DjangoCampus 2026: Scaling with Django and Beyond

Built a full-stack Purchase Order Management System just used Claude only for Google OAuth its confusing as hell. Stack: FastAPI · PostgreSQL · MongoDB · Docker · Google OAuth · Gemini AI · Vanilla JS Highlights: Dynamic PO creation with live 5% tax calculation Gemini AI auto-generates product descriptions Google OAuth + JWT authentication MongoDB logs every AI request 3 Docker containers running in sync 🔗 https://lnkd.in/gRt8Rihu Always building! #Python #FastAPI #Docker #GenerativeAI #FullStack #WebDevelopment

🚀 𝗕𝘂𝗶𝗹𝘁 𝗮 𝗦𝗺𝗮𝗹𝗹 𝗔𝗜 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 (𝗥𝗔𝗚) 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗼𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 Recently, I built a 𝗳𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗔𝗜 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 to explore how 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) systems work in a microservices setup. The goal was purely 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀. The system can 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀, 𝗰𝗿𝗲𝗮𝘁𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀, 𝗮𝗻𝗱 𝗮𝗻𝘀𝘄𝗲𝗿 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗲 𝘂𝗽𝗹𝗼𝗮𝗱𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁. 💻 𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗔𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: Python, FastAPI, OpenAI, Celery, RabbitMQ, Redis 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: Go (Gin) for user management & APIs 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱: Next.js, React, TypeScript, Tailwind, Zustand, React Query 𝗜𝗻𝗳𝗿𝗮: PostgreSQL, AWS S3, Docker & Docker Compose This project helped me understand: • RAG pipelines and document processing • Polyglot microservices (𝗚𝗼 + 𝗣𝘆𝘁𝗵𝗼𝗻) • Background workers with message queues • Integrating AI workflows with a modern React frontend 📌 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗥𝗲𝗽𝗼: https://lnkd.in/gJuiy9WJ Feel free to 𝗰𝗹𝗼𝗻𝗲 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼, 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲, 𝗼𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗶𝘁. Contributions, suggestions, or improvements are always welcome. #AI #RAG #NextJS #Golang #Python #FastAPI #Docker #LearningInPublic #SoftwareEngineering #ReactJs

Excited to share my latest project: an AI-Powered Event Ticketing & Analytics Platform! 🚀 I built a full-stack system that bridges web development and machine learning. It goes beyond basic event management by using historical data to mathematically predict future event turnout. 🛠️ The Tech Stack: Frontend & Analytics: Next.js, React, Chart.js Backend & Database: Node.js, MongoDB ML Microservice: Python, FastAPI, Scikit-Learn ✨ Key Features: Custom Node.js seeder for synthetic data generation and testing. Real-time visual dashboard to track sales, scan rates, and event volume. Linear Regression ML model running on a separate FastAPI microservice to instantly forecast attendance. Check out the demo below and the source code on my GitHub:👇 https://lnkd.in/gctJgmq3 #MachineLearning #DataScience #Nextjs #FastAPI #FullStack #ArtificialIntelligence #SoftwareEngineering

🦉𝐃𝐚𝐲 𝟐6/𝟑𝟎 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈𝐎𝐩𝐬 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐒𝐑𝐄 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 𝐏𝐮𝐛𝐥𝐢𝐜𝐥𝐲. Escaping "It works on my machine" syndrome 🐳 Over the last 25 days, the NightOwl SRE Platform has evolved into a massive, highly complicated machine. Right now, to run the whole platform locally, I have to boot up a React frontend, start a Node.js event dispatcher, initialize a Python FastAPI gateway, spin up isolated Model Context Protocol (MCP) tool servers, run Kafka... it takes pulling up 6 different terminal windows just to see if the AI is working. That is fine for local building. But it is an absolute nightmare for production deployment. Today for Day 26, it was time to lock everything down. We containerized the entire NightOwl architecture using Docker. But we didn’t just write basic Dockerfiles. We wrote heavily optimized, Multi Stage Builds. Here’s why that matters, When building a React app, you need a giant environment with Node installed and hundreds of megabytes of node modules just to compile the code. But once the code is compiled into static files, you don’t need any of that heavy junk to actually run the app. With multi stage builds, Docker builds the heavy environment in "Stage 1", compiles the code, and then copies only those tiny, compiled files into a fresh, ultra lightweight Nginx container for "Stage 2." The giant Node environment gets thrown in the trash. This dropped our final image sizes drastically, ensuring our containers deploy in seconds and run with a minimal attack surface. The entire autonomous platform is now locked, loaded, and portable. Tomorrow, we start orchestrating all these containers together! Who else is geeking out over multi stage Docker builds right now? #buildinpublic #SRE #ArtificialIntelligence #Docker #DevSecOps #Python #Reactjs #SoftwareEngineering #30DayChallenge

