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Fun with Large Models
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Fun with Large Models

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!

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Recent Articles

Latest from Fun with Large Models

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Fun with Large Models
Fun with Large Models
Jul 2, 2026 · Artificial Intelligence

DeepAgents Code Agent Server: Production Design, Dynamic Model Switching & Middleware

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.

AI AgentsDeepAgentsLangChain
0 likes · 19 min read
DeepAgents Code Agent Server: Production Design, Dynamic Model Switching & Middleware
Fun with Large Models
Fun with Large Models
Jun 27, 2026 · Artificial Intelligence

Quick Guide to LangChain DeepAgents: Exploring the Production‑Grade DeepAgents Code Framework

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.

AI AgentsArchitectureDeepAgents
0 likes · 14 min read
Quick Guide to LangChain DeepAgents: Exploring the Production‑Grade DeepAgents Code Framework
Fun with Large Models
Fun with Large Models
Jun 23, 2026 · Artificial Intelligence

Loop Engineering Demystified: How Automatic Loops and Validation Work

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.

AI AgentAI engineeringAutomation
0 likes · 7 min read
Loop Engineering Demystified: How Automatic Loops and Validation Work
Fun with Large Models
Fun with Large Models
Jun 11, 2026 · Artificial Intelligence

Master Claude Code with 6 GitHub Projects: From Multi‑Agent Collaboration to Source‑Code Deep Dive

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.

AI programmingClaude CodeDeepAgents
0 likes · 13 min read
Master Claude Code with 6 GitHub Projects: From Multi‑Agent Collaboration to Source‑Code Deep Dive
Fun with Large Models
Fun with Large Models
Jun 9, 2026 · Artificial Intelligence

Master AI Agents: 6 Essential GitHub Projects to Learn From

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.

AI AgentsAgent developmentGitHub
0 likes · 15 min read
Master AI Agents: 6 Essential GitHub Projects to Learn From
Fun with Large Models
Fun with Large Models
May 22, 2026 · Artificial Intelligence

How to Rigorously Evaluate Large Models: Methods and Key Benchmark Datasets

This guide explains why systematic evaluation is essential for large models, outlines three core evaluation approaches—human assessment, benchmark‑dataset testing, and automated judge models—introduces the most widely used benchmark suites, and shows how to use the open‑source EvalScope framework and prompt‑design techniques to conduct reliable model assessments.

EvalScopePrompt Designautomated judge
0 likes · 17 min read
How to Rigorously Evaluate Large Models: Methods and Key Benchmark Datasets
Fun with Large Models
Fun with Large Models
Apr 22, 2026 · Artificial Intelligence

How to Quickly Integrate Agent Skills in LangChain DeepAgents

This article provides a step‑by‑step guide to using Agent Skills in LangChain DeepAgents, covering the Skills directory structure, the four engineering steps (discovery, system‑prompt injection, progressive loading, execution), and two practical examples—a simple skill lookup and a complex docx‑processing skill—complete with code snippets and troubleshooting tips.

Agent SkillDeepAgentsFileSystemMiddleware
0 likes · 15 min read
How to Quickly Integrate Agent Skills in LangChain DeepAgents
Fun with Large Models
Fun with Large Models
Apr 17, 2026 · Artificial Intelligence

Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro

This guide walks you through interpreting training logs and loss curves, diagnosing common issues such as oscillation, under‑fitting, and over‑fitting, and applying concrete adjustments to learning rate, LoRA settings, batch size, and epochs, with scenario‑specific strategies to turn a novice into a tuning expert.

AI trainingLoRAhyperparameters
0 likes · 23 min read
Mastering Large Model Training: Practical Parameter Tuning from Beginner to Pro
Fun with Large Models
Fun with Large Models
Apr 9, 2026 · Artificial Intelligence

Harness Engineering: The Critical Factor That Determines AI Agent Performance

The article explains Harness Engineering, the emerging concept that moves AI agents from simple question answering to reliable task execution by adding constraints, orchestration, observation, and recovery mechanisms, and shows how it builds on Prompt and Context Engineering through layered architecture and real‑world examples from OpenAI and Anthropic.

AI AgentsAnthropicContext Engineering
0 likes · 16 min read
Harness Engineering: The Critical Factor That Determines AI Agent Performance