Built something that's been on my list for a while 👀 I built an open-source CLI tool called systemgen that lets you  save your entire project stack as a reusable profile and turn it into a  structured prompt any AI coding tool understands immediately. No need to constantly re-explain your setup EVERY single session. How it works: → Run systemgen init and answer a few questions about your stack → Your framework, folder structure, naming conventions + your prompt → Pipe it straight to Claude Terminal, Ollama, or copy to clipboard for Cursor Works for any language and stack: → Python / FastAPI → TypeScript / Next.js → Dart / Flutter → Go, Rust, JavaScript + more Useful when you want your AI to already know how you work before  you write a single line :) Pair it with mem0 = no starting from scratch, no re-explaining decisions you already made 🧠 w/ https://lnkd.in/eV7j5ScK Install 🔗: pip install systemgen Source 🔗: https://lnkd.in/e9FcDhdN Hope this helps ✅ Drop a Like if you found this useful! 👍 Follow me for more projects like this in the future💎 #python #opensource #ai #developertools

Ever wondered if Python could finally ditch its GIL shackles and go toe-to-toe with Go for screaming-fast backends? Spoiler: In 2026, it did. 🚀 Let's break it down with the latest from the trenches. First off, Python 3.14 made no-GIL mode production-ready, unlocking true multicore parallelism in FastAPI apps. We're talking 2-5x speedups for CPU-bound tasks like data crunching in microservices. The catch? You'll need to refactor for race conditions, and memory might spike, but it means architects can stick with Python's rapid dev cycle without jumping ship to Go for scalability. On the FastAPI side, version 1.0 dropped with native async support for Python 3.12, slashing context-switching overhead and delivering 20-30% lower latency in I/O-heavy APIs. It's a game-changer for high-throughput systems, making it competitive with Go's goroutines. Trade-off: Migrating sync code gets messier, with more debugging time upfront. Go isn't slacking either. Go 1.22 brought built-in WebAssembly support, letting you compile backends to run at near-native speeds in edge or serverless setups. It crushes FastAPI in cold starts by up to 50%, thanks to static binaries ditching interpreter baggage. Downside? Steeper curve for Wasm tweaks, but it's gold for hybrid cloud-edge architectures. And if you're picking sides, Uber's 2026 benchmark update shows Go edging out in raw throughput (15% better RPS in high-concurrency spots), but FastAPI wins big on dev velocity—30% faster feature rolls with its ecosystem. Go shines for ops efficiency, Python for quick innovations. ⚡ What's your take? Building high-performance backends—do you lean FastAPI for speed-to-market or Go for raw power? Drop your stack stories below. 👇 #FastAPI #Golang #PythonBackend #Concurrency #Microservices

🚀 𝗝𝘂𝘀𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗲𝘅𝘁𝘂𝗮𝗹... 𝗮𝗻𝗱 𝗶𝘁’𝘀 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 👀 Lately, I’ve been playing around with Textual (Python TUI framework) — no big project yet, just pure experimentation. And honestly… it feels different. 💡 Building UI without a browser 💡 No React, no Angular 💡 Just Python + terminal Still early for me, but a few things stood out: • Super fast to spin up • Clean UI with CSS-like styling • Everything in one language (Python) • Runs anywhere — even over SSH Not saying it replaces web or desktop apps… But for internal tools, dashboards, or admin panels — this could be really useful. For now, I’m just exploring and testing ideas. Let’s see where it goes 𝗔𝗻𝘆𝗼𝗻𝗲 𝗲𝗹𝘀𝗲 𝘁𝗿𝗶𝗲𝗱 𝗧𝗲𝘅𝘁𝘂𝗮𝗹 𝘆𝗲𝘁? #Python #Textual #LearningInPublic #DevExperiment #FullStackDeveloper