Ever chased that perfect backend setup where speed meets simplicity, only to hit Python's GIL wall or Go's verbosity? Buckle up - 2026 benchmarks show FastAPI closing the gap on Go faster than you think. ⚡ Python 3.14 finally stabilizes no-GIL mode, letting FastAPI handle true multi-threading for CPU-heavy tasks. We're talking 2-3x speedups in multi-core setups, making it killer for real-time data crunching without multiprocessing hacks. But watch out for thread-safety gotchas in old code - refactoring is key. This narrows the throughput race with Go's goroutines, though Go still edges out in raw speed for basic APIs. On the flip side, Go 1.23 amps up generics with smarter type constraints, slashing boilerplate in your server code. Think cleaner HTTP handlers with compile-time safety that FastAPI can't match for massive systems. The trade-off? A bit more compile time and a steeper curve if you're coming from Python. In high-traffic microservices, Go pulls 20-30% lower latency, but FastAPI wins for quick prototypes. Then there's energy efficiency - a CNCF study drops that Go sips 40% less power in Kubernetes under load, thanks to its lean GC and compiled efficiency. Perfect for sustainable cloud ops, cutting bills by up to 25%. FastAPI's interpreter overhead bites here for long runners, but you get Pydantic's validation and auto-docs as the upside. Uber's PyGoLink changes the game too, bridging FastAPI and Go via gRPC for hybrid services with under 1ms overhead. Leverage Python's speed-to-market for prototypes and Go for bottlenecks - boosting throughput 15-20% without full rewrites. Downside is debugging cross-language mess, but it's a architect's dream for scalable mixes. What's your take - sticking with pure FastAPI, all-in on Go, or mixing them for the win? Drop your stack experiences below! 🚀 #FastAPI #Golang #PythonPerformance #BackendEngineering #Microservices

Ever had to work with an API that has zero documentation? No docs. No source code. Just a black box that takes inputs and spits out outputs. I built ProtocolSense for that exact situation. Paste a few input/output examples and it tells you the hidden rules and logic running inside, with confidence scores and evidence .. in seconds. Once you have the rules, export them directly to TypeScript, Python, Zod schemas, or OpenAPI specs. Try at → protocolsense.com ---------- The backstory: It started as a Gemini API Developer Competition project. 2 weeks, built entirely inside Google AI Studio. Submitted and shipped. But I kept thinking about it. So I pulled it out of AI Studio and spent 3 days rebuilding it properly using: → Claude Code — handled auth, edge functions, refactoring → Groq — replaced Gemini for inference, the speed difference is night and day → Supabase — auth, database, edge functions Total time from idea to real product: under 4 days of actual work. If you've ever dealt with a legacy system or undocumented API, I'd love to hear how you handled it, and whether something like this would have helped.

Stop spending days reading files just to understand a new codebase. I built the fix in 48 hours. Meet CodeMind 🧠 Ask "How does the auth flow work?" and it: → Decomposes your query into sub-tasks → Semantically searches your entire repository → Returns a cited answer traced to the exact file Zero hallucinations. Zero cloud. Zero API costs. ━━━━━━━━━━━━━━━━ The part that surprised me most wasn't the AI. It was the chunking logic. Character-based chunking destroys source code context entirely. Line-based chunks with overlap are the only way to preserve relationships between functions across file boundaries. If your chunks don't overlap → your RAG pipeline is blind. ━━━━━━━━━━━━━━━━ The stack I chose deliberately: 🗄️ Endee — vector DB running locally in Docker   Expected it to be slow. Millisecond latency at 1B+ vectors. I was wrong. 🤖 Ollama — local LLM inference. Your code never leaves your machine. 🧠 all-MiniLM-L6-v2 — embeddings via sentence-transformers ⚡ FastAPI + Next.js — backend + frontend ━━━━━━━━━━━━━━━━ What's coming next: → GitHub repo indexing (paste URL, query instantly) → Multi-file diff analysis → 20+ language support ━━━━━━━━━━━━━━━━ Built with caffeine and one very persistent AttributeError: module 'jwt' has no attribute 'encode' 😅 Repo → https://lnkd.in/gkXU3nVf Would you use this in your local dev workflow? Comment below 👇 I read every reply. #buildinpublic #RAG #AI #opensource #LLM #VectorDatabase #Python #NextJS #FastAPI #CodeMind

