PythonMonkey Revolutionizes Multi-Language Runtimes with Unified Memory System

𝗦𝘁𝗼𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘄𝗮𝘆. FastAPI isn't just "another Python framework." It's a deliberate choice — and knowing when to reach for it matters more than knowing how to use it. 𝗣𝗶𝗰𝗸 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘄𝗵𝗲𝗻:  • You're building ML/AI-powered APIs and your team already lives in Python  • You need async performance without the boilerplate of Go or Java  • Auto-generated docs (Swagger/OpenAPI) aren't a nice-to-have — they're a requirement  • You want type safety that actually catches bugs before production 𝗦𝘁𝗶𝗰𝗸 𝘄𝗶𝘁𝗵 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗯𝗮𝗰𝗸𝗲𝗻𝗱𝘀 (𝗦𝗽𝗿𝗶𝗻𝗴, 𝗗𝗷𝗮𝗻𝗴𝗼, 𝗘𝘅𝗽𝗿𝗲𝘀𝘀, .𝗡𝗘𝗧) 𝘄𝗵𝗲𝗻:  • Your org already has deep expertise and infra around them  • You need battle-tested ORM support and a massive plugin ecosystem  • You're building monoliths where convention-over-configuration saves months 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗮𝗻𝘀𝘄𝗲𝗿? 𝗜𝘁'𝘀 𝗻𝗲𝘃𝗲𝗿 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. FastAPI shines where speed-to-deploy, async I/O, and Python-native ML pipelines intersect. Forcing it into a legacy enterprise CRUD app is like using a scalpel to chop wood. Choose your tools like an engineer, not a fan. Thoughts? When did FastAPI click (or not click) for you? #FastAPI #Python #BackendDevelopment #SoftwareEngineering #WebDevelopment #APIDevelopment #TechCommunity #Programming #MLOps #SystemDesign

🎬 MovieMate - AI Powered Tracker (React/FastAPI - Python) Live: https://lnkd.in/gdqynPRK GitHub: https://lnkd.in/gniV-Xqq Built a complete full-stack application to track movies and TV shows, with AI-powered features and production-grade infrastructure. Moving beyond Spring Boot, I explored python FastAPI backend to build this end-to-end application. 🚀What I built : → REST API with FastAPI, SQLAlchemy ORM, and Pydantic schema validation → TMDB API integration → Season-wise episode progress tracking stored as JSON in PostgreSQL → AI Review Generator - rough notes → full review using Groq (Llama 3.3 70B) → AI Recommendations based on watch history and ratings → Watch time stats with Recharts bar charts by genre and platform 🏗 Infrastructure : → Multi-stage Docker build (builder + runtime) to minimize image size → Docker Compose with PostgreSQL healthchecks and service dependency ordering → Backend healthcheck via curl on /docs endpoint → Deployed on Railway (backend + PostgreSQL) and Vercel (frontend) #FastAPI #React #PostgreSQL #Docker #GroqAI #TMDB #FullStack #Python #TailwindCSS #Vercel #Railway #WebDevelopment #Backend

I’m excited to share that I’ve officially published jse-scraper to PyPI! 🚀 Accessing real-time market data can often be a hurdle for independent developers and researchers. I built this library to bridge that gap, providing a high-performance, Playwright-powered tool to extract clean, analysis-ready data from the JSE in seconds. Key Features: -Headless Power: Handles dynamic JavaScript rendering to ensure reliable data capture. -Analysis-Ready: Returns data directly as Pandas DataFrames. -Open Source: Built for the Caribbean developer community. Whether you're building a portfolio tracker or a market dashboard, jse-scraper makes the data ingestion easy! Check it out here: https://lnkd.in/ewN-ChNX #Python #OpenSource #Fintech #DataEngineering #JSE #CaribbeanTech #jamaicadeveloperscommunity

Your API isn’t slow — your pagination might be. 📉 It worked perfectly… until your data grew. Then every page got slower than the last. Your code didn’t change. But your dataset did. The culprit? Offset pagination. The deeper the page, the more rows your database has to scan and skip. Page 1 → fast Page 1000 → painful Same query shape. Very different cost. The fix isn’t always caching. Sometimes it’s changing the pattern. Switch to cursor-based pagination. No skipping. Just seeking. In Django REST Framework: Use *CursorPagination* instead of *PageNumberPagination*. Performance stays consistent — even at scale. Because most performance issues aren’t complex. They’re patterns that don’t scale. And most developers don’t notice… until production. #BackendDevelopment #Django #Python #WebDevelopment #SoftwareEngineering #APIPerformance #DatabaseOptimization #SystemDesign #ScalableSystems #DjangoRESTFramework

Actionpackd Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites #Flask #Python #AI

