Most beginners start a Flask project like this: app.py routes.py models.py Everything works… until the project grows. Then suddenly the codebase becomes messy: • Business logic inside routes • Database queries everywhere • Hard to test • Hard to scale So I built something to solve this problem. 🚀 Flask Scalable Template A production-ready Flask project structure designed to keep applications clean and maintainable as they grow. Instead of putting everything in one place, the project separates responsibilities: services → business logic middleware → Middleware models → database layer utils → reusable helpers config → environment configuration Why this matters: • Cleaner architecture • Easier testing • Better maintainability • Scales well for larger projects This template is especially helpful for developers who struggle with “Where should I put this file?” in Flask projects. Building this helped me understand how backend applications should be structured in real-world projects. If you're learning Flask or building backend APIs, this might help you start with a better architecture. I’d love to hear your feedback or suggestions. GitHub link in the comments 👇 #Flask #Python #BackendDevelopment #OpenSource #WebDevelopment

Microsoft Agent Framework (MAF) v1.0 has been released. This is the production-ready release: stable APIs, and a commitment to long-term support. Whether you’re building a single assistant or orchestrating a fleet of specialized agents, Agent Framework 1.0 gives you enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A and MCP. https://lnkd.in/e7VP2eWG #MAF #AI #AIAgents #Python #DoNet

Production-ready multi-agent orchestration is finally here. With Agent Framework v1.0 introducing some exciting new features. My personal favourites: 1. Agent workflows 2. Middleware hooks 3. Integration with managed agents in Foundry Agent Service 4. Multi-agent orchestration with HITL 5. Skills 6. Foundry Tools, Memory, Observability and Evaluations and many more. Check out the amazing blog post from Shawn Henry detailing the various features with concrete examples in Python and .NET https://lnkd.in/dKBRDujY

Excited to share: AgentBudget now has first-party SDKs for Go and TypeScript — not just Python. Same idea across all three: → one line sets a hard dollar limit on any AI agent session → automatic cost tracking → circuit breaking when limits are hit → clear budget reports across OpenAI + Anthropic Python: pip install agentbudget Go: go get github. com/agentbudget/agenbudget/sdks/go TypeScript: npm install @agentbudget/agentbudget All SDKs follow the same session + budget pattern and ship with built-in pricing for 40+ models (GPT-4o, Claude, Gemini, Mistral, Cohere). If you’ve ever had an agent loop and quietly burn $50–$300… this is exactly what AgentBudget is designed to stop. Open source (Apache 2.0). No proxy. No cloud account. No infra. Just a library you drop in. ⭐ https://lnkd.in/e2_tB825 Would love feedback from Go + TypeScript folks building agents — what’s your stack looking like right now? Tags: #AIAgents #OpenSource #Go #TypeScript #Python #LLM #DeveloperTools

I wanted to share a side project I've been working on recently. An AI-powered software development system called #AgenticSDLC. https://lnkd.in/dixK2Dtr The idea was to build something end-to-end from scratch, covering backend, frontend, infrastructure, and #MLOps concepts while learning how modern AI-assisted development workflows are structured. This project gives me a good high level of understanding of how an end-to-end application of this sort can be built.   The system allows you to describe a development task in plain English, and a LangGraph-based agent autonomously plans, writes, reviews, and (lints the code in case of coding agents). Executions are tracked in PostgreSQL, run asynchronously via Celery workers, and streamed live to a Next.js dashboard. Note: The project is in development phase where I’m adding feature one after another. Right now, and there is only python coding agent I’ve added so far, more agents will come in future. (I was focusing on the building app infra first and infra is ready).   Tech stack: - Agent framework: LangGraph AI , LangChain - Backend: FastAPI, Postgresql, sqlalchemy.org, Celery + Redis - Frontend: NextJs 14, TypeScript, Tailwind CSS [ Note: FE of this app was completely vibe coded ; )] - Infrastructure: #DockerCompose, Alembic migrations   I used Claude as an AI pair programmer throughout the build to accelerate development — it helped with architecture decisions, code generation, and debugging. This allowed me to cover a lot of ground quickly while still understanding every layer of the system.   Key things I learned through this project: - End-to-end full stack development (actually more of backend development) - Async task queue architecture (#Celery + #Redis) - LLM agent workflows with #LangGraph - Docker Compose for multi-service orchestration - Database migrations and async #ORM patterns   The code is available here if you'd like to take a look (#README file should help you set it up on your system): GitHub link: https://lnkd.in/dixK2Dtr The codebase has very detailed comments along with documentation that explain the concept and implementation.

