[music] morning. How's everyone doing. >> Good.>> I'm going to need a lot of energy for this talk, so please back me up. I'm very nervous. Uh but we'll get through this.I'm declaring war on slop today. Let's talk about this. Every AIE has a secret.I I've told this to uh some folks that are personal friends and I'll just show show the secret. Now the first summit we had the secret which was we knew that the AI engineer was going to be a thing. Second summit we extended it to leadership.Third summit we realiz ...

[music] So companies spend millions on AI tools for software engineering. But do we actually know how well these tools work in the enterprise or are these tools just all hype. To answer this and for the past two years, we've been researching the impact of AI on software engineering productivity.And our research is time series because we look at get historical data, meaning we can go back in time. And it's also cross-sectional because we cut across companies. And the way we use to measure most of the of the ...

Code Quality & AI Integration - SonarQube MCP server detects production-grade code quality issues in real-time [1] - AI code generation shifts engineering bottleneck to code review, developers spend 90% of debugging time on AI-generated code [1] - AI reviewers share blind spots with AI generators, lacking proof checking, system behavior validation, and consequence review [1][3] - SonarQube addresses limitations of AI code review by providing enterprise-grade code analysis and instant feedback [1] SonarQube Capabilities - SonarQube processes over 750 billion lines of code daily, identifying various bug patterns [2] - It identifies security vulnerabilities like SQL injection, XSS, and hardcoded secrets [4] - It detects code smells, technical debt, and maintainability issues [4] - It identifies test coverage gaps [4] Implementation - SonarQube MCP server installation is simple and can be added to AI assistant's config [4] - GitHub repository is available [4]

AI Code Generation & Engineering Bottleneck - AI正在以极快的速度生成代码，但工程瓶颈已经从编写转移到审查 [1] - 开发者现在将 90% 的调试时间花在 AI 生成的代码上 [1] MCP Server & Code Quality - MCP服务器可以实时检测生产级代码质量问题 [1]

Core Problem & Solution - AI 代码生成提速，但工程瓶颈转移至代码审查，开发者 90% 的调试时间用于 AI 生成的代码 [1] - AI 代码审查存在盲点，与 AI 代码生成器有相同的根本缺陷 [1] - SonarQube MCP Server 提供企业级代码分析，针对漏洞、代码异味等提供即时反馈 [1] SonarQube Capabilities - SonarQube 每日处理超过 7500 亿行代码，积累了丰富的 bug 模式经验 [2] - SonarQube 检测安全漏洞（SQL 注入、XSS、硬编码密钥等）[4] - SonarQube 识别代码异味和技术债务 [4] - SonarQube 发现测试覆盖率缺口 [4] - SonarQube 评估可维护性问题 [4] AI Reviewer Limitations - AI 审查器进行模式匹配，而非验证 [3] - AI 审查器验证语法，而非系统行为 [3] - AI 审查器审查代码，而非后果 [3] Setup - 安装 SonarQube MCP 服务器 [4] - 将其添加到 AI 助手的配置中 [4]

RT Jimmy Song (송재준) (@jimmysong)Within the datacarriersize/OP_RETURN change is hidden a change to how many OP_RETURN outputs are allowed. Previously, it was 1. With the datacarriersize change, it's now as many as the user wants.The technical justification for multiple OP_RETURNs now being standard is completely unconvincing. To quote @theinstagibbs:"The motivation for doing this is for situations where you cannot commit to all data efficiently otherwise. Think SIGHASH_SINGLE | ACP scenarios. The datacarrier ...