TurboAPI Replaces FastAPI with Zig, 7x Faster

Solo dev confession thread 🧵 Me: "I'll just use SQLite locally, it's basically the same as PostgreSQL" Production: 💀 Me: "Python 3.13 just dropped, let me use the latest" psycopg2: "I don't know her" Me: "I'll add Alembic migrations later" Future me doing manual ALTER TABLE at midnight: 😭 Me: "I'll add rate limiting after launch" Me after launch: *stares at 47 things that now depend on the API* ───────────────────── I built a real-time app with Flutter + FastAPI + Go completely solo. Authentication, WebSocket messaging, social feeds, the whole thing. And I wrote every honest mistake down so you don't have to repeat them. The full article covers: → Why I used Go for WebSockets instead of FastAPI → The Python 3.13 deployment disaster (and the fix) → How I killed UI jank with BlocSelector → Security gaps I'm still patching (yes, I'm telling on myself) → Performance numbers from actual production Drop a 🙋 in the comments if you've shipped something solo and felt both proud and mildly terrified at the same time. Article link in comments 👇 #Flutter #FastAPI #Python #SoftwareDevelopment #MobileDevelopment

Today I ran into a classic backend bug while testing my FastAPI project 🚀 Everything was working fine until login started throwing a 500 error. After debugging, I found the issue: Environment variables are always read as strings — which caused a failure in timedelta() while generating JWT tokens. A small fix (casting to int) solved it, but the learning was huge: ✔ Always validate environment configs ✔ Debugging > coding ✔ Real-world issues teach the most Also successfully: ✅ Deployed FastAPI app on Render ✅ Integrated PostgreSQL ✅ Implemented authentication with JWT ✅ Added pagination Next step: Docker + CI/CD 🔥 Live API: https://lnkd.in/gVAWNtVp #FastAPI #BackendDevelopment #Python #DevOps #LearningInPublic

Built a real-time LAN chat and file sharing app using Python and Flask. It works like a mini WhatsApp for your local network — no internet needed. Multiple users can chat, share files, and see who's online, all running from a single Python script. Features include real-time messaging with WebSockets, drag and drop file sharing, typing indicators, message reactions, a search system, and a live analytics dashboard. Tech used: Python, Flask, Flask-SocketIO, SQLite, HTML, CSS, JavaScript. This project taught me real-world backend development, WebSocket communication, database design, and deploying Python web applications. 🔗 GitHub: https://lnkd.in/g8EpC-py #Python #Flask #WebDevelopment #BackendDevelopment #OpenSource

🚀 Day 55 – Exploring FastAPI (Modern Backend Magic!) Today I dived into FastAPI, one of the fastest and most efficient web frameworks for building APIs with Python. ⚡ 💡 What is FastAPI? FastAPI is a modern web framework that helps you build APIs quickly using Python, with automatic validation, documentation, and high performance. 🔥 Why FastAPI stands out: ✔️ Super fast (built on ASGI & Starlette) ✔️ Automatic API docs with Swagger UI 📄 ✔️ Type hints = better code + fewer bugs ✔️ Easy to learn and implement ✔️ Async support for high performance 🛠️ What I learned today: 🔹 Creating a basic API 🔹 Handling GET & POST requests 🔹 Path & Query parameters 🔹 Request validation using Pydantic 🔹 Auto-generated interactive docs 💻 Simple Example: from fastapi import FastAPI app = FastAPI() @app.get("/") def read_root(): return {"message": "Hello World 🚀"} 📌 Key Takeaway: FastAPI makes backend development simple, fast, and production-ready with minimal code. Consistency is the real power 💪 #Day55 #FastAPI #Python #BackendDevelopment #APIs #100DaysOfCode #LearningJourney 🚀

Learn in Public #4 ✅ Just added Real-time Video Calling to my Chat Application! Now users can not only text chat but also make video calls between devices. I tested it by calling from my laptop to my mobile phone — and it works smoothly! Watch the demo: - User 1 clicks Video Call button - User 2 gets incoming call popup and accepts - Camera & Microphone permissions - Live two-way video + audio - Mute & Camera toggle (on/off with single click) - End call from either side Built with: • Backend: FastAPI (Python) • Frontend: Simple HTML, CSS & Vanilla JavaScript • Video Calling: WebRTC with custom signaling I'm more comfortable with Python, so using FastAPI felt natural for the backend. Live Demo: https://lnkd.in/g8AuFeks GitHub Repository:   https://lnkd.in/g3DRrNMW What should I build next?   Screen sharing? Group calls? Or something else? Drop your suggestions in the comments 👇 #LearnInPublic #WebRTC #FastAPI #Python #JavaScript #ChatApp #FullStack

