What is Harness Engineering? Definition: Harness Engineering is the discipline of designing constraints, feedback loops, tool systems, and verification mechanisms around AI agents. This definition sounds very academic, so let’s understand it through a vivid metaphor: Harnessing a Thousand-Mile Horse: A thousand-mile horse (AI Agent) has powerful running capabilities, but without a rider, it might run randomly, injure passersby, or even rush off a cliff. Harness Engineering equips this horse with reins (constraints), brakes (safety controls), whip (incentive mechanisms), and a rider (monitoring), ensuring it travels safely on the correct path.

Scenario: Information Is Correct, But Execution Goes Wrong Let’s start with a real-world story: Background: A company deployed a RAG-based technical documentation Q&A system. This system worked perfectly—when users asked “How to configure Redis cluster?” it could accurately retrieve relevant information from technical documents and provide detailed configuration steps. Problem: When a user asked “Delete temporary files in the test directory,” the system correctly retrieved the right technical documentation, but during execution it mistakenly deleted the entire project’s core code.

The previous articles in this series focused on the architecture and implementation of P2P networks, but the performance bottleneck of P2P applications often lies not in the protocol layer itself, but in the underlying transport protocol — TCP congestion control. A single BitTorrent node may maintain hundreds of concurrent TCP connections, each independently performing congestion control. Choosing the wrong congestion control algorithm can severely degrade bandwidth utilization, especially on high-latency links or links with random packet loss. Understanding the evolution of congestion control not only helps P2P developers optimize transport performance but is also essential for gaining a deep understanding of how the internet transport layer operates.

Moving a P2P system from prototype to production involves engineering challenges across connection management, security, observability, and deployment. This article covers six critical areas with reusable code snippets and actionable guidance. Connection Management and Resource Limits P2P nodes must maintain a large number of simultaneous connections. Without resource caps, a node can suffer OOM crashes or file descriptor exhaustion. Production environments require strict control over three dimensions. Three-Layer Resource Control Model mermaid flowchart LR subgraph Transport Layer A1["Max Inbound<br/>Connections: 1024"] --> B1["Connection Queue"] A2["Max Outbound<br/>Connections: 512"] --> B1 end subgraph Stream Layer C1["Inbound Concurrent<br/>Streams: 128"] --> D1["Stream Scheduler"] C2["Outbound Concurrent<br/>Streams: 256"] --> D1 end subgraph Idle Management E1["Connection Idle<br/>Timeout: 30s"] --> F1{"Health Check"} E2["Stream Idle<br/>Timeout: 60s"] --> F1 F1 -->|"Pass"| G1["Update Active Timestamp"] F1 -->|"Fail"| G2["Close Stream/Conn<br/>Release FD & Memory"] end B1 --> C1 B1 --> C2 D1 --> E1 D1 --> E2 style A1 fill:#4CAF50,color:#fff style A2 fill:#4CAF50,color:#fff style C1 fill:#2196F3,color:#fff style C2 fill:#2196F3,color:#fff style E1 fill:#FF9800,color:#fff style E2 fill:#FF9800,color:#fff Rust Connection Management Rust’s SwarmBuilder provides a fluent API for connection configuration:

What is Context Engineering? In June 2025, Andrej Karpathy provided a definition of Context Engineering on the OpenAI engineering blog: “the delicate art and science of filling the context window with just the right information for the model to take the next step.” This definition is exceptionally elegant. The core distinction from Prompt Engineering lies in: Prompt Engineering: Optimizes “what you say” – focuses on how input instructions are expressed Context Engineering: Optimizes “what the model knows” – focuses on what information the model can access Imagine this:

A Frustrating Scenario Imagine this situation: you’re writing a perfect prompt about “the latest best practices for Python MySQL connections.” You carefully design a persona (“You are a Python database expert with 10 years of experience”), clear instructions (“Only provide 2024 best practices, no deprecated methods”), and specific format requirements (“List main approaches, pros/cons, code examples, security considerations”). This prompt is flawless. Yet, GPT confidently gives you code using 2018-deprecated methods with security vulnerabilities. Why?

Combining theory with practice, we’ll build a real distributed file sharing system. This system will leverage technologies introduced in previous articles — Kademlia DHT for node discovery and metadata distribution, Gossipsub for broadcasting, and a custom file transfer protocol. System Architecture mermaid flowchart TD subgraph Application CLI["CLI Interface"] API["REST API"] end subgraph Business Logic Index["File Index Manager"] Scheduler["Piece Download Scheduler"] Verify["Verify & Reassemble"] end subgraph P2P Network Discovery["Kad-DHT<br/>Peer Discovery + Metadata"] Broadcast["GossipSub<br/>Message Broadcast"] Transfer["Custom Protocol<br/>File Transfer"] end CLI --> Index API --> Index Index --> Scheduler Scheduler --> Verify Index --> Discovery Scheduler --> Transfer Broadcast -->|"New Peer Notification"| Index Core design principles:

Introduction In IoT applications, temperature and humidity monitoring is a common and important requirement. The ESP01, as a low-cost, low-power WiFi module, provides a convenient solution for implementing temperature and humidity monitoring. This article details how to use the ESP01 board for temperature and humidity monitoring development, including hardware connections, code implementation, and functional analysis. Hardware Preparation ESP01 Board: Core control module, responsible for data collection and network communication. DHT Temperature and Humidity Sensor: Used to measure ambient temperature and humidity. Dupont Wires: Used to connect the ESP01 and DHT sensor. Connect the DHT sensor’s VCC pin to the ESP01’s 3.3V pin, the GND pin to GND, and the data pin to the designated pin on the ESP01 (defined as DHTPIN in the code).

Overview: How to Analyze a Protocol (MongoDB) Protocol Document Analysis Approach MongoDB Protocol OpCode Reference Table Analyzing the Most Common OpCode OP_MSG Extending Protocol Parsing in DeepFlow Agent DeepFlow Agent Development Document Overview Code Guide Define a Protocol with a Constant Identifier Prepare Parsing Logic for the New Protocol Define the Struct Implement L7ProtocolParserInterface Extending DeepFlow Protocol Collection Using Wasm Plugins Kafka Protocol Analysis Kafka Header and Data Overview Kafka Fetch API Kafka Produce API Kafka Protocol DeepFlow Agent Native Decoding DeepFlow Agent Wasm Plugin Wasm Go SDK Framework Plugin Code Guide Conclusion Native Rust Extension Wasm Plugin Extension Appendix Overview MongoDB is widely used today, but lacks effective observability capabilities. DeepFlow is an excellent solution for observability, but it lacks support for the MongoDB protocol. This article extends DeepFlow with MongoDB protocol parsing, enhancing observability in the MongoDB ecosystem. It briefly describes the process from protocol document analysis to implementing code parsing within DeepFlow.

What is Prompt Engineering? The core definition of Prompt Engineering is: Designing natural language inputs to guide Large Language Model outputs toward specific results. This concept seems simple, but it hides a profound assumption: The same model, different prompts → completely different outputs. Imagine you have an incredibly smart assistant with zero background knowledge. This assistant can perfectly understand and execute any instruction, but it lacks prior knowledge and has no memory. Prompt engineering is the art of learning how to converse with such an assistant.