This guide walks through the new MCP 2026 update—showing how to build a stateless FastAPI server and a TypeScript client, run curl calls, and highlights benefits such as session‑free requests, cacheable tool lists, easy load‑balancing, and optional OAuth2 protection.
In a FastAPI project I deliberately introduced five hidden bugs and ran Claude Code's /code-review, which identified every injected issue, confirmed them, and even discovered an old real bug, while the article details the tool's multi‑phase pipeline, performance metrics, and speed‑up tricks.
This article introduces FastAPI, compares it with Spring Boot and Flask, explains why its ASGI architecture delivers superior performance, provides step‑by‑step setup and code examples—including routing, validation, dependency injection, async support, database integration, unified responses, middleware, and automatic documentation—then evaluates its pros, cons, ideal use cases, and a real‑world benchmark against Spring Boot.
FastAPI, a modern Python web framework, offers high‑performance APIs, automatic documentation, type‑safe validation and native async support, making it a compelling alternative to Spring Boot and Flask for AI model serving, microservices, and rapid prototyping, as demonstrated by benchmarks, code examples, and real‑world comparisons.
FastAPI, a modern Python web framework, offers high performance, rapid development, and strong type safety, making it an attractive choice for Java developers looking to build AI services, data‑processing APIs, or micro‑services, as demonstrated by benchmark results, real‑world case studies, and a step‑by‑step implementation guide.
FastAPI, a modern Python web framework built on ASGI, offers high performance, automatic documentation, type‑safe request validation, and native async support, making it an attractive choice for Java developers seeking to build fast APIs, microservices, or AI model deployment services.
This article explains why FastAPI has become popular among Java developers, comparing its high‑performance, type‑safe, async‑first design to Spring Boot and Flask, showing benchmark results, core concepts, step‑by‑step setup, pros and cons, real‑world use cases, and a practical performance comparison.
This article introduces WebRTC, compares it with WebSocket, explains its architecture and core APIs, shows how to build a FastAPI‑based signaling server, demonstrates a full JavaScript P2P connection setup with TURN configuration, and provides code for screen sharing and audio/video controls.
This article explains WebSocket fundamentals, compares it with HTTP polling, details the handshake and frame format, and provides complete server‑side examples in Python, FastAPI, Go, and Java Spring, followed by a full real‑time chat system with private messaging, database schema, heartbeat handling, and Redis‑based online presence management.
This article explains FastAPI’s core concepts, compares it with Spring Boot and Flask, shows why its ASGI‑based architecture delivers superior performance, provides step‑by‑step setup, code examples, benchmark results, pros and cons, and practical use‑case recommendations for Java developers.
Using Cursor AI, the author demonstrates how to create a full‑stack study‑room reservation platform in about 20 minutes, detailing the FastAPI backend, Vue 3 frontend, role‑based access, reservation workflow, database schema, security mechanisms, deployment steps, and key design considerations.
The article examines the challenges of locating and managing enterprise architecture knowledge, explains why mere documentation is insufficient, and introduces the open‑source AxisRobo‑PAMP platform that connects business capabilities, applications, data, risks, and AI projects through a workflow‑driven governance model.
This article walks through adding a POST /v1/chat/stream endpoint that uses Server‑Sent Events (SSE) to stream structured events—including status, tool calls, token deltas, and completion—into a static web page, enabling a multi‑turn, typewriter‑style chat UI while preserving the original synchronous /v1/chat API for backward compatibility.
This tutorial walks through the functional planning, project layout, core Python code, context management, FastAPI service, and execution steps needed to create a full‑featured AI coding assistant that can generate, explain, refactor, fix, test, and review code.
In 45 days a two‑person team delivered an AI‑powered contract review platform that parses PDFs, extracts key clauses, flags risks, and integrates with enterprise tools, using Python, FastAPI, LangChain, large language models, vector databases and OCR technologies.
Arthas Eyes packages Alibaba’s Arthas into a browser‑based GUI, using a Python FastAPI backend and a Vue 3 front‑end, allowing developers to attach to local or remote Java processes, visualize memory, GC, threads, trace calls, view flame graphs, and perform diagnostics within five minutes without memorizing CLI commands.
The article provides a step‑by‑step learning roadmap for beginners to master AI and Agent development, covering essential programming foundations, model APIs, prompt engineering, tool calling, RAG, multi‑stage project builds, evaluation, logging, security, and deployment, with concrete examples and open‑source resources.
