最新博客文章
34 / 200AI 开发实战教程，从零到一构建你的 AI 应用。
All200
Beginner32
Intermediate92
Advanced76
All
Prompt Engineering12
RAG Retrieval Augmented17
Agent Development34
Model Fine-tuning21
Deployment and Operations25
AI Application Development40
DeepSeek API16
Multimodal Development6
AI Safety6
Model Inference3
Retrieval Systems1
RAG Retrieval1
Model Architecture3
API Development1
Agent Development10
Model Deployment1
Multimodal1
Model Evaluation2
Tutorial
Date
Reflection and Self-Correction: Doubling AI Output Quality
Agent Development
Intermediate
Reflection is a key technique to improve the quality of AI output. This article explains how to enable AI to self-evaluate, discover errors, and iteratively correct them, significantly improving output quality through a reflection loop.
Agent Security and Permission Control
Agent Development
Intermediate
When AI agents can autonomously call tools, access databases, and execute code, security becomes critical. This article explains agent security architecture design, including sandbox isolation, permission models, audit logs, and injection protection.
MCP Server Development in Practice: From Protocol to Production-Grade Tool Service
Agent Development
Intermediate
MCP is becoming the standard protocol for AI tool invocation. Starting from protocol principles, this article walks you through implementing a production-grade MCP server, covering tool definition, authentication, error handling, streaming responses, and deployment operations.
Plugin Lifecycle & Hot Reload: Fiber State Machine, Nested Plugins, dispose
Agent Development
Advanced
How plugins load and unload: Fiber state machine transitions, nested plugin parent-child isolation, and how old instances settle during hot reload with dispose.
DeepSeek Harness Built-in Plugins Guide: Categories, Selection & Disabling
Agent Development
Intermediate
Explore dsh built-in plugins by capability: files, Shell, Sandbox, Skills, subagents, workflows, sessions, UI & telemetry. Learn config, selection, and disabling.
DeepSeek Harness Explained: Agent = Model + Harness, Everything Is a Plugin
Agent Development
Beginner
DeepSeek Harness (dsh) is DeepSeek's MIT-licensed Agent runtime. Explore Agent = Model + Harness, its plugin core, four run modes, and how it differs from classic Agent frameworks.
Introduction to the MCP Protocol: Building Standard Interfaces for AI Tool Calls
Agent Development
Beginner
MCP (Model Context Protocol) is the hottest AI tool calling standard protocol in 2026. This article explains the MCP protocol principles from scratch, Server construction, Client integration, and a complete integration solution with DeepSeek API.
Design of Multi-Agent Communication Protocols
Agent Development
Advanced
When multiple AI agents need to collaborate, communication protocols are the key link connecting them. This article explores the design principles, message formats, routing strategies, and consistency guarantees of multi-agent communication protocols.
DeepSeek Harness Plugin DI, Effects & Service Model
Agent Development
Advanced
Inside the dsh plugin kernel: inject dependency timing, ctx.effect auto-tracking and reverse cleanup, and Service registration. Practical patterns for dependency cycles, optional deps, and service replacement.
AI Agent Architecture Design: From ReAct to Plan-and-Execute
Agent Development
Intermediate
AI Agent is one of the hottest technology directions currently. This article provides an in-depth analysis of mainstream Agent architectures such as ReAct, Plan-and-Execute, and Multi-Agent, helping you understand the applicable scenarios and design principles of different architectures.
Function Calling in Practice: Let AI Invoke External Tools
Agent Development
Beginner
Function Calling is the bridge connecting AI to the real world. Starting from scratch, this article explains the principles, implementation methods, and common application scenarios of Function Calling, enabling AI to query databases, send emails, and call APIs.
Multi-Agent Collaboration Patterns in Practice: From Orchestration to Autonomy
Agent Development
Advanced
Single agents have limited capabilities; multi-agent collaboration is key to unlocking complex tasks. This article starts from practical experience, compares mainstream collaboration modes such as Supervisor, GroupChat, and Sequential, and provides runnable code implementations.
DeepSeek Agent Development Tutorial
Agent Development
Intermediate
Complete tutorial for building AI agents based on DeepSeek: ReAct mode, tool calling, memory management, and multi-agent collaboration. Includes practical code for LangChain Agent, AutoGPT-style autonomous agents, and Function Calling Agent.
Define Tools in DeepSeek Harness: Schema, Approval & Pipeline
Agent Development
Advanced
Register a tool with ctx and make models call it reliably: parameter schema, approval hooks, return values, and the fixed dsh tool pipeline order.
Agent Skills Guide: SKILL.md, Progressive Disclosure & Scripts
Agent Development
Intermediate
A complete guide to Agent Skills: SKILL.md YAML metadata, progressive disclosure and Token budget, deterministic scripts vs prompts, MCP boundaries, and security audit.
Agent Testing and Debugging Strategies
Agent Development
Intermediate
The non-deterministic behavior of agents makes traditional testing methods ineffective. This article introduces specialized testing strategies for agents, including evaluation-driven testing, observability debugging, and CI/CD integration solutions.
Multi-Agent Collaboration: Building an AI Team
Agent Development
Advanced
A single Agent has limited capabilities; multi-Agent collaboration can solve more complex problems. This article explains the design patterns, communication mechanisms, and practical application cases of multi-Agent systems, teaching you to build AI teams that can work collaboratively.
