FastAPI, Supabase, and Docker for Modern Devs

Building a URL Shortener sounds simple until you have to handle database collisions and clean API redirects. 🚀 Hey LinkedIn family! 👋 Saif here. I recently wrapped up a new Backend project: a Production-Ready URL Shortener API. My goal wasn't just to make it work, but to understand how to build scalable, containerized backend systems. The Features (What it does) Short-Code Generation: Custom logic to create unique, collision-resistant URLs. Smart Redirects: Handling 302 redirects with real-time click tracking. Analytics: Dedicated endpoints to monitor URL performance. URL Management: Ability to deactivate links on the fly. The "Under the Hood" (The Deep Tech) This is where the real learning happened. I didn't just write Python; I built a mini-infrastructure: FastAPI & Pydantic: For strict data validation and lightning-fast performance. PostgreSQL & SQLAlchemy: Managing relational data with clean ORM patterns. Alembic: Handling database migrations (version control for my DB schema). Dockerized Environment: I used Docker to isolate the PostgreSQL environment, managing complex port mappings to avoid host-system conflicts. The Tech Stack 🛠 Backend: FastAPI, Python 3.12 🗄 Database: PostgreSQL, SQLAlchemy (ORM) 🔄 Migrations: Alembic 🐳 Infrastructure: Docker & Docker Compose What’s Next? Currently, it’s running perfectly in my local Docker environment. The next step? I'm moving it to AWS (EC2/RDS) to learn cloud deployment and security groups. Stay tuned—I'll be making the API live in a few days! I'd love to hear your thoughts on the architecture. #Python #FastAPI #BackendDevelopment #Docker #PostgreSQL #SoftwareEngineering #AWS

🚀 The Myth of "10-Hour Vibe Coding": Architecting a Cloud-Native Brain for V3 🧠☁️ Everyone loves a good "I built this in 10 hours with AI" story. Yeah, right! 😅 What nobody tells you is that those initial 10 hours of vibe coding took me a solid month of hard yakka to refine into a proper, functioning memory sandbox. Moving from a local toy to a production cloud environment forced me into some deep engineering reality checks. 🛑 Local Docker Postgres was sweet as—sub-millisecond reads ⚡. But chucking it onto the cloud (Supabase)? Nah, mate. A 40ms network lag turned my DAG retrievals into a brutal N+1 query nightmare, freezing the UI for up to 5 seconds 🥶. You can't just run local sandbox code on a cloud server and hope for the best. So, I’m ripping into the V3 rewrite with a massive architectural shift to fully unleash Supabase's vector and graph potential: 🧠 Compute to Data: No more pulling heaps of data over the network to Python. I'm chucking the DAG traversal and vector cosine maths straight into Postgres RPCs. ⚡ Bypassing the Middleman: Next.js now talks straight to the DB via @supabase/ssr. Faster, leaner, and zero Python bottlenecks. 🤖 Background Worker: Demoting my Python FastAPI from a main server to a quiet internal worker agent. It handles AI logic and LLM deductions asynchronously via a message queue without causing network jitter. 🛡️ True Cloud-Native: Sorting out PgBouncer for transaction-level pooling and locking down strict RLS (Row Level Security) so everything is safe and sound. 🔒 Re-architecting this solo project from a local script into a true Serverless + Agentic beast has been a massive learning curve, but getting the engineering right is going to be absolutely brilliant. 🍻 Time to build! #BuildInPublic #Supabase #PostgreSQL #SoloDev #SoftwareEngineering #VectorSearch #Architecture

🚀 From Idea to API: Building a FastAPI CRUD App with Neon (PostgreSQL) I recently built a backend CRUD application using FastAPI connected to a cloud PostgreSQL database (Neon). This project helped me understand how real-world backend systems handle data efficiently. 🔹 What I Built A complete Student Management API where users can: • Create student records • Read all / single student data • Update student information • Delete records 🔹 Backend Stack • FastAPI (high-performance Python framework) • SQLAlchemy (ORM for database handling) • PostgreSQL (Neon cloud database) 🔹 Key Learnings • Structuring scalable backend architecture • Connecting FastAPI with a cloud database (Neon) • Writing clean CRUD operations using SQLAlchemy • API testing using Swagger Docs 🔹 What’s Next I’m planning to extend this into a full-stack app by integrating it with Next.js and adding authentication (JWT). This is just a small step towards building production-level systems like SaaS platforms 🚀 I’d appreciate your feedback and suggestions! #SMIT #FastAPI #Python #PostgreSQL #BackendDevelopment #WebDevelopment #CRUD #Neon #LearningJourney