An end‑to‑end multi‑agent Retrieval‑Augmented Generation platform is presented, featuring compositional reasoning, systematic multi‑dimensional evaluation, and a controlled prompt‑improvement loop that automatically identifies weak prompt dimensions, proposes diffs, and requires human approval before deployment, with full observability via SSE and persisted logs.
This guide introduces the open‑source Free Claude Code project, explains its FastAPI‑based proxy architecture that routes Claude Code requests to various backends such as NVIDIA NIM, OpenRouter, DeepSeek, LM Studio, llama.cpp, and Ollama, and provides step‑by‑step instructions for installation, configuration, and deployment on local machines.
The article demonstrates building a type‑safe, reusable generic repository with Python generics and SQLAlchemy, showing how to replace repetitive CRUD implementations across multiple FastAPI entities, reduce code size from hundreds of lines to a few dozen, and avoid common pitfalls such as missing rollbacks.
The article explains how, after format standardization removed the bottleneck for enterprise AI agents, the real challenge shifted to internal logic design, and presents five reusable Agent Skill design patterns—Tool Wrapper, Generator, Reviewer, Inversion, and Pipeline—complete with code samples, typical use cases, and best‑practice guidelines for combining and selecting them.
Claude Code offers powerful AI‑assisted coding but its official API is costly and hard to access; the open‑source free‑claude‑code proxy intercepts Claude’s requests, forwards them to free or low‑cost LLM providers, provides multi‑model routing, zero‑cost usage, detailed setup steps, advanced bot and voice features, and troubleshooting guidance.
free-claude-code is a lightweight Python/FastAPI proxy that lets Claude Code bypass costly official APIs by routing requests to free or low‑cost LLM providers, addressing high cost, access restrictions, and model lock‑in, while offering easy setup, multi‑provider support, and advanced Discord/Telegram bot features that have earned it over 16,000 GitHub stars.
The qwen2API project offers a FastAPI backend and React+Vite frontend that expose the Qwen web chat as OpenAI Chat Completions, Anthropic Messages, and Gemini GenerateContent interfaces, featuring tool calling, image generation, account pool management, multiple deployment options, and various execution engines.
free-claude-code, which amassed over 16 000 stars in a week, offers a zero‑cost local proxy that solves Claude Code’s high API fees, access restrictions, and model lock‑in by routing requests to free or low‑price LLM providers, supports six providers, provides a quick 10‑minute setup, and adds advanced features like Discord bots and interactive model selection.
This article walks through a complete production‑ready FastAPI setup, covering secure OIDC/JWKS authentication, Redis‑backed token‑bucket rate limiting, zero‑downtime rolling deployments on Docker/Kubernetes, and observability best practices such as request‑ID middleware and structured JSON logging.
The article explains why streaming output is needed for LLMs, describes LangGraph's five stream modes, provides minimal code examples, compares the modes, shows asynchronous streaming with FastAPI, and outlines a practical streaming chatbot workflow.
The article demonstrates how to eliminate repetitive CRUD code in FastAPI projects by creating a type‑safe, generic repository using Python generics and SQLAlchemy, showing a concrete abstract base class, concrete implementations, custom filters, error handling, and real‑world metrics that cut repository code from hundreds to a few dozen lines.
This tutorial extends the previous FastAPI example by adding Redis caching and Celery asynchronous tasks, showing the project layout, a complete docker‑compose configuration, required Python packages, sample FastAPI and Celery worker code, startup commands, health‑check setup, and key takeaways.
This article explains FastAPI's type‑hint‑driven design, compares it with Flask and Django, details dependency injection, Pydantic validation, async vs. sync functions, middleware versus DI, and summarizes the key skills acquired over a 14‑day learning roadmap.
This article shows how to use FastAPI's built‑in BackgroundTasks for asynchronous notifications, creates a Dockerfile to containerize the app, and demonstrates production‑grade deployment with fastapi run, Uvicorn workers, and Gunicorn, while noting when to switch to Celery for heavier tasks.
After eight days of isolated concepts, Days 9‑12 guide you through assembling a functional FastAPI Todo API—setting up Alembic migrations, implementing CRUD endpoints with JWT authentication and user‑level permission, and writing pytest‑httpx tests that yield a complete project with 47 passing tests.
The article explains why a FastAPI backend should move from a single main.py file to a responsibility‑layered architecture, detailing the recommended directory layout, pydantic‑settings configuration, SQLModel SQLite integration, APIRouter modular routing, and the resulting test coverage.
