Django Boilerplate for ML Projects

I once spent three days trying to optimize a high-concurrency data pipeline in Django, only to realize I was fighting the framework’s architecture, not the problem. Last week, on a client project involving real-time sensor data, we hit a wall where Django’s ORM and sync nature couldn't keep up with the throughput requirements. The lesson? Pick your Python weapon based on the job, not just what you know best. Django is unbeatable for complex admin panels, strict schema management, and rapid prototyping. It gives you the "batteries included" safety net that lets you ship features instead of building boilerplate. FastAPI, on the other hand, is for when you need to squeeze out every drop of performance. Its asynchronous nature is a massive win for I/O-bound tasks and heavy WebSocket integration. If you’re building a CRUD-heavy enterprise dashboard, stick with Django. If you’re building a high-scale microservice that needs to handle thousands of concurrent requests, move to FastAPI. Don't force a monolith into a microservice’s shoes. What’s the one project where you swapped backends midway because the first choice didn't scale? #Python #SoftwareEngineering #Django #FastAPI #SystemDesign

Moving from the flexibility of Flask to the "Batteries-Included" power of Django! 🐍🔥 After spending significant time building with Flask, where I enjoyed the "build-it-from-scratch" approach, I decided to dive deep into Django today to see how it handles large-scale architectures. The transition is eye-opening! Here’s what I learned today while building a User Management System: ✅ The Architecture Shift: In Flask, I was used to manual setups for everything. Django’s "Batteries-Included" philosophy (like the built-in User model and Admin panel) is a massive time-saver for rapid development. ✅ From SQL/Manual JSON to Django ORM: I moved away from manual dictionary mapping to using Django’s ORM for JsonResponse. It’s interesting to see how User.objects.all() simplifies data retrieval. ✅ API-First Thinking: I bridged the gap between Backend and Frontend using the Fetch API. Instead of standard page redirects, I built a system where my Django backend serves JSON, and JavaScript handles the UI dynamically via Popups (Modals). ✅ The "Nickname" Logic: One thing I loved? Django’s URL names. In Flask, I’d often hardcode paths, but in Django, using name='user_list_link' makes the code so much more maintainable. The Verdict: Flask taught me how things work under the hood. Django is now showing me how to scale those concepts efficiently. #Python #Django #Flask #WebDevelopment #Backend #CodingJourney #SoftwareEngineering #LearningInPublic #SaaS

Claude Code vs GitHub Copilot vs Cursor — A Backend Developer’s Honest Take I’ve spent the last several months using these three tools seriously. Not toy projects — real production systems, PostgreSQL databases, Django REST APIs. Here’s my actual verdict: GitHub Copilot Best for: Day-to-day coding inside VS Code/PyCharm Strength: Inline autocomplete is still the smoothest in class Limitation: Doesn’t deeply understand your whole project structure Verdict: ✅ Use it. It’s the floor, not the ceiling. Cursor Best for: Large refactors and cross-file changes Strength: Codebase-wide context. It knows how your models relate to your serializers to your views. Limitation: Slightly steeper learning curve. Takes time to get prompts right. Verdict: ✅ Essential if you work on complex, multi-app Django projects. Claude Code (via terminal) Best for: Architecture decisions, debugging hard problems, system design Strength: Longest context window. Best at reasoning about WHY code is broken, not just what’s broken. Limitation: More expensive on tokens for routine tasks Verdict: ✅ My go-to for anything requiring real reasoning depth. My current stack: Copilot for flow → Cursor for refactoring → Claude for hard problems According to March 2026 rankings, Claude Opus 4.6 is now rated #1 for technical tasks with 75.6% SWE-bench score. For backend engineers, this matters — it means more accurate architecture suggestions and fewer hallucinations on complex Django patterns. The answer isn’t one tool. It’s knowing which tool to reach for. Which is in your daily stack right now? #GitHubCopilot #CursorAI #ClaudeCode #DjangoDevs #PythonBackend #AITools #DeveloperTools