Design of Agent Memory Systems
Agent Development
Advanced
Memory is the key to transforming agents from one-time tools to long-term intelligent companions. This article deeply analyzes the architecture design of agent memory systems, covering short-term/long-term memory, RAG-enhanced memory, and hybrid retrieval strategies.
Sessions, Turns & Trajectory Replay: DeepSeek Harness Observability
Agent Development
Intermediate
Inside dsh's session data model: session/turn/step hierarchy, append-only event logs, and what the Trajectory view reveals for recovery, forking, and cost attribution.
Agent Protocols Explained: Where MCP, A2A, and AG-UI Each Belong
Agent Development
Advanced
Three agent protocols answer three different questions. MCP standardizes how tools and context are exposed, A2A covers discovery and delegation between agents, AG-UI streams a running agent to the frontend. It also shows a bridge in code — a remote A2A agent wrapped as an MCP tool — plus capability negotiation, version drift, mismatched error semantics, and cancellation.
Agent Memory System Design: Short-Term, Long-Term, and Tool Memory
Agent Development
Advanced
Memory is the foundation of Agent intelligence. This article models four layers: working, episodic, semantic, and tool memory, covering compression, retrieval, writing, and forgetting strategies. Implement a complete Agent memory subsystem with vector memory and reflection using DeepSeek API.
Capability Seams in DeepSeek Harness: Build Custom Agent Extensions
Agent Development
Advanced
Learn how Capability Seams extend dsh: definer, provider, and consumer roles, three-layer events in the Agent loop, and what to deliver when building a custom capability.
Build Your First DeepSeek Harness Plugin: apply, ctx & cordis.yml
Agent Development
Intermediate
Write a runnable dsh plugin from scratch: TypeScript module exporting name and apply, ctx registration semantics, scratch-plugin layout, and loading it via cordis.yml insert and --patch.
Multi-Agent Collaboration: Building an AI Team with Skill Chains
Agent Development
Advanced
A single AI has limited capabilities, but collaboration among multiple AI roles can solve complex problems. This article explains how to orchestrate multiple Agents with skill chains, building a collaborative team of planner → executor → reviewer.
Long-Running Agent Engineering: Task Graphs, Resumption, and Cost Control
Agent Development
Advanced
When an Agent works for hours, the bottleneck is not how long the model thinks but whether state survives a process restart. Task graph persistence, checkpoint granularity by side-effect density, idempotency with intent logs, lease-guarded resumption, precondition fingerprints for invalidation, context compaction, externalized memory, and cost gates with human checkpoints.
AI Agent Tool Development in Practice: From Design to Deployment
Agent Development
Intermediate
Tools are the hands and feet of agents. This article provides an in-depth explanation of how to design, develop, test, and deploy high-quality AI agent tools, including tool interface design, error handling, performance optimization, and security protection.
Agent Loop Basics: Understanding AI Autonomous Workflows from Scratch
Agent Development
Beginner
Agent Loop is the hottest AI development paradigm today. Starting from the ReAct pattern, this article helps you understand how AI achieves the autonomous loop of observation → planning → execution → feedback, and master the core concepts of Agent development.
Agent Reliability Engineering: Timeouts, Retries, Idempotency, and Fault Isolation
Agent Development
Advanced
Treat an Agent as a distributed system that will fail. This guide covers inheriting a wall-clock budget as context rather than resetting it per layer, why retries require idempotency, binding idempotency keys to intent, compensating irreversible side effects with a Saga ledger, and proving these mechanisms with chaos tests.
SKILL.md Guide: Fields, 25+ Platform Paths & Token Budget
Agent Development
Intermediate
A practical SKILL.md handbook: YAML frontmatter rules, install paths for 25+ platforms, progressive loading Token budgets, and Skill vs MCP roles.
Hands-on: Build Your First Agent Loop Application
Agent Development
Advanced
From theory to practice, this article teaches you step by step how to build a complete Agent Loop application using the DeepSeek API. It includes the complete process of requirements analysis, task planning, step-by-step execution, and result verification.
Tool Calling Loop: Enabling AI to Use External Tools
Agent Development
Intermediate
Function Calling is a key capability of Agent Loop. This article explains in depth how AI autonomously selects tools, calls APIs, and parses results to build truly capable AI assistants.
In-Depth Implementation of ReAct Agents
Agent Development
Advanced
ReAct (Reasoning + Acting) is one of the most classic paradigms in agent development. This article explains in depth how to build a production-grade ReAct agent, from principles to implementation, including prompt engineering, tool management, and error recovery.
Agent State Machine and Fault-Tolerant Orchestration: Building Reliable Agents
Agent Development
Advanced
In real business, agents fail: tool timeouts, context overflow, loop deadlocks. This article models the agent lifecycle with a finite state machine and explains the complete implementation of fault-tolerant orchestration, including timeout circuit breaking, retry compensation, and session recovery.
Scope Isolation & Event System in DeepSeek Harness Plugins
Agent Development
Advanced
Learn dsh scope semantics and the event bus: auto-isolated registrations, cross-plugin communication via services or events, naming and payload conventions, listener leak fixes.
