🚀 Finally shipped my Football Match Outcome Prediction System ⚽ Built with Python, XGBoost, FastAPI, PostgreSQL & Streamlit. 🔗 football-match-outcome-predi… ⏳ First load may take ~1 min (free tier). #Python #MachineLearning

Hi,Lets Connect! I work in full stack with Next and FastAPI

Hi Shrikant ,Lets Connect! i am full stack dev working with Next and FastAPI

FastAPI repo have absolutely zero issues right now.

Just sent you a DM, the AI/ML backend role is right in my lane (agentic AI, RAG, FastAPI, 1.5 yrs shipping production). Would love to be considered

Which one do you use for backend frameworks? - Spring Boot - NestJS - FastAPI - Express - Django - Go Gin - Ruby on Rails - Laravel

Want to become a Python Developer in 2026? Start here first: - Python Basics & Syntax - Functions Advanced Concepts (decorators, generators) - OOP (Classes, Inheritance, Dunder Methods) - File Handling Data Formats (CSV, JSON) - Working with APIs (requests, FastAPI) - SQL for Data Operations - Git GitHub (version control) - Virtual Environments Packaging - Error Handling Logging - Testing Basics (pytest) - Web Frameworks (Flask or FastAPI) - Deployment (Docker basics cloud) Python development isn’t just writing scripts. It’s about building clean, scalable, and maintainable applications that solve real problems. Master these foundations first. Then start shipping projects. Save this list. Build daily.

Fresh IT Graduate | AI Engineer Built multi-agent AI systems, RAG applications, and ML-powered products with Python, FastAPI, React, and LangGraph. Seeking AI Engineer or ML Engineer opportunities. Building AI that solves real problems. #OpenToWork #AIJobs #GenAI #Python #LLM

This talk explores the systems engineering aspects of building production-grade ML inference platforms and systems using PyTorch and FastAPI, focusing on the architectural and operational complexities involved in serving DL workloads reliably under real-world traffic constraints.

🚀 5 Stages of Python Web Development Every Developer Should Learn Most beginners think learning Python alone is enough. It’s not. Follow this if you starting from scratch 1️⃣ Frontend * HTML * CSS * JavaScript 2️⃣ Backend (Python) * Django * Flask * FastAPI 3️⃣ Database * PostgreSQL * MySQL * SQLite * MongoDB 4️⃣ APIs * REST * GraphQL * JSON * SOAP 5️⃣ Deployment * Docker * Render * Vercel 📌 Learn them in this order: HTML/CSS/JS → Python → Framework → Database → APIs → Deploy Master this stack, and you’ll be able to build and ship complete web applications from scratch. 💻🔥

The Best Open Source Repos to Start Contributing Today A. Python 1. github/fastapi/fastapi 2. github/pandas-dev/pandas 3. github/scikit-learn/scikit-learn 4. github/matplotlib/matplotlib 5. github/scrapy/scrapy 6. github/pytest-dev/pytest 7. github/cookiecutter/cookiecutter 8. github/zulip/zulip B. Java 1. github/JabRef/jabref 2. github/trinodb/trino 3. github/open-metadata/OpenMetadata 4. github/questdb/questdb 5. github/elastic/elasticsearch 6. github/TEAMMATES/teammates 7. github/commons-app/apps-android-commons 8. github/zaproxy/zaproxy C. JavaScript / TypeScript 1. github/freeCodeCamp/freeCodeCamp 2. github/storybookjs/storybook 3. github/vitejs/vite 4. github/vitest-dev/vitest 5. github/microsoft/TypeScript 6. github/microsoft/vscode 7. github/eslint/eslint 8. github/fastify/fastify 9. github/hoppscotch/hoppscotch 10. github/appsmithorg/appsmith D. Go 1. github/kubernetes/kubernetes 2. github/helm/helm 3. github/docker/cli 4. github/containerd/containerd 5. github/moby/moby 6. github/gohugoio/hugo 7. github/mattermost/mattermost 8. github/pingcap/tidb 9. github/hashicorp/terraform 10. github/SigNoz/signoz E. Rust 1. github/rust-lang/rust-clippy 2. github/rust-lang/rustfmt 3. github/nushell/nushell 4. github/hyperium/hyper 5. github/servo/servo 6. github/tikv/tikv 7. github/GyulyVGC/sniffnet 8. github/tensorzero/tensorzero F. Cloud / DevOps / Infrastructure 1. github/kubernetes/kubernetes 2. github/helm/helm 3. github/argoproj/argo-cd 4. github/prometheus/prometheus 5. github/grafana/grafana 6. github/open-telemetry/opentelemetry-collector 7. github/hashicorp/terraform 8. github/crossplane/crossplane 9. github/backstage/backstage 10. github/meshery/meshery G. AI / ML / Data 1. github/pytorch/pytorch 2. github/scikit-learn/scikit-learn 3. github/pandas-dev/pandas 4. github/jupyter/notebook 5. github/ray-project/ray 6. github/mlflow/mlflow 7. github/apache/airflow 8. github/apache/superset 9. github/bokeh/bokeh 10. github/pymc-devs/pymc H. Backend / API / Databases 1. github/fastapi/fastapi 2. github/fastify/fastify 3. github/supabase/supabase 4. github/appwrite/appwrite 5. github/hasura/graphql-engine 6. github/PostgREST/postgrest 7. github/trinodb/trino 8. github/questdb/questdb 9. github/pingcap/tidb 10. github/open-metadata/OpenMetadata I. Frontend / UI 1. github/storybookjs/storybook 2. github/vitejs/vite 3. github/vuejs/core 4. github/mui/material-ui 5. github/gatsbyjs/gatsby 6. github/electron/electron 7. github/Leaflet/Leaflet 8. github/webdriverio/webdriverio