This article analyzes the fundamental conflict between stateful AI agents and the inherently stateless, distributed nature of modern web services, explores time, state, and execution model mismatches, and presents a practical Agent‑as‑API solution using FastAPI, Redis, SSE, and Kubernetes to achieve scalable, fault‑tolerant deployments.
This tutorial walks through building a full registration and login system in FastAPI, covering bcrypt password hashing, JWT token creation with expiration, dependency-injected authentication helpers, protected endpoints, and a suite of passing tests, demonstrating a clear and practical approach to backend authentication.
This article shows how FastAPI's dependency injection can centralize authentication, permission checks, database connection lifecycles, and pagination logic, removing duplicated code across endpoints by defining reusable dependencies, sub‑dependencies, and yield‑based resources, with concrete code examples and test results.
This article shows how FastAPI's response_model lets you define distinct input and output Pydantic models to hide sensitive fields like passwords, outlines common CRUD scenarios, demonstrates partial updates with PATCH and exclude_unset, and explains why extracting models into a dedicated file improves code organization.
This article shows how FastAPI combined with Pydantic lets you define schema‑driven validation—including nested models, Field metadata, and separate input/output models—so you can drop manual if‑else checks and rely on automatic 422 responses and generated OpenAPI docs.
This tutorial explains how FastAPI uses Python type hints together with Path() and Query() to automatically extract, type‑convert, and validate both path and query parameters, generating OpenAPI docs and standard 422 errors without manual parsing.
This guide shows how to install FastAPI with a single pip command, create a minimal four‑line API, run it instantly with the built‑in dev server, explore the automatically generated Swagger UI, and understand the clear separation between development (fastapi dev) and production (fastapi run) commands.
This article walks through a complete engineering solution for Dify knowledge‑base file handling—covering the upload workflow, FastAPI backend, MinIO storage, observable logging, common integration pitfalls, and practical strategies to achieve a reliable, traceable, and scalable pipeline.
This guide shows how FastAPI’s built‑in CLI commands and the official full‑stack template streamline moving from a simple demo to a production‑ready service, cutting setup time, reducing errors, and unifying development and deployment workflows for Python API projects.
Discover ten essential Pydantic V2 techniques—including strict mode, field constraints, separate create/update/response models, cross‑field validators, custom error handling, reusable types, forbidden extra fields, nested models, computed fields, and discriminated unions—to prevent subtle bugs and ensure robust, secure FastAPI APIs in production.
This article walks through a complete 7‑stage Superpowers workflow—brainstorming, isolated git worktrees, fine‑grained task planning, sub‑agent execution with two‑stage reviews, strict test‑driven development, global code review, and final branch finishing—using Python 3.11, FastAPI, SQLAlchemy and Pytest to implement robust SKU inventory deduction in an e‑commerce system.
This article dissects Open WebUI’s full‑stack architecture—covering its SvelteKit front‑end, FastAPI API gateway, Pipe plugin system, storage choices, model adapters, production‑grade configurations, common pitfalls, and a deployment checklist—providing a practical guide for building robust AI conversational interfaces.
The article shows how Pydantic’s default type coercion can silently accept malformed data—dangerous for payment APIs—and presents ten advanced techniques, including strict mode, field constraints, separate create/update/response models, cross‑field validators, custom error handling, reusable types, extra‑field forbidding, nested models, computed fields, and discriminated unions, to enforce robust FastAPI request validation.
This article dissects DeerFlow 2.0’s architecture, detailing its TypeScript‑React frontend, Python‑LangGraph backend, FastAPI interface, the deerflow‑harness core, agent and skill scheduling mechanisms, three collaboration modes, and how it compares to OpenClaw.
This article walks through the design and implementation of a production‑ready Web AI agent that uses FastAPI as the HTTP layer, LangGraph to orchestrate multi‑step reasoning, and MCP to expose external tools, showing how to manage state, integrate multiple LLM providers, and extend the system with persistence, rate‑limiting, and monitoring.
This article explains why rigorous data validation is crucial and presents eleven practical Pydantic v2 techniques—including strong typing, boundary validation, separating validation from conversion, composing small models, using Annotated and RootModel, enforcing immutability, handling circular references, writing clear errors, keeping business logic out of models, and validating all external data—to make Python code more robust and maintainable.
This article walks Python developers through designing high‑concurrency back‑end systems, covering architectural planning, modular project layout, async I/O with asyncio and FastAPI, load balancing with Gunicorn, database scaling, Celery task queues, caching, rate limiting, monitoring, and graceful shutdown techniques.