🚀 𝐃𝐚𝐲 𝟏: 𝐃𝐢𝐯𝐢𝐧𝐠 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐒𝐩𝐞𝐞𝐝 𝐨𝐟 𝐅𝐚𝐬𝐭𝐀𝐏𝐈! ⚡ After building robust applications with Django and DRF, I’ve decided to level up my backend game. Today marks Day 1 of my journey into FastAPI, and I’m already blown away by its efficiency! Here’s a breakdown of what I explored today: 🔍 𝙒𝙝𝙖𝙩 𝙞𝙨 𝙁𝙖𝙨𝙩𝘼𝙋𝙄? It’s a modern, high-performance web framework for building APIs with Python, based on standard Python-type hints. It’s designed to be easy to use while being one of the fastest Python frameworks available. 🌟 𝙒𝙝𝙮 𝙩𝙝𝙚 𝙝𝙮𝙥𝙚? (𝘼𝙙𝙫𝙖𝙣𝙩𝙖𝙜𝙚𝙨) 𝗦𝗽𝗲𝗲𝗱: It’s on par with Node.js and Go, thanks to Starlette and Pydantic. 𝐀𝐮𝐭𝐨-𝐃𝐨𝐜𝐬: It generates Swagger UI and ReDoc automatically—no more manual API documentation! 𝐓𝐲𝐩𝐞 𝐒𝐚𝐟𝐞𝐭𝐲: Fewer bugs thanks to rigorous data validation. Asynchronous: Native support for async and await. 🏢 𝙒𝙝𝙤’𝙨 𝙪𝙨𝙞𝙣𝙜 𝙞𝙩? FastAPI isn't just a trend; industry giants like Microsoft, Uber, Netflix, and Tesla are leveraging it to power their services. Plus, the community is massive and growing every day! 📈 🛠️ 𝙏𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙁𝙤𝙪𝙣𝙙𝙖𝙩𝙞𝙤𝙣𝙨 𝘾𝙤𝙫𝙚𝙧𝙚𝙙: 𝗧𝗵𝗲 𝗨𝘃𝗶𝗰𝗼𝗿𝗻 𝗦𝗲𝗿𝘃𝗲𝗿: Learned how this lightning-fast ASGI server keeps everything running. 𝐇𝐓𝐓𝐏 𝐌𝐞𝐭𝐡𝐨𝐝𝐬:Refreshed my knowledge on GET, POST, PUT, and DELETE in the context of FastAPI routes. 𝐓𝐡𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Spent time exploring the official FastAPI docs and their GitHub repo—the DX (Developer Experience) is top-tier! I’m excited to see how my Django background helps me master this "fast" new world. Stay tuned for more updates! #Python #FastAPI #WebDevelopment #BackendDeveloper #CodingJourney #LearningEveryday #Django #SoftwareEngineering #TechCommunity

I built a full LMS in one week. Django. Redis. Celery. Postgres. Alpine.js. 70+ pages. Real-time notifications. AI-assisted workflow. But here's the part most developers skip - I didn't touch the codebase for the first 2 days. Day 1–2: just planning. PRD, requirements, user flows, wireframes. I had the whole thing mapped out — all 70+ pages — before writing a single migration. Day 3–7: build mode. Backend done, frontend hooked up. Somewhere in the middle, I decided to upgrade notifications from basic to real-time HTTP polling. Wasn’t planned. Didn’t slow anything down. What kept it from going sideways? Every backend day ended with testing. If something broke, it didn’t carry over. First thing next morning, fix it before touching anything new. No piling up issues. No "I’ll come back to it." You won’t. When your foundation is solid, changes don’t feel risky, they’re just decisions. And when you actually test as you go, moving fast doesn’t mean things break easily. This week reminded me: speed isn't about typing faster. It's about knowing what you're building before you build it, and not lying to yourself about what's actually done. The loop from Monday applies here too. → Plan → Build → Test → Fix → Ship What’s one rule you follow to move fast without messing things up? #Django #Python #SoftwareDevelopment #BuildInPublic #TechCanada

Stop chasing the latest framework if your database isn't indexed properly. Last week, a client asked to rebuild their backend in a bleeding-edge stack to "fix" slow performance. After an hour of profiling, we realized their Django ORM was just missing a few select_related calls. We didn't need a rewrite. We needed boring, reliable optimization. In my experience as a Tech Lead, business success rarely comes from the trendiest library on GitHub. It comes from keeping the stack simple enough to debug at 2 AM. When we use FastAPI or Supabase, we choose them because they are predictable. We want code that works, documentation that is thorough, and a community that has already solved our edge cases. Shipping a reliable product beats having a "modern" codebase that crashes every time you push to production. Innovation is about solving business problems, not just writing code that looks cool on your resume. What is one "boring" tool in your stack that you would never trade for a shiny new alternative? #SoftwareEngineering #TechLeadership #WebDevelopment #Programming #EngineeringMindset

