Dataset: 2026-10-27-catalog-ai-v2
Ruleset: b2e8889c66f7
Scanned: 2026-10-27
Window: February–September 15, 2026

Catalog tracking observation. This repository is measured with the same fixed window and ruleset, but it is not part of the published benchmark or its aggregate claims.

编程导航 AI 开发实战新项目，基于 Spring Boot 3 + Java 21 + Spring AI 构建 AI 恋爱大师应用和 ReAct 模式自主规划智能体YuManus，覆盖 AI 大模型接入、Spring AI 核心特性、Prompt 工程和优化、RAG 检索增强、向量数据库、Tool Calling 工具调用、MCP 模型上下文协议、AI Agent 开发（Manas Java 实现）、Cursor AI 工具等核心知识。用一套教程将程序员必知必会的 AI 技术一网打尽，帮你成为 AI 时代企业的香饽饽，给你的简历和求职大幅增加竞争力。

01Result

liyupi/yu-ai-agent ↗

liyupi/yu-ai-agent AI coding statistics: 0%

of 0 commits in February–September 15, 2026 were committed by an AI agent — a floor, not an estimate

Too little recent activity to publish. Only 0 commits landed in this window, so the percentage is noisy. Repositories below 100 are excluded from the index for this reason.

No signed agent commits found

No agent signatures were found in the last 0 considered commits. That is a real result, not a failure to measure.

It does not mean no AI was used here. A developer can use an agent and commit by hand without leaving a detectable git signature.

agent commits

other considered commits

—

agent contributors

—

largest contributor

02Drivers

What shapes this result

Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.

No agent signatures were detected, so there are no agent, mode, or timing drivers to break down.

03Evidence

Inspect the matches

Public commit samples let you inspect the signatures behind the aggregate result.

No matched commit samples are available for this observation.

04Method

What entered the denominator

The result begins with commit history, removes merges and routine automation, and reports literal agent signatures as a lower bound.

Exclusion ledger

Commits in window	0
Merge commits removed	−0
CI / dependency bots removed	−0
Considered	0

Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.

Continue exploring

Java AI coding statisticsCompare evidence across Java repositories →GitHub AI commit analyzerInspect another repository with the same evidence model →

Reproduce this observation

Run the same detector locally.

Deterministic signature matching; no code-style inference. Squash merges can remove trailers, so this result remains a measured floor.

npx qmmit-cli liyupi/yu-ai-agent

Scan another repository

Try