AutoClip is an open‑source, locally‑run tool that uses Alibaba's Qwen large language model and OpenAI Whisper to automatically download, transcribe, analyze, and cut high‑light segments from YouTube or Bilibili videos, offering real‑time task monitoring, smart collections, preview, Docker deployment, and a roadmap of future AI‑driven features.
This article walks through practical techniques for designing a highly concurrent Python backend that stays stable under load, covering architecture planning, async programming, load balancing, database scaling, distributed tasks, caching, rate limiting, monitoring, and graceful shutdown.
After struggling with Flask's heavyweight middleware and limited plugin ecosystem, the author switched to FastAPI and demonstrates how seven extensions—FastAPI Users, FastAPI-Mail, FastAPI‑SocketIO, FastAPI‑Limiter, FastAPI‑Cache, FastAPI‑CrudRouter, and FastAPI‑Plugins—dramatically simplify authentication, email, real‑time communication, rate limiting, caching, CRUD generation, and ecosystem integration, turning weeks of debugging into minutes of development.
This article details a lightweight pure‑HTML5 solution for synchronizing PDF documents across participants using PDF.js for rendering and a FastAPI‑based WebSocket layer for real‑time state broadcasting, covering architecture, workflow, code snippets, and synchronization logic.
After fixing unlimited queries and pagination issues, this article reveals why async FastAPI still stalls under load, outlines the hidden bottlenecks in the request lifecycle, and provides practical rules and code examples for offloading heavy work to background workers, ensuring scalability, idempotence, and observability.
FastAPI feels lightning‑fast on small datasets, but returning millions of rows can exhaust memory, block the event loop, and cripple the database; this article explains why that happens and provides concrete design rules—selective fields, pagination, cursor‑based queries, streaming, and chunked processing—to keep APIs stable at scale.
This article walks through the complete design and implementation of an intelligent attendance system that uses face detection, face encoding comparison, and liveness verification with the Python face_recognition library, OpenCV, FastAPI, SQLModel, and WebSocket communication, providing end‑to‑end code samples for backend APIs, database schema, and interactive front‑end pages.
DeepTutor, an open‑source AI tutoring platform from HKU’s Data Science Lab, combines a four‑layer architecture, interactive visualizations, a dual‑loop reasoning engine, and exam‑cloning features, and can be deployed with a single Docker command, offering a guided learning experience that emphasizes thinking over instant answers.
This guide walks through configuring Alibaba Cloud credentials, implementing a FastAPI backend with email function calling, Alibaba OpenSearch, image generation via DashScope, speech recognition, and a responsive HTML/CSS/JavaScript front‑end that supports text chat, image recognition, image synthesis, and voice interaction.
This article compares Django, Flask, and FastAPI—three of the most popular Python web frameworks—by examining their philosophies, built‑in features, learning curves, performance characteristics, and code examples for a simple Hello World service and a user‑query API, then provides a decision flowchart and a detailed matrix to help developers pick the best fit for their next project.
This article walks through the complete design and implementation of a daily news summarizer, covering source selection, web‑scraping with BeautifulSoup, database schema with SQLModel, LLM‑based summarization, FastAPI endpoints, front‑end layout, category/date browsing, and a scheduled update loop.
This guide walks through designing and implementing an AI assistant that connects FastAPI to Alibaba Cloud large‑model services, supports streaming text Q&A, image understanding, text‑to‑image generation, network search, and MCP‑based map queries, with full front‑end and back‑end code examples.
This article explains how FastAPI’s async support, type‑hint integration, automatic OpenAPI docs, and rich ecosystem enable Python developers to build scalable, secure microservices with layered architecture, JWT authentication, performance optimizations, comprehensive testing, Docker/Kubernetes deployment, and structured logging.
The article examines seven new Pydantic v2 capabilities—TypeAdapter, Annotated + Field, deterministic validators, precise serialization hooks, computed_field, RootModel, and ConfigDict—showing how each resolves common API‑validation pain points, improves contract clarity, and boosts performance with concrete code examples and a FastAPI integration.
The author integrates the newly released GLM‑4.7 model into Claude Code, runs three real‑world coding scenarios—including a React dashboard, a FastAPI authentication service, and a refined landing page—and finds that its stability, reasoning, and output quality closely match Claude Sonnet 4.5, positioning GLM‑4.7 as a strong open‑source alternative.