🚨 I was wrong about polyglot runtimes. PythonMonkey changed my mind. Last year, I hit a nightmare in data pipeline using both Python and Node. Every tiny bridge between them added milliseconds that turned into hours at scale. I tried subprocesses, sockets, and shared memory-ugly patches. Then I found PythonMonkey. It doesn’t treat JS as an external tool. It runs the Mozilla SpiderMonkey JS engine inside Python itself. Same process. Same memory space. No serialization. No IPC. No more JSON dumps between Python and JS. Strings, lists, and dicts move natively. You can pm.require("crypto-js") right inside Python and decrypt data with JS tooling instantly. JS Promises map to Python await objects. Async just works. It even ships console, setTimeout, XMLHttpRequest-full JS globals-directly into your Python VM. This isn’t just about convenience. It’s the first serious path toward unified multi-language runtimes. AI agents, data pipelines, webapps-all sharing one memory system. Some say it's overkill. It’s the start of something huge. When Python can run NPM modules without context switching, you’re not just saving time-you’re blurring the boundary between languages. The landscape is evolving faster than people realize. Follow me so you don’t miss the next runtime revolution. #Python #JavaScript #Developers #AIagents #GitHub #OpenSource #DataEngineering #SoftwareArchitecture

🐳 I just containerized a Python Flask API using Docker and the results surprised me. Starting point: a naive Dockerfile that produced a 1.15 GB image running as root with zero health monitoring. After applying production best practices: ✅ Image size: 1.15 GB → 136 MB (88% reduction) ✅ Runtime user: root → appuser (non-root) ✅ Build context: 24.18 MB → 1.22 KB ✅ Health checks configured for orchestration compatibility ✅ Docker Compose with Flask + Redis fully orchestrated ✅ Automated score: 100/100 ✅ GitHub Actions CI: Passing I then went a step further; pushed the image to Docker Hub, deleted it locally to simulate a fresh machine, pulled it back down, and ran it. Every security configuration survived the round trip intact. That is what genuine portability looks like. Key things I learned: → The base image you choose is the single biggest factor in image size → Instruction ORDER in a Dockerfile is a performance decision, not just syntax → Running as root inside a container is never acceptable in production → Docker Compose makes multi-container apps manageable with one command I wrote up the full breakdown, every decision, every optimization, and every result in a detailed Medium article. 📖 Read the full article: https://lnkd.in/dzUkHybc 💻 GitHub: https://lnkd.in/dhckiN3d 🐳 Docker Hub: https://lnkd.in/d4hP6Hdf #Docker #DevOps #Python #Flask #Containerization #CloudEngineering #SoftwareEngineering #OpenToWork

Zapier costs $100/month. Make costs $100/month. n8n costs $0 if you self-host — and it can write Python. I've been using n8n for AI automation workflows and the thing that surprised me most: it's not a dumbed-down no-code tool. It's a proper automation platform that happens to have a visual interface. When the GUI isn't enough — you write JavaScript or Python directly in the node. No plugins. No workarounds. 5 things that make it different: ⚡ AI-native: LangChain-based agent nodes built in ⚡ 400+ integrations, 900+ templates ready to use ⚡ Custom code (JS or Python) in any node ⚡ Full self-hosting — your data never leaves your server ⚡ Enterprise SSO + air-gapped deployment for regulated industries 181k GitHub stars. This is what Zapier should have been. Full guide in comments 👇 #n8n #WorkflowAutomation #SelfHosted #AIAgents #NoCode #OpenSource #DevOps

I would easily pay $1,000 for this tool. But its free. Creators call it Skill Seekers - The data layer for AI systems. This tool just automated the most painful part of building with Claude Code, turning any documentation source into a ready-to-install AI skill with automatic conflict detection built in. Skill Seekers does deep AST parsing on Python, JavaScript, TypeScript, Java, C++, and Go repositories, extracts every function, class, and method with parameters and types, then cross-references them against the documentation to find exactly where the docs lie and where the code has moved on without updating anything. It runs as an MCP server with 26 tools, so you can just tell Claude Code to scrape a GitHub repo, detect conflicts, merge the sources, and package the skill without touching the CLI once. The three-stream GitHub architecture splits repos into Code, Docs, and Insights streams and includes issues, labels, stars, and forks as weighted signals for better topic routing. Repo link in 1st comment