Master's graduate from Beijing Institute of Technology, published four top‑journal papers, previously worked as a developer at ByteDance and Alibaba. Currently researching large models at a major state‑owned enterprise. Committed to sharing concise, practical AI large‑model development experience, believing that AI large models will become as essential as PCs in the future. Let's start experimenting now!
This article details DeepSeek Harness's general settings including permission policies, model adapters, and plugin architecture, then compares four agent presets—Standard, Minimal, PTC, and Creative—showing their token usage, toolsets, and ideal use cases with a 3D game benchmark.
This tutorial introduces DeepSeek Harness, a plugin-based AI agent framework, covering its design philosophy, quick and source-code installation methods using Node.js and pnpm, and demonstrates building a 3D racing game in one shot compared to multi-iteration alternatives.
This practical guide details seven core data sources for training a cybersecurity LLM—including academic papers, standards like MITRE ATT&CK, CVE/CWE databases, textbooks, community blogs, open datasets, and model distillation via EasyDataset—providing specific acquisition channels and reproducible Python code for each.
The author demonstrates how, within an hour, the Doubao‑Seed‑Evolving large model can generate a playable Flappy Bird with two‑player mode, a 3D city‑racing game, and a 4399‑style game portal, detailing environment setup, prompt engineering, debugging steps, and insights into the model’s strengths and current limitations.
This article dissects DeepAgents Code’s command architecture, explaining the five‑layer framework, slash‑command registration, priority handling via BypassTier, skill and startup commands, and the engineering safeguards that balance interaction efficiency with system safety in production‑grade AI agents.
This article dissects the DeepAgents Code Agent Server, detailing how it solves four production‑grade challenges—model switching, tool registration, session memory, and sub‑agent orchestration—through runtime context, configurable middleware, a unified tool registry, sub‑agent delegation, and extensible middleware such as LocalContext, ResumeState, Memory, Skills, and backend priority mechanisms.
This article provides a comprehensive walkthrough of the DeepAgents Code repository, explaining its client‑server architecture, module organization, technology stack—including DeepAgents SDK, Textual UI, SQLite persistence, and streaming protocol—and the design rationale behind building a production‑ready AI agent framework.
The article traces the origin of Loop Engineering, defines it as an autonomous loop system for AI agents, outlines its evolution from Prompt to Context to Harness Engineering, and explains the two core steps—automated start and verification—along with practical implementation details.
This guide walks developers through six curated GitHub repositories that enable advanced multi‑agent usage of Claude Code, teach the fundamentals of building a custom code‑agent from scratch, and provide deep source‑code analysis for a complete understanding of AI‑powered programming assistants.
The article outlines a progressive learning path for AI agents, recommending six GitHub projects—from a beginner-friendly tutorial to production‑grade frameworks—detailing each project's purpose, difficulty, key takeaways, and suitable audience, helping programmers transition from users to builders.