𝘼 𝟐𝙂𝘽 𝘿𝙤𝙘𝙠𝙚𝙧 𝙞𝙢𝙖𝙜𝙚 𝙞𝙨 𝙖 𝙙𝙚𝙥𝙡𝙤𝙮𝙢𝙚𝙣𝙩 𝙗𝙤𝙩𝙩𝙡𝙚𝙣𝙚𝙘𝙠. I was building a GenAI API and the image size was massive. Every deploy took forever. Then I switched to multi-stage builds. Here is the exact snippet that cut the size by 70%: # 𝘚𝘵𝘢𝘨𝘦 1: 𝘉𝘶𝘪𝘭𝘥 𝘍𝘙𝘖𝘔 𝘱𝘺𝘵𝘩𝘰𝘯:3.10-𝘴𝘭𝘪𝘮 𝘈𝘚 𝘣𝘶𝘪𝘭𝘥𝘦𝘳 𝘞𝘖𝘙𝘒𝘋𝘐𝘙 /𝘢𝘱𝘱 𝘊𝘖𝘗𝘠 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘮𝘦𝘯𝘵𝘴.𝘵𝘹𝘵 . 𝘙𝘜𝘕 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 --𝘵𝘢𝘳𝘨𝘦𝘵=/𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 -𝘳 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘮𝘦𝘯𝘵𝘴.𝘵𝘹𝘵 # 𝘚𝘵𝘢𝘨𝘦 2: 𝘙𝘶𝘯 𝘍𝘙𝘖𝘔 𝘱𝘺𝘵𝘩𝘰𝘯:3.10-𝘢𝘭𝘱𝘪𝘯𝘦 𝘞𝘖𝘙𝘒𝘋𝘐𝘙 /𝘢𝘱𝘱 𝘊𝘖𝘗𝘠 --𝘧𝘳𝘰𝘮=𝘣𝘶𝘪𝘭𝘥𝘦𝘳 /𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 /𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 𝘊𝘖𝘗𝘠 . . 𝘌𝘕𝘝 𝘗𝘠𝘛𝘏𝘖𝘕𝘗𝘈𝘛𝘏=/𝘢𝘱𝘱/𝘥𝘦𝘱𝘴 𝘊𝘔𝘋 ["𝘱𝘺𝘵𝘩𝘰𝘯", "𝘢𝘱𝘱.𝘱𝘺"] The logic is simple: • 𝙎𝙩𝙖𝙜𝙚 𝟏 installs dependencies in a full environment. • 𝙎𝙩𝙖𝙜𝙚 𝟐 copies only the artifacts needed to run. No build tools. No cache. Just the app. Smaller images mean faster scaling and cheaper storage. 𝘼𝙧𝙚 𝙮𝙤𝙪 𝙨𝙩𝙞𝙡𝙡 𝙪𝙨𝙞𝙣𝙜 𝙨𝙞𝙣𝙜𝙡𝙚-𝙨𝙩𝙖𝙜𝙚 𝙗𝙪𝙞𝙡𝙙𝙨 𝙛𝙤𝙧 𝙝𝙚𝙖𝙫𝙮 𝙖𝙥𝙥𝙨? #Docker #DevOps #Python #PlatformEngineering #ShreyasTech

Title: Async Views — Handle High-Volume Flower Orders 🚀 Opening Hook: Imagine a bustling flower shop in spring, bouquets flying off the shelves! 🌸 How do you ensure every order blooms with efficiency? The Problem: Traditional views can wilt under pressure. Let's look at the typical approach: ```python # BAD approach def handle\_order\(request\): flowers = Flower.objects.all\(\) bouquets = Bouquet.objects.filter\(order=request.order.id\) # Processing logic return render\(request, 'order.html', \{'flowers': flowers, 'bouquets': bouquets\}\) ``` The Solution: Let your orders blossom using Django's async views! 🌼 ```python # GOOD approach async def handle\_order\(request\): flowers\_task = Flower.objects.all\(\) bouquets\_task = Bouquet.objects.filter\(order=request.order.id\) flowers, bouquets = await asyncio.gather\(flowers\_task, bouquets\_task\) return render\(request, 'order.html', \{'flowers': flowers, 'bouquets': bouquets\}\) ``` Think of it like arranging multiple bouquets at once, instead of one by one! Did You Know? 💡 Under the hood, Django's async views allow for non-blocking I/O operations, which means your server can handle other requests while waiting for database queries. Why Use It? - ⚡ Performance impact - 🧹 Code quality improvement - 📈 Scalability advantage The Golden Rule: Async views will help your code "rose" to the occasion. Engagement Question: How have async views helped your projects? Or what's your favorite tip for using them? Share below 👇 Hashtags: #Django #Python #WebDevelopment #Backend #Performance #FlowerShop #DjangoORM

