#REST vs RPC
90 related articles
·4 min
Complete Guide to Pi Coding Agent: Core Usage and Extension Techniques for a Minimalist Terminal AI Coding Tool
Complete guide to Pi coding agent: design philosophy, installation, shortcuts, session management, and 7-layer customization architecture. How this 45K-star minimalist terminal tool redefines AI coding workflows.
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·4 min
WebMCP in Practice: How MakeMyTrip Is Reshaping the Travel Booking Experience
India's largest OTA platform MakeMyTrip uses WebMCP to standardize AI Agent interactions with web apps, replacing fragile DOM scraping with natural language-driven test automation and simplified complex booking scenarios.
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·4 min
WebMCP in Practice: How MakeMyTrip Is Reshaping the Travel Booking Experience
India's largest OTA platform MakeMyTrip uses WebMCP to standardize AI Agent interaction with web apps, solving DOM scraping fragility, enabling natural language test automation, and simplifying complex international flight bookings.
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·3 min
Cross-Region Packet Loss Monitoring in Practice: Tool Selection and Deployment Guide
A systematic guide to cross-region packet loss monitoring covering core challenges, tool comparison (MTR, SmokePing, PRTG, Zabbix, ThousandEyes), and a self-hosted deployment solution using Prometheus + Grafana.
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·6 min
Hands-On with Panel AI On-Premises Deployment: One-Click Enterprise Adoption and Unified Compute Cluster Scheduling
In-depth review of Panel AI v1.1.1: second-level installation, no-public-IP networking, batch compute cluster management. Learn how enterprise AI on-premises deployment barriers are dramatically lowered.
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·5 min
From Vibe Coding to AI Engineering: A Complete Guide to Enterprise-Grade Development
In the AI programming era, Vibe Coding alone can only build toys. This article deeply analyzes the complete engineering path from Vibe Coding to SDD spec-driven development, covering Claude Code and Codex tool selection, the SuperPower plugin, and domestic LLM comparisons.
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·6 min
The AI Builder's Handbook: A Complete Guide to LLM Principles, RAG Architecture, and Agent Design
From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.
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·2 min
FastAPI Fundamentals: Frontend-Backend Separation and RESTful Design Explained
Learn the core concepts behind FastAPI: frontend-backend separation, API interface design, and RESTful specification. Master resource-oriented design before writing your first line of code.
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Fuse: An Open-Source MCP Tool Built to…
·2 min
Fuse: An Open-Source MCP Tool Built to Optimize Claude Code for C# Codebases
Fuse is an open-source MCP/CLI tool that improves Claude Code's performance on large C# codebases using Roslyn-powered semantic queries to reduce token usage.
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·3 min
ByteDance Interviewer Deep Dive: How Vibe Coding Is Reshaping Frontend Engineers' Core Competitiveness
A ByteDance interviewer breaks down the 3-layer Vibe Coding interview framework: AI tool awareness, complex product engineering, and a 1-hour full-stack challenge. Architectural thinking wins.
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·3 min
Build an MCP Server in 10 Minutes: Let AI Call Your Code Directly
MCP (Model Context Protocol) is the open standard for AI tool integration. Build your own MCP server with ~20 lines of Python. Learn tools, resources, prompts, and both local and remote deployment.
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GitHub Daily · July 17: AI Coding Infr…
·3 min
GitHub Daily · July 17: AI Coding Infrastructure Explodes — From Context Graphs to Vector Indexes
AI coding infrastructure explodes on GitHub: context management, code graphs, and vector indexes become the new battleground as the community shifts from apps to underlying capabilities.
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·3 min
Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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·2 min
MCP vs. Skills: A Clear Breakdown of AI Agent Architecture Layers
MCP and Skills aren't alternatives — they occupy different layers of AI Agent architecture. This article breaks down Function Call, MCP, and Skills to clarify each layer's role.
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·5 min
The Definitive Guide to Claude Code: Core Methodology and Best Practices for Agentic Programming
A deep dive into Claude Code, the definitive course from DeepLearning.AI and Anthropic: from agentic principles and context optimization to three hands-on cases—RAG chatbot, Figma-to-frontend, and data analysis. Master AI-assisted coding methodology.
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·4 min
MCP Protocol Deep Dive: Its Relationship with Function Calling and Agents, Plus a Practical Guide
Deep dive into MCP (Model Context Protocol): clarifying the three-layer relationship between MCP, Function Calling, and Agents, covering protocol roles, call flows, transport choices, and production security for AI developers.
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·4 min
A Deep Dive into ASP.NET Core: The Complete Guide to Microsoft's Cross-Platform Web Framework
A comprehensive analysis of ASP.NET Core's architecture and technical strengths: cross-platform deployment, high-performance Kestrel server, modular middleware, built-in DI, and modern web capabilities like Blazor, gRPC, and SignalR.
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·4 min
Zhipu GLM-5.2 Released: China's First Open-Source Model to Cross the 'Primary Model' Threshold
Zhipu releases GLM-5.2 with 1M token context, matching GPT-5.x and Claude. Zcode 3.0 offers 3M free daily calls with one-click migration from Claude Code and Codex.
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·6 min
Hands-On With Hermes Agent's Eight Major Updates: A Complete Guide to AI Automation and Self-Evolution
The Hermes Agent gets a major upgrade with eight new features: native iMessage, parallel background sub-agents, Unreal Engine MCP support, a self-evolving Skill Hub, and more. A hands-on breakdown of the core changes and their real impact on personal AI automation workflows.
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·6 min
The Eight-Step Method for Agent Development: A Practical Path from Pitfalls to Production
Many enterprises fail at AI Agents due to choosing the wrong tools and lacking methodology. This article outlines an eight-step Agent development method—from cognitive foundations, scenario selection, hand-writing ReAct, and structured output to Tool Use, RAG, evaluation sets, and production fallback.
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