𝐅𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 𝐭𝐨 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐢𝐧 𝟑𝟎 𝐦𝐢𝐧𝐮𝐭𝐞𝐬. Not a demo. Actual production infrastructure. Building AI agents is the easy part. The hard part? Auth, streaming, observability, background tasks, database migrations. The stuff that turns a prototype into a product. This full-stack template ships all of it out of the box. FastAPI + Next.js with 5 AI frameworks (PydanticAI, LangChain, LangGraph, CrewAI, DeepAgents), and 20+ configurable integrations. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: • 𝐖𝐞𝐛𝐒𝐨𝐜𝐤𝐞𝐭 𝐬𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠: real-time agent responses with tool approval UI • 𝐅𝐮𝐥𝐥𝐲 𝐭𝐲𝐩𝐞-𝐬𝐚𝐟𝐞: end-to-end type safety across Python and TypeScript • 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐫𝐞𝐚𝐝𝐲: auth, rate limiting, Logfire observability, Celery background tasks, webhooks LangChain featured it as a Community Content Spotlight. 100% test coverage. Ships with CLAUDE.md and AGENTS.md so AI coding assistants work with it natively. 𝐓𝐞𝐜𝐡 𝐬𝐭𝐚𝐜𝐤: FastAPI, Next.js, PostgreSQL, MongoDB, Redis, Docker Repo here 👉 https://lnkd.in/gJw6x4MY 🔔 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for more open-source finds. #AIAgents #FastAPI #NextJS #OpenSource #Python

Day 2 of 30 — I turned yesterday's local script into a live web app anyone can use. After Day 1, I had a haiku generator running entirely on my laptop. Cool for me. Useless for everyone else. You can't share a localhost link. So I asked myself the obvious question — how do I get this in front of people without spinning up a server, managing infrastructure, or writing a single line of deployment code? Two tools I had known but never really used much: Groq and Streamlit. Groq hosts Llama in the cloud and gives you API access — same model I was running locally on Day 1, but now on their infrastructure. Faster. No GPU needed on my end. Streamlit turns a Python script into a web app in about 10 lines of code. Connect your GitHub repo, add your API key to Streamlit's secrets manager, and it gives you a public URL. That's it. No Docker. No EC2. No YAML files. Just Python. The app itself is simple — type your mood and a highlight from your day, get a haiku back. But what happened under the hood is the part worth paying attention to: → Day 1: local Python script → Docker → Llama on my machine → terminal output  → Day 2: Python script → Groq cloud API → Llama in the cloud → Streamlit UI → public URL → anyone can use it Same LLM. Same prompt architecture. Same three-part structure: system prompt, user message, response. What changed was everything around the model — the infrastructure, the interface, the accessibility. I know what you're thinking. Who genuinely needs a haiku generator? Nobody. But that's not the point. The point is that in just few hours, starting from zero, I went from typing in a terminal to having a deployed GenAI web application with a shareable link, cloud inference, secrets management, and a UI — using nothing but Python and two free-tier services. Try it yourself here → https://lnkd.in/gqMyFQBm The diagram below shows the full architecture — from local terminal to deployed web app — and what Llama is actually doing internally on every request. Day 2 of 30 done. #GenAI #BuildInPublic #30DayChallenge #Streamlit #Groq #Llama #Python #AIEngineering #LLM #DataEngineering

Did you know FastAPI is now crushing 1M+ requests per second in benchmarks, making it a real contender against Go for high-performance backends? 🚀 As a backend dev, I've been digging into the latest updates, and it's clear Python is closing the gap on Go's raw speed. Here's what stands out from recent releases and benchmarks. First off, Python 3.12 brings killer optimizations like faster function calls and smarter garbage collection. This shaves 10-20% off API response times in FastAPI async workloads, narrowing the divide with Go's goroutines. Trade-off? You'll need to tweak code for compatibility, unlike Go's seamless updates, but it means better scalability for microservices without a full language switch. 🐍 Then there's Pydantic V2 baked into FastAPI 0.100+. With Rust-powered validation, data parsing speeds up by 10-50x, rivaling Go's native JSON handling in data-heavy APIs. Sure, it bumps memory a tad due to those Rust deps, but the type safety boosts dev productivity. Go keeps it simpler for minimal setups, though. Don't sleep on the experimental No-GIL Python via PEP 703. It's paving the way for true multicore parallelism, letting FastAPI scale like Go on CPU-bound tasks. Early days mean more thread safety headaches, and Go's concurrency is more battle-tested, but this could eliminate offloading to Go for real-time processing. Finally, TechEmpower's Round 22 benchmarks show FastAPI, juiced by Python 3.11+ and UVloop, hitting those massive req/sec numbers. It's great for rapid prototyping with auto-docs, though Go edges out on cold starts in resource-tight spots. If you're architecting high-throughput systems, these shifts make FastAPI a strong pick without Go's learning curve. What's your take? Building with FastAPI or sticking to Go for performance-critical backends? Drop your stack or war stories below! 💬 #FastAPI #Golang #PythonPerformance #BackendEngineering #APIOptimization