⚡ I switched from Django REST Framework to FastAPI on a live project. Here's what nobody tells you about that decision. 👇 --- I was building an enterprise portal for a US client. Deadline was tight. Performance expectations were high. My team suggested FastAPI. I had used DRF for years. But I said — okay. Let's try. What happened next surprised me. 😅 --- 🥊 ROUND 1 — Speed DRF: 🐢 Synchronous — one request at a time FastAPI: ⚡ Async — thousands of requests simultaneously Winner: FastAPI 🏆 --- 🥊 ROUND 2 — Learning Curve DRF: 😤 Takes time — but once you know it, you fly FastAPI: 😍 Know Python type hints? You already know FastAPI Winner: FastAPI 🏆 --- 🥊 ROUND 3 — Database & ORM DRF: ✅ Django ORM is MAGIC. Admin panel FREE. Migrations effortless. FastAPI: ⚠️ No built-in ORM. No admin panel. Extra setup needed. Winner: DRF 🏆 --- 🥊 ROUND 4 — Production DRF: ✅ Used by Instagram, Pinterest. Battle-tested. Stable. FastAPI: ✅ Used by Netflix, Microsoft. Perfect for microservices. Winner: Tie 🤝 --- 🎯 My Honest Recommendation: 🏢 Large team + Complex project? → DRF. Every time. ⚡ Microservices + High performance? → FastAPI. No question. 🚀 Solo developer building SaaS? → FastAPI. Ship faster. 💡 My best stack: FastAPI + SQLAlchemy + PostgreSQL + AWS EC2 --- The truth? Both are excellent. The wrong choice is picking one without understanding your project. --- Which one are you using? Team DRF 🟢 or Team FastAPI 🔵? Drop it in the comments! 👇 #FastAPI #Django #Python #WebDevelopment #BackendDevelopment #FullStackDeveloper #AWS #OpenToWork #RemoteWork

Here's an MCP server for CadQuery I vibe coded with Anthropic Claude code. CadQuery is a parametric CAD library for Python. AI models are spatial reasoning blind: they write the code to generate Parts A and B, but they don't know if Part A is actually next to Part B as it's supposed to be (or if it's even the shape they think it's going to be). That's where this MCP server comes in: an AI mode/agent sends it CadQuery it's written, then the server interprets the cq and generates PNG renderings or 3D model files (3MF and GLB). The model, if it's multimodal can then use its computer vision components to evaluate whether the thing looks right, and if it doesn't, where the problem is, which it then uses to iterate its design. This is a thin wrapper, but it kinda works. I have one running on Google Cloud Run. Here you go: https://lnkd.in/g6XREpn4

For years, the AWS Lambda Handler Cookbook was missing one thing I kept putting off: real, production-grade CRUD across multiple functions with a single, unified Swagger. v9.6.0 finally fixes that, thanks to the event handler alpha feature in Powertools for AWS Lambda's event handler. What's new in v9.6.0: 🔧 Create, get, and delete order APIs as micro Lambda functions over DynamoDB 📄 Unified OpenAPI schema generated across all endpoints 🔍 Automated API breaking changes detection in CI 📑 Swagger published to GitHub Pages and always in sync with the code What you get overall in the cookbook template: 🏗️ Production-ready serverless project in Python with CDK infrastructure 🧪 Five testing strategies: unit, integration, infrastructure, security, and E2E ⚙️ CI/CD with GitHub Actions across dev, staging, and production environments 📊 CloudWatch dashboards and alarms with SNS notifications out of the box 🔒 WAF protection, input validation with Pydantic, and idempotent API design 🏷️ Feature flags and dynamic configuration via AppConfig 📈 Business KPI metrics and distributed tracing with Powertools for AWS Lambda Thanks to Leandro Cavalcante Damascena for developing the Powertools OpenAPI feature that enabled the unified schema. I hope you merge it soon :) 🔗 https://lnkd.in/dZe74TCc #AWSLambda #Serverless #AWS #OpenAPI #PowertoolsForAWS #PlatformEngineering

🚀 Modern Tech Stack Cheat Sheet (2026 Edition) If it’s real-time → WebSockets If it’s scale → Apache Kafka If it’s simplicity → REST If it’s flexibility → GraphQL If it’s AI → Python If it’s infra → Go If it’s logs → Elasticsearch If it’s low-latency → Redis If it’s high-availability → PostgreSQL If it’s streaming → Apache Flink If it’s low-level → C If it’s high-performance → C++ If it’s enterprise → Java If it’s frontend → React If it’s styling → Tailwind CSS If it’s fullstack → Next.js If it’s backend → Node.js If it’s type safety → TypeScript If it’s auth → OAuth If it’s payments → Stripe If it’s search → Meilisearch If it’s caching → CDN If it’s queues → RabbitMQ If it’s containers → Docker If it’s orchestration → Kubernetes If it’s monitoring → Prometheus If it’s dashboards → Grafana If it’s CI/CD → GitHub Actions If it’s version control → Git If it’s testing → Jest If it’s API testing → Postman If it’s secrets → HashiCorp Vault If it’s messaging → gRPC If it’s event-driven → Pub/Sub If it’s data warehouse → BigQuery If it’s vector DB → Pinecone If it’s serverless → AWS Lambda 💡 Build simple. Scale smart. Choose wisely. #SoftwareEngineering #WebDevelopment #SystemDesign #DevOps #Programming #TechStack #Developers

