Flask CRUD Operations: Create, Read, Update, Delete

Flask + Jinja2 Revision Complete! Just wrapped up a solid revision of key Flask concepts and templating with Jinja2. Here's what I covered: 🔹 Flask Structure – Organizing apps for scalability 🔹 Rendering Templates – Dynamic content with render_template 🔹 Database Integration – Connecting Flask with databases 🔹 Flask-WTF – Handling forms securely and efficiently 🔹 Jinja2 Basics – Template syntax and logic 🔹 Jinja2 Inheritance – Building reusable layouts with base templates Revisiting these fundamentals really strengthens backend development skills and makes building clean, maintainable web apps much easier. Consistency > intensity. Small revisions like this go a long way! #Flask #Python #WebDevelopment #Jinja2 #Backend #LearningJourney #Coding

We're going "AI-First" at my agency, and that shift highlighted something clear: Python shines for heavy backend logic. When building complex orchestration layers that sit between our Next.js frontend and Supabase, I find Django REST Framework or FastAPI just click better than Node.js. The ORM maturity, cleaner async handling for heavy processing, and the sheer volume of excellent ML/AI libraries

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

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

Observability is not logging more. For a long time, I believed adding more logs meant better visibility. In reality, I was creating noise. What changed my perspective was understanding the difference between: Logs → What happened Metrics → How often and how fast Traces → Where time was spent In Django applications, real observability started when I: Added structured logging (JSON, not plain text) Introduced request IDs to trace flows end-to-end Monitored database query durations Measured error rates, not just error messages Set alerts based on behavior, not just crashes The biggest lesson? Most production incidents are not sudden failures. They are slow degradations you didn’t measure. Senior backend development means designing systems that explain themselves. If your Django app can’t tell you what it’s doing under load — you’re flying blind. Hashtags #Observability #Django #BackendEngineering #Python #ProductionSystems #DevOps #ScalableSystems

🚨 BREAKING: Stop Paying for Web Scraping — Run It Yourself There’s a powerful, battle-tested Python framework that lets you scrape and structure data from any website — directly from your own machine. It’s called Scrapy. No SaaS bills. No API rate limits. No data leaving your infrastructure. Just clean, scalable data extraction — on your terms. 💡 Why Scrapy stands out: → Define your spider once, reuse it anytime → Extract clean, structured data effortlessly → Crawl millions of pages at scale → Export instantly to JSON, CSV, XML ⚙️ More than just scraping — it’s a full framework: → Asynchronous architecture for high-performance crawling → Built-in middleware (proxies, retries, throttling) → Powerful CSS & XPath selectors → Pluggable pipelines for validation, cleaning & storage → Proven reliability with 15+ years in production 📊 Trusted by 50,000+ projects and backed by a strong open-source community. 💻 Runs seamlessly on macOS, Windows, and Linux. 👉 If you’re serious about data engineering, automation, or AI pipelines — this is a must-have in your stack. 🔗 GitHub Repo: https://lnkd.in/dJq2GaCV 💬 Curious how this compares with modern AI-based scraping tools? Let’s discuss in the comments. Prakash Software Solutions Pvt. Ltd #Python #WebScraping #DataEngineering #OpenSource #AI #Automation #TechTools

We're moving heavily toward an AI-First stack, and I've noticed something interesting on the backend. When integrating complex, stateful logic—especially around data transformation or when connecting to tools like LangChain or sophisticated ML models—I keep reaching for Django or FastAPI over Node.js. The structure Python offers

My emotional states as an API Developer: ✅200 OK: I am a god. The code is poetry. I should get a raise. 🆕 201 Created: I have birthed a resource. I am a creator of worlds. 🚫400 Bad Request: Why are you sending me strings in an integer field, Karen? 🔐401 Unauthorized: Me trying to talk to my crush. 🔍404 Not Found: My motivation on a Monday morning. 🔥500 Internal Server Error: [Internal Screaming] Everything is on fire and I don’t know why. Building APIs is 10% logic and 90% handling the creative ways people try to break your endpoints. 🐍💻 #FastAPI #Python #APIDevelopment #Backend #CodingHumor

FastAPI 0.131.0 shipped a silent performance monster. I ran the numbers. For those unfamiliar — FastAPI is one of the most popular Python web frameworks for building APIs. It's fast, async-native, and built on top of Pydantic for data validation. It's become the go-to choice for ML/AI backends, microservices, and production APIs across the industry. Now it just got a whole lot faster. JSON serialization now runs on Pydantic's Rust engine. The change? Almost embarrassingly simple. Declare your return type. That's it. FastAPI hands serialization to Pydantic's Rust core instead of Python's json library. ✅ Do this: @app.post("/search") async def search(query: str) -> SearchResponse: return await run_search(query) ❌ Stop doing this: return JSONResponse(content=response.model_dump()) You're converting Pydantic → dict (Python) → JSON bytes (Python). Two slow steps. Let FastAPI + Pydantic handle it in Rust. One step. The benchmarks: 📊 100 search hits, 35KB JSON, 5,000 iterations → Pydantic Rust: 14,372 ops/s → json.dumps (pre-converted): 7,466 ops/s → model_dump + json.dumps (typical): 4,987 ops/s → ~3x faster out of the box 📊 Real-world RAG response — 50 hits w/ 256-dim embeddings + 20 chat messages, 188KB JSON → Pydantic Rust: 1,690 ops/s → Typical pattern: 419 ops/s → 4x faster. 1.8ms saved per request. Bigger payload = bigger gap. #FastAPI #Python #Pydantic #Performance #Backend #WebDev #RustLang

🚀 I Built a CSV Data Upload & Preview Web App using React and Flask watch the code i write live, and correct me if im wrong☺️ I recently built a simple full-stack application that allows users to upload a CSV file and instantly preview the data in the browser. 🔹 Frontend: React (Vite) 🔹 Backend:Flask (Python) 🔹 Data Processing:Pandas 📌 Features: • Upload CSV files directly from the browser • Send file to backend using FormData and Fetch API • Process the dataset using Pandas • Handle missing values by replacing NaN with `None` • Convert dataset into JSON format • Display structured data in the frontend This project helped me understand: • File handling between React frontend and Flask backend • Working with FormData and API requests • Using Pandas for data preprocessing • Handling JSON serialization issues like NaN values 💡 Small project, but a great step in improving my full-stack development and data processing skills #React #Flask #Python #Pandas #FullStackDevelopment #DataScience #WebDevelopment #LearningByBuilding