🚀 Ever wondered what’s really inside `app.routes` in FastAPI? Most developers use FastAPI daily… but very few actually understand how routing works under the hood 👀 👉 `app.routes` is not just a list — it’s the “core routing table” that decides: * which endpoint gets executed * how requests are matched * and why “order matters more than you think” 💡 Each route carries powerful metadata: `path`, `methods`, `endpoint`, `name`, `response_model` & more — everything FastAPI needs to process a request efficiently. ⚠️ Fun fact: The router follows a “top-to-bottom matching strategy” -> first match wins. 🔥 If you’re building APIs, understanding this gives you: * better debugging skills * cleaner architecture * fewer “why is this endpoint not working?” moments 💬 Have you ever debugged using `app.routes`? Drop your experience below 👇 #FastAPI #Python #BackendDevelopment #APIs #WebDevelopment #SoftwareEngineering

How much money do you save by using LLMs locally? Well I was curious, so yesterday I made a website to figure it out and display it nicely, this is the result! Well actually I half vibecoded it, using local models with GitHub Copilot (hence the 8 million input tokens in one day, in case you were wondering 😁). Quick summary of the tech stack: - daemon on the mac studio where LM Studio is running and that extracts input/output token stats from `lms log stream` and sends it to an API endpoint on the web server running fastAPI to store the records in a sqlite database - pricing comes from openrouter's /api/v1/models endpoint - streamlit for the frontend for easy development The GitHub repos are private for now, but if there is interest I will make them public 😊 #LocalAI #LLMs #Vibecoding #GenerativeAI #LMStudio #FastAPI #Streamlit #GitHubCopilot #Python #SelfHosted

🚀 Built a QR Code Generator using Flask! 🧪 I recently created a simple yet useful QR Code Generator Web App where users can instantly generate QR codes from text or URLs. 💡 This project helped me understand: • Backend development with Flask 🧪 • Connecting frontend with Python • Handling user input & generating dynamic output ⚙️ 🔗 GitHub: https://lnkd.in/dN8JBRTn Always learning, always building. 💻 Would love your feedback! 🙌 #Python #Flask #WebDevelopment #Projects #Coding #Learning

We run AI agents across every major codebase at Cerberus Labs - .NET backends, Vue frontends, Python desktop apps, hardware integration docs. Each agent carries project-specific context between sessions. What that context layer actually contains is worth examining, because it's not documentation for its own sake. It's the accumulated record of decisions that aren't obvious from the code. PayTable's agent knows that financial operations require advisory locks before any balance mutation - not because advisory locking is a general best practice, but because we debugged concurrent race conditions across connection pools until we understood exactly where the failure boundary was. It knows that the logging pipeline routes through Channel<T> because four days of debugging connection pool exhaustion taught us that synchronous logging under high concurrency blocks the thread pool before the DB does. It knows that meal period transitions in the order flow have to complete atomically, and why. These constraints aren't guessable from the file structure. They're not in the README. They exist in the context layer because we put them there. The same pattern holds across the other projects. The Python desktop tools know which SQLAlchemy session lifecycle decisions were intentional and which SQLite constraints are load-bearing. The Vue frontends know which component boundaries reflect architecture decisions versus historical accident. When an agent reasons about where to make a change, that distinction matters. Without it, an agent reads the code as it is and makes locally plausible suggestions. With it, an agent reads the code in the context of how it got there - and those are different problems. The first produces code that compiles. The second produces code that belongs within the architecture's context and design tenets. The difference between an agent that generates plausible code and one that generates correct code is the context layer underneath it. #python #dotnet #vue #AI

I've been building a desktop app that lets you analyze data in (pretty much) plain English. Load a data file, type something like 'describe *', and it just works! It generates stats, charts, and the Python/R code for those interested. Runs totally local, nothing leaves your machine, and you can even try it for free. https://scarabdata.dev/ https://lnkd.in/g5mbD6kA