This article demonstrates how to use ByteDance's Trae Solo to automatically generate a FastAPI service that wraps a LangChain/LangGraph weather‑assistant agent, enabling seamless OpenAI‑style access from the Chatbox client, with step‑by‑step code, testing, and deployment details.
This article walks through building a LangChain‑based multimodal RAG system that parses PDFs (both native and scanned), splits them into semantic chunks, stores embeddings in a vector database, and generates answers with precise source citations, complete with code samples and API integration.
This tutorial extends a LangChain 1.0 multimodal RAG project by adding end‑to‑end image analysis and audio transcription features using Qwen3‑Omni, detailing data structures, utility classes, API changes, and Postman testing procedures.
This guide lists ten typical mistakes that new FastAPI developers make—such as using synchronous I/O in async endpoints, ignoring Pydantic models, mishandling database sessions, creating per‑request clients, misconfiguring CORS, monolithic code files, returning raw ORM objects, weak authentication, lacking timeouts/retries for external calls, and skipping testing—and provides concrete solutions with code examples to keep applications fast, reliable, and maintainable.
This article walks through the design and implementation of a multimodal Retrieval‑Augmented Generation (RAG) system using LangChain 1.0, detailing a front‑end/back‑end separated architecture, FastAPI service setup, multimodal data handling, conversation history management, streaming responses, and Postman testing to verify the intelligent Q&A module.
This article compares Flask and FastAPI, explaining their core differences in speed, developer experience, and standards, and provides detailed code examples for installation, routing, async handling, dependency injection, validation, middleware, testing, deployment, and documentation to help developers decide which framework best fits their Python API projects.
Learn how to design and deploy a high‑performance, production‑grade LLM API using FastAPI, covering async routing, type‑safe Pydantic models, streaming via SSE/WebSockets, middleware, caching, rate limiting, observability, retries, and cost‑control strategies for robust AI services.
This article reviews the most popular Python web frameworks of 2024—FastAPI, Django, Flask, Requests, Asyncio, and Django REST Framework—detailing their usage trends, strengths, weaknesses, and ideal use cases, while also highlighting emerging tools and how to choose the right one for your project.
This guide explains how to use industry sector classification to support value investing by scraping sector and stock data, parsing JSON responses, filtering by keywords, and exposing the results via FastAPI services, then displaying them in a Uniapp front‑end table for easy stock selection.
This guide explains why Docker is ideal for deploying large AI models and walks you through every step—from preparing the model and API code, building and running Docker images, to testing, optimizing, and finally deploying the containerized service in production environments.
This article dissects NanoChat’s end‑to‑end LLM pipeline—from a lightweight 561M‑parameter transformer and custom Rust BPE tokenizer to Chinchilla‑scaled training, multi‑task fine‑tuning, optional RL on GSM8K, KV‑cache inference optimizations, and benchmark results that slightly surpass GPT‑2 Large.
This article walks you through building modern Python web applications, starting with Flask fundamentals, advancing to FastAPI’s async capabilities, covering RESTful API design, database integration with SQLAlchemy and Tortoise ORM, testing, deployment, and performance comparisons to help you choose the right framework.
This article dissects the engineering of Tencent's Youtu-GraphRAG, covering its architectural challenges, real‑time FastAPI/WebSocket design, security measures, iterative retrieval chains, parallel processing, intelligent caching, schema‑driven knowledge handling, and performance tweaks, offering practical insights for AI system builders.
Based on a survey of over 30,000 Python developers, the 2025 report shows half are newcomers, AI‑assisted coding is exploding, FastAPI leads web frameworks, most still run legacy versions, Rust‑based tools gain traction, and PostgreSQL remains the dominant database, shaping Python’s future direction.
This report details a university project that built a flexible, high‑performance multi‑agent large‑model framework for ecological environment big‑data analysis, covering system architecture, individual agents, memory mechanisms, report generation, a FastAPI‑LangGraph backend, a React frontend, testing methodology, and future directions.
This guide introduces the n8n‑workflows open‑source collection, offering over 2,000 pre‑built JSON workflow templates categorized into 12 functional groups, and explains how to import them directly into n8n or run a local Python‑SQLite search service for instant workflow discovery.
Discover how replacing FastAPI’s default json module with the high‑performance ORJSON serializer can slash response latency, reduce CPU usage, and boost throughput up to five times—all with a single line of code and without altering existing endpoints.
In just three weeks, I single‑handedly created a production‑grade Retrieval‑Augmented Generation (RAG) API with FastAPI, leveraging Cursor and Claude Code to automate coding, testing, and deployment, and I share practical insights on AI‑assisted development, high cohesion‑low coupling design, TDD, git worktree parallelism, and agent orchestration.
