Turned 10,000 Code Files into a Wiki in Just a Few Games’ Time

Understanding Massive, Aging Projects — Without the Pain

Programmers have long struggled to understand massive, aging codebases.

Imagine suddenly being handed a project with nearly 10,000 files — how long would it take to truly digest it?

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The AI Boost for Code Understanding

Fortunately, a domestically developed AI programming tool can now help you cut through the complexity.

Let’s take a real-world test using Microsoft’s `terminal` repository on GitHub.

The repo has about 7,500 files in C++, C#, JSON, XML, and Markdown.

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Step 1 — Clone the Repository

git clone —depth=1 https://github.com/microsoft/terminal.git

Step 2 — Generate the Repo Wiki

Import the project into the AI tool → Click Repo Wiki → Click Generate.

You can even leave it running while you play a game — by the time you’re back, you’ll have a complete, auto-generated Wiki.

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Key Features

• Organized architecture in both text and diagrams.
• Shared Mode for team-wide access.
• Ideal for analyzing unfamiliar or uncommented codebases.

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Meet Qoder — A New AI Programming Powerhouse

Launched in August, Qoder quickly claimed three top spots in AI programming:

• Strongest model — World-class + proprietary models, outperforming competitors with a 13.22% higher effect score.
• Most complete coverage — IDE, CLI, JetBrains plugins, one account for all.
• Best cost-performance — 205% more tasks for the same cost compared to peers.

> As founder Shuton explains — Qoder focuses on the 95% of dev time spent on real-world software (long-lived, value-generating, commercially-critical codebases) rather than “cool demos.”

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Our Test Candidate — Spring Petclinic

Spring Petclinic is a small but complete enterprise-grade Java example:

• Covers MVC, JPA, Thymeleaf
• Includes unit testing, database migration, exception handling

Clone Petclinic

git clone https://github.com/spring-projects/spring-petclinic.git

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Test 1 — IDE Mode

Accurately understanding code context is fundamental.

• Open Petclinic in Qoder IDE.
• Open a Java file (e.g., `OwnerController.java`).
• Use "Intelligent Q&A" to ask:

> Which Service and Repository are used by the `findAll` method in this controller?

Result: Qoder traces `OwnerService → OwnerRepository` and explains the full call chain.

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Model Selector

Four graded modes:

• Lite
• Efficient
• Performance
• Auto (Intelligent Routing)

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Quest Mode — Spec-driven Autonomous Programming

Example spec:

> Add a RESTful API: `GET /api/vets/specialties`

> Return all specialties via JPA & Spring Data REST, with unit tests.

The AI:

• Designs requirement docs.
• Executes tasks automatically.
• Produces an overall summary.

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Test 2 — JetBrains Plugin Mode

For backend developers using JetBrains IDEs:

Example Q&A:

> How to add pagination to `/owners/{ownerId}`?

Debugger Integration:

• Set breakpoint in `findowner`.
• Ask:

> What object does `ownerRepository.findById` return?

Qoder understands debug context without copy-pasting variables.

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Database Safety Check:

> Is `findByLastName` vulnerable to SQL injection?

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Memory Awareness — Qoder adapts to your coding style and problem patterns over time.

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Test 3 — CLI Mode

Install via:

curl -fsSL https://qoder.com/install | bash

Restart terminal, run:

qodercli

Example bug fix:

> There’s a bug in Vet.java — find and fix it.

GitHub Integration:

• Use `@qoder` in PRs/Issues to request explanations or fixes.
• Config `.github/workflows/` for auto code reviews.

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Summary of Findings

Qoder’s strengths:

• IDE + JetBrains + CLI coverage.
• Enhanced Context Engineering (+13.22% accuracy).
• Memory awareness (+40% dialogue quality).
• Top-tier models, cost efficiency: 205% more tasks per budget.

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Durability Evaluation Set

Open-sourced here:

https://github.com/agentic-coding-durability-evalset

Covers:

• Python, JS, TS, Java, Golang
• Mainstream + recent frameworks
• Complex, real-world scenarios

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Try Qoder

Qoder:

https://qoder.com/

Durability Set:

https://github.com/agentic-coding-durability-evalset

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Related AI Ecosystem — AiToEarn

For content creators integrating AI across platforms, AiToEarn官网 offers:

• AI generation tools
• Cross-platform publishing (Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X)
• Analytics & AI model ranking

Explore:

• AiToEarn博客
• AI模型排名

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Bottom line — Qoder is pushing boundaries in real-world AI programming, while AiToEarn is doing the same in multi-platform AI content workflows.

Both show how AI tools are evolving from assistant to autonomous agent for complex, high-value work.