🚀 𝗙𝗹𝗮𝘀𝗸 𝘃𝘀 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 If you have worked with Python for backend development, you have probably come across Flask and FastAPI. Both are powerful, but they serve slightly different purposes depending on your use case. 🔹 Flask is a lightweight and flexible micro-framework. It’s been around for years and has a huge community. You get full control over how you structure your application. However, that flexibility comes at a cost — you often need to write more boilerplate code and manage things like validation and async handling manually. 🔹 FastAPI, on the other hand, is relatively newer but built for modern APIs. It leverages async programming and type hints, making it incredibly fast and developer-friendly. ⚡ Why is FastAPI faster? FastAPI is built on Starlette (for async support) and Pydantic (for data validation). It uses asynchronous request handling, which allows it to process multiple requests efficiently without blocking the server. 🐢 Why is Flask slower? Flask is primarily synchronous. While you can use async with Flask, it’s not its core strength. For high-concurrency applications, this can become a bottleneck. 🧠 When to use Flask? 1. Small to medium projects 2. Simple APIs or web apps 3. When you need flexibility and full control ⚡ When to use FastAPI? 1. High-performance APIs 2. Microservices architecture 3. Real-time or async-heavy applications 4. When you want automatic validation and documentation 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 - Flask is like a blank canvas — simple and flexible. FastAPI is like a smart toolkit — optimized and ready for scale. Both are great — the choice depends on your project needs, not just speed. #Python #FastAPI #Flask #BackendDevelopment #WebDevelopment #APIDesign #SoftwareEngineering #Programming #Developers #TechCommunity #CodingLife #LearnToCode #AsyncProgramming

prefetch_related solves N+1. But it fetches everything at once. In a real system, that's often not what's needed. Example - - Fetching all orders for a user when only the last 5 are displayed. - Fetching all items in an order when only active ones matter. prefetch_related alone can't do this. It has no way to filter the prefetched queryset. This is exactly what the custom Prefetch() object is meant for. What Prefetch() adds - 1. The Prefetch() object wraps a relationship and lets full control over the prefetch query. Filter the related queryset. Order it. Annotate it. 2. All of this happens in a single additional query, not one per row. The N+1 fix stays intact. 3. to_attr can be used for storing results under a custom name. This matters when the same relationship needs to be prefetched in two different ways simultaneously. The real gains - → Prefetch() makes the ORM behave like a data pipeline. → It fetchs exactly the right data, shaped correctly, in the minimum number of queries. → No need of post-processing in Python. No filtering after the fact. Most Django codebases never reach for Prefetch() and pay the cost in over-fetching data at scale. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

Asynchronous APIs ... Simple. If you've built with Flask, Starlette's mental model is familiar—routes, requests, responses. What changes is the foundation: Starlette is async-first, which means your server handles many requests concurrently without spawning threads. In Day 1, we go from zero to: ✓ A running local server with Uvicorn ✓ Two working API endpoints (GET routes) ✓ A POST handler that reads JSON and creates new tasks Plus: why async matters, why the async/await pattern is everywhere in Starlette, and what the request object actually gives you. No complex setup. Just fresh virtual environment + pip install starlette uvicorn + 30 lines of code that's already production-shaped. → Read the full Day 1 (of 5) Article here: https://lnkd.in/gRNJdhaz #Starlette #Python #ASGI #BackendDevelopment #APIDevelopment #Tutorial #linkedin

Running Prophet forecasting inside a Node.js event loop? That's how you kill your server. When building SensorCore — AI-native analytics via MCP — we hit a wall: our ML tools (Isolation Forest, Decision Trees, Prophet) need heavy computation that doesn't belong in a JavaScript runtime. The solution: a dedicated Python microservice. Our analytics engine is a standalone FastAPI app running on Uvicorn. It owns ALL the heavy ML work: - 13 REST endpoints - 11 Python analyzers - Direct ClickHouse access for data - Python 3.11 + pandas + scikit-learn + Prophet + ruptures Why Python, not Node.js for ML? - scikit-learn, Prophet, ruptures — mature, battle-tested Python libraries - NumPy/pandas for vectorized data processing - No equivalent ecosystem exists in JavaScript - Python's data science stack is 10+ years ahead Why a separate service, not embedded? - Node.js stays fast — zero ML overhead on the API server - Independent scaling — scale ML horizontally without touching the API - Crash isolation — a Prophet OOM doesn't take down your ingestion pipeline - Independent deployments — update ML models without restarting the API The stack in one line: FastAPI + Uvicorn + pandas + scikit-learn + Prophet + ruptures + ClickHouse Packaged in a python:3.11-slim Docker container. One docker-compose up and it's running. The Node.js server handles 1000+ req/s for log ingestion. The Python engine crunches millions of rows for ML. Each does what it's best at. Do you separate your ML workloads from your API server? Or run everything in one process?

We’ve been shipping across the Chonkie stack. Here’s what’s new: - ChonkieJS just got a major refresh, with out-of-the-box support for 7 chunkers, including CodeChunker, SemanticChunker, TableChunker, and FastChunker. Teams can now build best in class retrieval systems without ever leaving the JS ecosystem. - Chonkie Python now includes the TeraFlopAI Chunker, powered by TeraFlopAI’s segmentation API for stronger semantic splitting. We’re seeing especially strong performance on legal documents. Our open-source ecosystem continues to grow across both libraries, and a big part of that momentum comes from contributors pushing the project forward. If you’re building retrieval or ingestion systems in JS or Python, there’s a lot new to explore. Check us out on GitHub: - ChonkieJS: https://lnkd.in/e4v9fyg7 - Chonkie Python: https://lnkd.in/eEnmgYZR