Hey, this is Lance. Recently put out this blog post called debugging deep agents with lang. And the big idea here was connecting lang as a system of record for your traces with code agents like deep agents, but it could be other code agents like clock code to create kind of an iterative feedback loop.So you're having a code agent produce some langraph code that's being run. Traces are going to lang. And there's a way for the code agents to pull traces back, reflect on them, and update your lane share langra ...

Voice is one of the most natural ways to interact with AI. And as the models are getting better, I'm excited about new use cases and interaction patterns that it's going to unlock, especially in industries like education and customer service. It's surprisingly easy to get started building a voice agent.And so let's go through that in this video. I'm Tannushri and I'm going to show you how to build a voice agent, specifically a French tutor with this framework called Pipecat. going to walk through how it wor ...

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 Avi Chawla (@_avichawla)You're in a tech lead interview at Google.The interviewer asks:"AI generates 30% of our code now.But our engineering velocity has only increased by 10%.How would you fill this gap?"You: "Using AI code reviewers will solve this."Interview over!Here's what you missed:Many engineers think the solution to AI bugs is more AI.Their mental model is simple: "If AI can write it, AI can review it."But if AI could catch these issues, why didn't it write correct code in the first place?There' ...

You're in a tech lead interview at Google.The interviewer asks:"AI generates 30% of our code now.But our engineering velocity has only increased by 10%.How would you fill this gap?"You: "Using AI code reviewers will solve this."Interview over!Here's what you missed:Many engineers think the solution to AI bugs is more AI.Their mental model is simple: "If AI can write it, AI can review it."But if AI could catch these issues, why didn't it write correct code in the first place?There's enough evidence to sugges ...

What does volume debugging look like in my mind. To perform these kind of tasks like help me understand uh what commit has landed in production or is this feature flag enabled, right. An engineer needs understanding of code but also understanding of production and and production is composed of all of these different tools that each has a silo of data but the tools don't really talk to each other, right.And so it falls upon a human to bring their tribal knowledge and also you know knowledge of how to operate ...

Core Functionality of Langsmith Studio - Langsmith Studio is an IDE for building and debugging AI agents, compatible with any Langraph agent [1] - It provides a visual representation of the agent's structure and execution flow [5] - Users can interact with agents by sending messages and observing real-time execution [6][7] - Studio allows for debugging through trace view, showing the Langchain trace generated by each call [7] Debugging Capabilities - Studio facilitates debugging by allowing users to step through the execution path and identify issues in each step [10][11] - It supports hot reloading, enabling quick testing of changes made to the application [8][14] - Interrupts (breakpoints) can be set to inspect the state of the agent at specific points during execution [15] - Forking allows users to go back to previous steps, edit the state, and rerun the execution from that point with modified data [17][18] Practical Applications and Problem Solving - The platform can identify problems such as overly complex language in AI responses and address them by modifying prompts [9][10][12][13] - It helps diagnose issues related to flaky tools that intermittently fail to return results [3][15][16][19]

Overview - The document demonstrates running the Fabric Debugging example in a Jupyter notebook [1]