【補足】hermes-agent を使うなら ① GitHubからcloneしてrequirementsをpip install ② ローカルでFastAPIサーバーを起動して /chat にPOST ③ APIキーは.envで管理、GPU推奨（8GB以上） 注目は： ・会話履歴を自動で保持して成長を可視化 ・ツール呼び出しと自己改善ループがワンコマンド 実際に動かして「成長」を体感してみてください

Unrelated to HTMX/FastAPI. I'm rebuilding devpu.sh to be Docker Compose compatible. It's a completely different model from what I was doing before and would have required a complete rewrite even if I stuck to FastAPI HTMX. But since I'm doing the rewrite, I thought I might as well pick a stack that manages async better and is more popular (it's open source).

🐍 Why did adding just one keyword make a Python app handle more users? ⚡ Let’s talk about async. 🧩 The Setup A team built an API using Python. Flow: User → FastAPI → External API → Database Everything worked fine. Then traffic grew. Suddenly: • Responses became slow • Requests started piling up • More servers were needed • Costs increased 🔍 The Root Cause The app spent most of its time waiting: • API calls • Database queries • Network requests While waiting, it couldn't do other work. 🛠 The Fix They switched key endpoints from: def to async def Now Python could handle other requests while waiting for I/O operations. 💡 What Changed Asyncio became the traffic manager. It helped them: • Serve more users • Reduce response times • Use resources efficiently • Scale without adding many servers 🧠 The Real Insight async doesn't make code execute faster. It makes waiting smarter. 🎯 Final Lesson In Python, performance problems aren't always solved with more hardware. Sometimes, one async keyword can unlock far better scalability. 🚀🐍

I'm rewriting /dev/push entirely, moving it from FastAPI HTMX to TanStack Start. Not because I love React (I don't), but because it doesn't matter once somebody else is writing the code. And Codex is actually writing the code (mostly).

During a home lab set up, ArRENCE even gives you the raw code to run the LLM from. It's a small, simple, light weight, no frills llama.cpp|fastapi build; but it will get you up and running and then you can easily expand off that code to customize/optimize/further secure it :)

express? fastapi? sure. but if you want a stack that doesn't make you write boilerplate for auth, validation, and routing... morojs. it's just better. 🌲⚡️

大型语言模型后训练的环境搭建，比算法本身更折腾。OpenEnv搞了一套标准化接口，把RL训练环境做成独立、容器化的服务，用WebSocket通信而不是轮询，延迟和隔离都兼顾了。设计上值得看的是它用FastAPI做server，每个环境一个单独容器，CLI一键部署到HF Spaces，而且容器提供商抽象层让你能在本地Docker、K8s甚至UV上跑，架构分层很清晰。 github.com/huggingface/OpenE…

production-ready full-stack AI agent template you didn't know you needed FastAPI Next.js pick your framework: → PydanticAI → LangChain / LangGraph → CrewAI / DeepAgents WebSocket streaming, auth, multi-DB, 20 integrations all out of the box just run: pip install fastapi-fullstack github.com/vstorm-co/full-st…