🤖 Python Project: Hotel Menu Chatbot I recently built a Hotel Menu Chatbot using Python and Flask that allows users to interact with a restaurant menu in a simple and interactive way. 🍽️ Project Features: • View menu categories • Select items from different food categories • Interactive chatbot-style interface • Simple web interface using HTML, CSS, and Flask • Menu data stored in a JSON file 🛠 Tech Stack: 🐍 Python 🌐 Flask 📄 JSON 🎨 HTML & CSS This project helped me practice backend development, working with APIs, handling JSON data, and building simple chatbot logic. 🔗 GitHub Repository: https://lnkd.in/gHkAEYWi I’m currently working on more Python automation, AI, and web development projects to improve my skills. 💬 Feedback and suggestions are welcome! #Python #Flask #Chatbot #WebDevelopment #PythonProjects #Coding #DeveloperJourney

🚀 Stop guessing your tech path. Here’s the 2026 Python + AI Roadmap. Most beginners get stuck because they don’t know what to learn next. In 2026, Python alone isn’t enough — you need Full-Stack + AI skills. Here’s the roadmap that actually works: 🔹 Foundation → Python + Git 🔹 Frontend → React (UI that users love) 🔹 Backend → Django/Flask (real systems) 🔹 AI Layer → APIs + ML (the game-changer) 💡 This is the stack behind real-world products. 📌 If you’re serious about tech — don’t just learn, build. 👇 Comment “ROADMAP” and I’ll share more resources! #PythonDeveloper #FullStack #AI #WebDevelopment #CodingRoadmap #TechCommunity #SoftwareEngineering

Okay, let's be real for a second. Writing the Python code for a machine learning model or an LLM script is incredibly satisfying. But building the web interface so other people can actually test it out? That usually means suddenly having to wrestle with HTML, CSS, JavaScript, or React. As someone focusing heavily on AI and backend logic, frontend web dev is the last thing I want to spend my weekend on. For Day 7 of #TheLLMBlueprint, I want to share the tool that completely fixed this bottleneck for me: Gradio. If you haven't used it, Gradio is an open-source Python library that lets you build a working, interactive web UI for your models in literally 3 to 5 lines of code. You don't need to know any web development. You just: Write your standard Python function (e.g., a function that takes a text prompt and returns a generated response). Tell Gradio what your input looks like (a text box) and what your output looks like (a text box). Run the script. Boom. It spins up a clean web app locally on your machine. Even better, if you set share=True, it generates a temporary public link you can send to your friends or colleagues so they can test your AI model right from their own phones, without needing to install anything. Sharing your AI projects shouldn't be harder than building them in the first place. What's your go-to stack for showing off your Python/AI projects? Are you a Gradio fan, or do you prefer something like Streamlit? Let me know below. 👇 #AIEngineering #Python #Gradio #MachineLearning #TheLLMBlueprint #AI_with_Harshit #BuildInPublic

If you're building backend systems in Python—especially APIs for AI applications—you already know FastAPI is an absolute game-changer. But beyond the raw speed, the engineering concepts behind its design are what make it my go-to framework for modern backends: 1️⃣ Strong Typing & Validation: Thanks to Pydantic, data validation goes from being an imperative headache to a clean, declarative process. You catch errors right at the entry point. 2️⃣ Native Async Support: Handling I/O bound tasks, database queries, or external calls to LLMs becomes incredibly efficient with native async and await. 3️⃣ Dependency Injection: Honestly, one of my favorite features. It makes sharing database connections, enforcing security rules, and writing isolated unit tests incredibly straightforward. 4️⃣ Automatic Documentation: Getting OpenAPI (Swagger) and ReDoc generated automatically drastically reduces the friction between backend and frontend teams. The image below shows how it compares with other popular frameworks. It forces you into good development habits by design. For the Python devs out there, what is your favorite feature of FastAPI? #FastAPI #Python #Backend

🚀 I Built an AI-Powered Weather Dashboard using Django What started as a simple weather app turned into a full interactive dashboard. While building this project, I explored how APIs, data visualization, and AI can work together in a real application. 🌦 Features I implemented: 📍 Get weather using current GPS location 🔎 Search weather by any city 📊 Hourly temperature graphs 📅 5-day forecast dashboard 🗺 Interactive weather map visualization 🌅 Sunrise & sunset data 🌫 Air Quality Index 🌧 Weather animations 🤖 AI-generated weather summary ⚙ Tech Stack Python • Django • APIs • JavaScript • Chart.js • Maps • AI This project helped me better understand how to build data-driven web applications and improve both backend and UI development. More projects coming soon as I continue learning and building. 💻 If you want to download source code Kindly visit: https://lnkd.in/g_QHgCNg #Python #Django #AI #WebDevelopment #MachineLearning #APIs #CodingJourney #100DaysOfCode