This article explains how to use Python's context manager protocol and FastAPI's lifespan feature to automatically initialize and clean up resources in both standard FastAPI applications and MCP Server, covering implementation methods, code examples, and differences across transport modes.
FastAPI’s async model excels with I/O‑bound tasks but can be crippled by CPU‑heavy or blocking code; this guide explains why, compares built‑in BackgroundTasks and run_in_executor, and shows when to offload work to dedicated queues like Celery or Dramatiq for reliable, scalable processing.
This guide explains how to secure FastAPI applications using OAuth2 with JWT, role‑based access control, refresh‑token workflows, multi‑factor authentication, and integration with external providers such as Auth0, Keycloak, and Firebase.
Learn how to efficiently serve static assets like CSS, JavaScript, and images in FastAPI applications by installing necessary dependencies, organizing a scalable project structure, mounting static directories, rendering templates with Jinja2, and configuring production-ready setups with Nginx or CDN, while avoiding common pitfalls.
This tutorial explains the fundamentals of JSON Web Tokens, their structure, how to encode them with base64Url, and provides a complete FastAPI implementation—including installation, token generation, verification, protected routes, and practical security recommendations—for building robust authentication in distributed systems.
This article explains how to refactor a monolithic dependency‑injection container into separate repository, service, and application containers, aligning with the Single Responsibility Principle, improving maintainability, testability, and limiting change ripple effects within clear architectural boundaries, with full Python/FastAPI code examples.
After noticing API endpoints taking up to a second, the author discovered that misusing FastAPI's Depends() with a get_db() function that recreated the database engine on each request caused severe latency, and by refactoring get_db() into a proper generator that reuses a single engine, response times were roughly halved.
This article introduces three emerging open‑source MCP projects—FastAPI‑MCP for converting FastAPI endpoints, ACI.dev as a one‑click MCP orchestrator with 600+ tool connectors, and Dive, a cross‑platform desktop client—detailing their purpose, key features, usage steps, and GitHub repositories.
This article explains the concept of rate limiting, compares five common algorithms—including fixed‑window, sliding‑log, sliding‑window counter, token bucket and leaky bucket—covers distributed designs using Redis and Lua scripts, and provides complete FastAPI implementation and pytest tests for both fixed‑window and sliding‑log strategies.
This article explains how FastAPI middleware sits between incoming requests and outgoing responses, detailing built‑in and third‑party middleware such as CORS, GZip, HTTPS redirect, session, trusted host, error handling, rate limiting, authentication, custom headers, logging, timeout, trailing‑slash handling, IP whitelisting, proxy headers, CSRF protection, context management and global state, each with usage scenarios and code examples.
This tutorial explains how to integrate Redis caching into a FastAPI application, covering installation, connection handling, cache expiration, testing, cache clearing, and best‑practice considerations to dramatically improve API response times and scalability.
Learn how to create fast, modern Python APIs using FastAPI, automatically generate interactive Swagger documentation, and enhance your endpoints with validation, metadata, and a full-featured to‑do list project, all illustrated with clear code examples and step‑by‑step deployment instructions.
This article explains how FastAPI builds on Starlette, covering the ASGI protocol, Starlette's initialization, middleware design—including ExceptionMiddleware and user-defined middleware—and routing mechanisms, while providing concrete code examples and performance insights for backend developers.
Learn how to quickly create downloadable, professionally styled PDF invoices using FastAPI and the xhtml2pdf library, covering prerequisites, FastAPI setup, HTML template design, PDF rendering, and server launch, plus tips for dynamic data, alternative tools, and security considerations.
This article explains why FastAPI struggles with CPU‑intensive tasks due to Python’s Global Interpreter Lock, describes the types of workloads affected, and provides concrete solutions such as background task queues, microservices, ProcessPoolExecutor, and C/C++ extensions to keep APIs responsive and scalable.
This article walks through a step‑by‑step evolution of backend configuration management—from hard‑coded constants to modular config files, environment‑specific .env files, Pydantic‑validated models, and finally YAML‑based settings—demonstrating how to build a clean, secure, and scalable setup for FastAPI applications.
Learn how to efficiently configure and manage database connections in FastAPI, covering synchronous and asynchronous setups, dependency injection, session handling, and testing strategies, to prevent leaks, boost performance, and ensure scalable, reliable applications.