Day 7 of My Full Stack Journey! 🚀 Today was all about Django Models & Databases — and it was a big day! Here's what I covered: 🔹 Created Django Models with fields like CharField, IntegerField, EmailField & BooleanField 🔹 Ran Migrations and explored the SQLite Database visually 🔹 Set up Django Admin Panel & created a Superuser 🔹 Registered Models in Admin and managed data through GUI 🔹 Used Django ORM — all(), filter(), get(), create(), update(), delete() 🔹 Built 2 complete models — Student & Book — independently! 🔹 Learned 3 ways to add data: Admin Panel (GUI) Django Shell (Python) DB Shell (SQL) The biggest insight today — ORM eliminates the need to write raw SQL. Python directly talks to the database! 🤯 Consistency > Motivation. Showing up every day is the real skill! 💪 Next up → HTML Forms in Django! 🎯 #Django #Python #FullStackDevelopment #100DaysOfCode #WebDevelopment #LearningInPublic #Day7

Your Django endpoint is slow. There are no errors, and nothing obvious stands out in the logs. Even average response times can look fine. But one request is quietly running hundreds of small queries and dragging everything down. That’s what makes this hard to spot. In this post, Jaume Boguñá shows how to trace a slow request with AppSignal and fix it once and for all👇 https://lnkd.in/e-6iCZZt

Day 24: Django Signals Today I dived deeper into Django Signals and how they help build event-driven applications. 🔹 What I learned: • Signals enable communication between different parts of a Django app • They trigger actions automatically when specific events occur • Help keep code decoupled, clean, and maintainable 🔹 Core Concepts: • Sender → The model/class that sends the signal • Receiver → Function that listens and responds • Signal → Event trigger (like "post_save", "pre_delete") 🔹 Hands-on Implementation: ✔ Used "post_save" signal to auto-create a user profile ✔ Connected signals using "@receiver" decorator ✔ Registered signals properly in "apps.py" ✔ Used "Q" and ORM concepts alongside signals 🔹 Advanced Understanding: • Difference between "pre_save" and "post_save" • Avoiding infinite loops in signals • Why signals should not contain heavy business logic • When to use signals vs overriding "save()" method 🔹 Best Practices: ✔ Keep signals lightweight ✔ Use a separate "signals.py" file ✔ Always ensure signals are imported (apps ready) ✔ Prefer signals only for side-effects (not core logic) 🔹 Common Mistakes I Learned to Avoid: ❌ Forgetting to register signals ❌ Writing complex logic inside signals ❌ Debugging difficulties due to hidden execution ❌ Multiple triggers causing performance issues 🔹 Real-World Use Cases: 📌 Auto profile creation after user registration 📌 Sending emails/SMS notifications 📌 Logging database changes 📌 Updating related models automatically 💡 Key Takeaway: Django Signals are powerful for handling background actions, but they should be used wisely to maintain performance and readability. 🔄 Flow Summary: Model Event → Signal Triggered → Receiver Function → Action Executed

This Django + Celery bug has silently corrupted data in more production apps than you think. And most engineers don't even know they're hitting it. Here's the scenario. User registers. You fire a Celery task to send a welcome email. task = send_welcome_email.delay(user.pk) Looks fine. Works in dev. Works in staging. Then in production — random users never get their email. No errors. No exceptions. Task succeeds. Email just... doesn't send. The bug? Your Celery task ran before Django committed the transaction to the database. The task woke up, queried User.objects.get(pk=user.pk) — and the user didn't exist yet. This is the classic Django + Celery race condition. It's been silently wrecking production apps for years. The old fix was ugly: transaction.on_commit(lambda: send_welcome_email.delay(user.pk)) It worked. But it was boilerplate you had to remember everywhere. Celery 5.4 shipped a proper fix — .delay_on_commit(): send_welcome_email.delay_on_commit(user.pk) One method. Zero boilerplate. Task only fires after the DB transaction commits. Race condition gone. I hit this on a large-scale project handling thousands of user signups. Switched every task trigger to .delay_on_commit() and the ghost failures disappeared overnight. If you're using Celery with Django and still calling .delay() directly in views — check your logs. You might be losing tasks you don't know about. Drop a comment if this has burned you before. I know I'm not the only one. 👇 #Django #Celery #Python #BackendEngineering #DjangoTips #WebDevelopment #BuildingInPublic #SoftwareEngineering