Every SaaS product eventually needs roles. Not because someone planned for them. Because a customer asked for them. "Can we have read-only users?" "Can billing access invoices but not our actual data?" "Can contractors get limited access without full admin rights?" These requests come early. And they expose a problem that is cheap to solve at the start and expensive to retrofit into a live system. In today's article I break down the RBAC architecture that covers the full range — from simple owner/member roles at launch to granular custom roles for enterprise customers: - Why tenant isolation and RBAC are separate concerns that must both be enforced - A data model that supports system roles, custom tenant roles, and multi-tenant memberships - A permission service with Redis caching — one DB hit per user per session - DRF permission classes that compose per-action on the same viewset - Ownership transfer as a separate endpoint — and why role editing must not include it - Exposing the permission set to the frontend so UI and API enforcement stay in sync - The one rule that makes RBAC safe: tenant scoping is always mandatory underneath it #Django #SaaS #Python #BackendDevelopment #RBAC #WebSecurity #SoftwareArchitecture #WebDevelopment #ProductEngineering

🔧 #PythonJourney | Day 150 — Debugging Production Issues & Learning Persistence Today was about persistence in the face of challenges. Sometimes the best learning comes from solving problems that don't have obvious solutions. Key accomplishments: ✅ Built complete backend architecture:   • 7 fully functional API endpoints   • SQLAlchemy ORM with 5 production-grade models   • PostgreSQL integration with proper relationships   • Redis caching layer configured   • Celery async task queue set up   • Docker multi-container orchestration ✅ Implemented critical features:   • User authentication via API key   • URL creation with custom slugs   • Click tracking with event logging   • Analytics aggregation ready   • Soft delete pattern for data preservation   • Password-protected URLs with bcrypt hashing   • Audit logging for compliance ✅ Database design mastery:   • UUID primary keys with proper type casting   • Foreign key relationships with cascade deletes   • PostgreSQL-specific types (JSONB, INET, UUID)   • Index optimization for query performance   • Relationship configurations with back_populates ✅ Docker expertise:   • Multi-service orchestration (PostgreSQL, Redis, FastAPI, Celery, Celery Beat)   • Health checks for service dependencies   • Environment-based configuration   • Volume management for data persistence What I learned today: → Debugging is a critical skill - sometimes it takes multiple attempts → Small details matter (endpoint ordering, type compatibility) → Persistence pays off - keep trying different approaches → Understanding error messages is half the solution → Building production systems is incremental and iterative Progress summary (Days 143-150): - ✅ Project architecture designed - ✅ SQLAlchemy models created - ✅ FastAPI endpoints implemented - ✅ Docker environment configured - ✅ Database connectivity verified - ✅ Authentication implemented - ✅ Test user creation working - ⏳ Endpoint testing (WIP) The foundation is solid. API endpoints are ready for comprehensive testing with pytest and then deployment to GCP. This journey taught me that backend development is about building reliable, scalable systems piece by piece. Each layer matters - from database design to API routing to Docker orchestration. #Python #FastAPI #Backend #Docker #PostgreSQL #SoftwareDevelopment #CodingJourney #Persistence #Learning

🚀 Just deployed Imagify — a full-stack AI Image Generator, live on a cloud server! No fancy CI/CD pipelines. Just a solid production-grade Dockerfile + Docker Compose setup, deployed directly to a cloud server. Sometimes simple is powerful. 🛠 Tech Stack • React — Frontend • Node.js + Express — Backend API • MongoDB — Database • Docker + Docker Compose — Containerization & orchestration • Nginx — Reverse Proxy • OpenAI / Replicate API — AI Image Generation ⚙️ How it works in production Three Docker services running together via Docker Compose: ✅ Nginx (port 80) — receives all user traffic and proxies it to the Node.js backend. Had to tune the buffer size and add a dynamic DNS resolver to handle large auth token headers without 502 errors. ✅ Node.js Backend (port 4000) — built on Node:20-Alpine with python3, g++, and make pre-installed so native npm modules compile cleanly across any machine. ✅ MongoDB — protected by a Docker healthcheck so the backend only starts once the database is genuinely ready — no more race-condition crashes. 🧠 Real problems I solved → 502 Bad Gateway from oversized auth headers → Nginx buffer tuning → Native module build failures in different environments → Unified Alpine base image → Services crashing on startup → Docker healthchecks with ordered startup 🚢 Deployment approach Wrote a production-level Dockerfile and Docker Compose file → SSH'd into the cloud server → pulled the repo → ran docker compose up. Done. No pipeline needed when your configuration is solid. Learned more about production infrastructure from this one project than from months of tutorials. Open to connect with devs working on full-stack or containerized projects! #MERN #Docker #DockerCompose #Nginx #MongoDB #FullStackDevelopment #WebDevelopment #AIApps #OpenAI #DevOps #SoftwareEngineering