perhaps O(n) time with O(1) extra space?) Would love to see different approaches from the community 👇 #LinkedList #DataStructures #Algorithms #Python #CodingInterview #ProblemSolving #LeetCode Rajan Arora

I spent 3 hours debugging code that worked perfectly fine 😶🌫️😶🌫️😶🌫️ The problem? I was convinced something was broken and kept "fixing" things that didn't need fixing.... Turns out the API was just slow. I needed to wait 30 seconds instead of 10. This happened while building my meeting summarizer — a Python app that transcribes recordings and sends email reports via Claude. Here's what nobody tells you about learning to code in your late 30s: Your business brain works against you. 12 years of "moving fast" as a PM doesn't translate to code. I kept jumping to solutions before diagnosing the actual problem. The fix was embarrassingly simple: add a longer timeout!!!!! The real lesson? Slow down. Actually read the error message. Sit with the confusion a bit longer. What's a lesson that took you way too long to learn? #AIlearning #CareerTransition #Python #BuildingInPublic

🚀 Just solved the Power of Two problem on LeetCode — and here’s a quick look at my approach 👇 Instead of jumping straight into bit manipulation, I focused on a simple iterative logic: 🔹 Start from 2 🔹 Keep multiplying by 2 🔹 Check if we reach the given number 🔹 If yes → it’s a power of two ✅ 🔹 If we overshoot → it’s not ❌ 💡 This approach emphasizes clarity over cleverness — building intuition step-by-step before optimizing further. While there are more optimized solutions (like bit tricks), I believe: 👉 Strong fundamentals > premature optimization 📊 Result: ✔️ 100% runtime efficiency ✔️ Clean and readable logic Always aiming to improve not just what I solve, but how I think 💭 #LeetCode #DSA #ProblemSolving #Python #CodingJourney #100DaysOfCode #WomenInTech #FutureEngineer #TechGrowth #Consistency #LearnInPublic #CodingLife #SoftwareEngineering #DeveloperMindset

We had a Python service where every HTTP request took exactly 15 seconds. Never 12, never 18. When every operation lands on the same number, you're not measuring the operation, you're measuring a timeout. py-spy dump on a worker showed the main Python thread idle, the executor thread idle, and a hidden tokio-rt-worker thread parked on a futex. That third thread was the clue. The HTTP client (primp, Rust + tokio under the hood) holds a tokio runtime that does not survive fork(). Anything using a prefork model, including Celery, gunicorn --preload, and multiprocessing with the fork start method, produces children with a broken runtime that hangs on every request until an outer timeout fires. The fix was a one-line swap to curl_cffi (libcurl-based, fork-safe); end-to-end latency went from 15 s to 0.5 s with zero behavior change. The takeaway I keep relearning: mixing native runtimes with fork() is a footgun, and a too-clean number in your metrics is a clue, not a feature. #Python #Debugging #SoftwareEngineering

I kept wishing I had one place that brought everything together for me to stay structured. So I built one. VIGIL is a personal system that brings tasks, habits, goals, and routines into one place. The focus is simple: track execution, stay consistent, and understand patterns over time. It includes: • tasks and checklists • habits and streak tracking • goals and routines • lightweight analytics for trends and insights Built to be simple, easy to use, and something I’d actually stick with daily. Tech stack: • Python • Streamlit • Supabase Still a work in progress with some rough edges, but already useful in my day-to-day. GitHub: https://lnkd.in/gNGA9akE #Python #Streamlit #DataScience #Supabase #BuildInPublic