#api-design (41)
- 8 Search APIs for Agentic AI RAG Systems: Complete Comparison 2026
Comprehensive comparison of top search APIs for agentic AI RAG systems in 2026 - features, performance, pros & cons, and when to use each for optimal agent performance.
- gRPC vs REST: Complete Comparison 2026
Comprehensive comparison of gRPC and REST - features, performance, pros & cons, and when to use each for modern API communication.
- Exposing Your Agent: Building API Endpoints with AgentRouteHandler
Learn how to expose your Flue agents as robust API endpoints using AgentRouteHandler, enabling real-world integration and interaction for production AI systems.
- LLM API Pricing Models: Complete Comparison 2026
Comprehensive comparison of leading LLM API pricing models, including cost structures, token pricing, usage tiers, hidden fees, and optimization strategies for developers.
- Build Custom Connectors for Trigger.dev and the MCP
Build custom Trigger.dev connectors and utilize the Managed Connector Platform to integrate with any external service, enhancing your AI workflows.
- Choosing Synchronous or Asynchronous Service Communication
Learn to choose between synchronous and asynchronous communication patterns in distributed systems to build scalable, resilient, and performant applications.
- Smart Home Integration and Action Execution
Integrate your on-device AI agent with smart home systems to execute real-world actions using local APIs and tiny LLMs for intent mapping.
- Build Context-Aware Systems with MCP and TypeScript
Master the Model Context Protocol to design, implement, and deploy robust context-aware systems using the official TypeScript SDK effectively.
- Build a TypeScript MCP Server for AI Context
Build a TypeScript MCP server to define and resolve dynamic context, empowering intelligent AI tools with relevant, real-time data.
- MCP Extensions: Diving into MCP Apps and Crafting Custom Solutions
Explore MCP extensions, focusing on the MCP Apps specification, and learn to design and implement custom context solutions for dynamic tool interaction.
- Build a TypeScript MCP Server for AI Context
Build a TypeScript MCP server to define and resolve dynamic context, empowering intelligent AI tools with relevant, real-time data.
- MCP Extensions: Diving into MCP Apps and Crafting Custom Solutions
Explore MCP extensions, focusing on the MCP Apps specification, and learn to design and implement custom context solutions for dynamic tool interaction.
- Integrate AI Agents with Tools using Model Context Protocol (MCP)
Learn to integrate AI agents with external tools using the Model Context Protocol, covering tool schemas, registration, and practical applications.
- Powering AI Agents with Large Language Models
Learn to integrate Large Language Models into AI agents, enabling them to reason, plan, and make decisions through effective API interactions.
- Designing AI APIs: Seamless Integration for Intelligent Services
Learn how to design robust, scalable, and secure APIs for AI-powered applications, covering integration patterns, communication protocols, and best practices for intelligent services.
- How Netflix Builds Scalable and Resilient Systems
Readers will understand Netflix's distributed system architecture, including its microservices, cloud infrastructure, and fault tolerance strategies for extreme scale.
- Personalization & Recommendations: The Brain Behind Your Feed
Explore the complex architecture behind Netflix's personalization and recommendation systems, including data flows, model ensembles, and design tradeoffs for delivering a unique user experience.
- 9. Integrating Modern Frameworks and Backend APIs
Discover how to seamlessly integrate modern frontend frameworks like Next.js with backend APIs and serverless functions on Void Cloud, building full-stack applications with confidence.
- 15. Project 1: Building a Full-Stack Web Application
Build and deploy your first full-stack web application on Void Cloud, combining a modern frontend with serverless backend functions for a truly scalable solution.
- 16. Project 2: Crafting a Scalable AI-Powered API
Build a scalable, AI-powered API on Void Cloud. Learn to integrate AI services, manage secrets, and deploy a robust backend with automatic scaling and observability.
- Building RESTful APIs with Node.js
Readers will learn to design, implement, secure, and optimize RESTful APIs with Node.js, preparing them for backend engineering challenges and interviews.
- Connect iOS Apps to APIs Using URLSession and Swift Concurrency
Learn to fetch data from APIs using URLSession, decode JSON with Codable, and manage asynchronous operations with Swift's async/await concurrency.
- Integrate AI Agents with Enterprise CRM, Knowledge Bases, & Backends
Readers will learn to integrate AI agents with enterprise CRM, knowledge bases, and backend services, enabling real-time data fetching and record updates.
- Security Best Practices for React Applications
Learn essential security best practices for React applications, including protecting against XSS and CSRF.
- Understanding Large Language Models (LLMs) & AI APIs
Learn how to interact with Large Language Models using AI APIs in Python, setting the foundation for building intelligent applications.
- Diving into Puter.js Core APIs - The Foundation
Learn the Puter.js Core APIs to build interactive and robust applications for the Puter.js Web OS.
- File System Access - Reading, Writing, and Managing Data
Learn how to use Puter.js's File System API for reading, writing, and managing data in your applications.
- Deploying to AWS ECS Fargate & Secrets Management
Deploy a Node.js API to AWS ECS Fargate with secrets management for production readiness.
- Node.js Backend: Build & Deploy Production APIs with Docker & AWS
Design, develop, and deploy scalable Node.js backend applications, integrating authentication, databases, Docker, and AWS for production readiness.
- The LangExtract API: Core Functions and Parameters
Learn how to use the LangExtract API for structured information extraction with Python.
- Server-Side API Security: REST, GraphQL, and Beyond
Learn the principles of securing server-side APIs, including authentication, authorization, input validation, and rate limiting.
- Build Flexible LLM Apps with Mozilla's any-llm Python Library
Learn to use Mozilla's any-llm Python library to integrate diverse LLM providers, reducing vendor lock-in and simplifying your AI application development.
- Core Concepts: Prompts, Completions, and Parameters
Explains the core concepts of prompts, completions, and parameters in Large Language Models.
- Getting Started with any-llm
Learn how to use the any-llm library to interact with various large language models easily and efficiently.
- Project 1: Building a Type-Safe REST API with Node.js
Learn to build a type-safe REST API with Node.js and Express.js using TypeScript.
- Project: Containerizing a Web Application (Frontend + Backend)
Learn how to containerize a full-stack web application using Docker and docker compose.
- Initializing the Spring Boot 3.3 Project
Learn how to initialize a new Spring Boot project and create a basic REST endpoint.
- Building the RESTful API with Spring Web
Learn how to build a RESTful API with Spring Web for a basic To-Do List application.
- Securing Your API with Spring Security 6
Learn how to secure your API with Spring Security 6, including user authentication and authorization using JWTs.
- Project: Interacting with an API
Learn how to interact with APIs using Python, including making GET requests and understanding JSON data.
- Intermediate Topics: JSON Schema and Validation
Learn how to use JSON Schema for data validation and consistency in AI applications.