AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed is crucial [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI to address the complexities of production systems, which involves more than just code [13] Resolve AI's Solution - Resolve AI provides AI site reliability engineer agents to troubleshoot alerts and incidents [11] - Resolve's agents can understand production systems from code to backend databases, offering a faster solution [11] - Customers are using Resolve AI for "vibe debugging," indicating usage beyond incidents and alerts, leading to increased product usage [12] Talent Acquisition - Resolve AI competes with companies like Meta, OpenAI, and Anthropic for AI engineers [14] - Resolve AI attracts top researchers by offering the opportunity to significantly impact the company and change software engineering [16] Future of Software Engineering - Humans will operate at a higher level of abstraction, with agents handling much of the work [17] - Underlying infrastructure and tools will adapt to be more suitable for agents [17]

Software Development & Problem Solving - Software development addresses problems created by software itself [1] - Fixing bugs on GitHub exemplifies using software to solve software-related issues [1] Engineering & Crisis Management - The idea that engineering cannot solve crises it creates is considered flawed [1]

Today's conversation is with Spiro Zanthos. Spiros is a serial entrepreneur who's achieved two successful exits to Splunk and VMware. Now he and his co-founder May Agaral are on a mission to rethink software engineering tools for an AI first world with their new company Resolve AI.In our conversation, Spiro shares the status of productivity gains from AI and software engineering. How Resolve was born after realizing he spent 15 years in infrastructure and observability software just creating more work for h ...

AI发展现状 - AI技术目前擅长的领域并非软件工程，而是作为辅助工具，由软件工程师监督AI的工作 [1] - 软件工程师的角色正在转变为监督AI，通过输入指令来指导AI完成任务，而非直接编写代码 [1][2] - 行业普遍认为AI并非万能，在某些特定测试中表现不佳，技术发展尚未达到终点 [3][4] 行业趋势 - 行业内存在对AI价值的过度炒作和高估现象，需要警惕 [2] - 行业不应忽视AI的持续发展，回到2022年的状态是不现实的 [4]

AI Coding Evolution - 软件开发正经历从终端到图形界面，再到AI辅助的快速演变 [1][2][3][4] - Cursor 旨在通过AI 自动化编码流程，重点在于模型和人机交互 [46] - Cursor 的目标是让工程师更专注于解决难题、设计系统和创造有价值的产品 [47][49] Context Engineering & Coding Agents - Context Engineering 关注于为模型提供高质量和有针对性的上下文信息，而非仅仅依赖 Prompt 技巧 [16][17] - Semantic Search 通过自动索引代码库并创建嵌入，提升代码搜索的准确性和效率 [19][20] - Semantic Search 将计算密集型任务转移到离线索引阶段，从而在运行时获得更快、更经济的响应 [22] - Cursor 发现用户更倾向于使用 GP 和 Semantic Search 相结合的方式，以获得最佳效果 [22] Cursor's Products & Features - Tab 功能每天处理超过 4 亿次请求，通过在线强化学习优化代码建议 [7] - Cursor 正在探索多种 Coding Agents 的管理界面，包括并行运行和模型竞争 [38][39][42][43] - Cursor 正在探索为 Agent 提供计算机使用权限，以便运行代码、测试并验证其正确性 [44] - Cursor 允许用户通过自定义命令和规则，共享 Prompt 和上下文信息，实现团队协作 [32][33]

Software Development Efficiency - The primary goal is to accelerate the deployment of software to production [1] - The industry observes that deploying a prototype to production often takes longer than expected due to unforeseen issues [1] - A significant improvement would be achieved if deploying to production becomes as simple as creating the initial prototype [1] Agentic AI Impact - Agentic AI and software engineering aim to improve the speed of software delivery [1] Measurement of Success - The key metric for success is the increased speed at which software can be shipped to a broad audience [1]

AI Model Performance - Anthropic's Claude Sonnet 4.5 achieves a 77% score on a key software engineering benchmark [1] - The AI model can operate autonomously for over 30 hours on complex tasks [1]

RT Tetsuo (@tetsuoai)Grok 4 prompt for optimizing your Python code.(NOTE: after phase 1, the Model will pause to ask you if any changes need to be made before it continues)>>> promptYou are a collaborative AI panel of four senior software engineers speaking with one voice. Your mission: analyze, refactor, and harden code to production standards across security, performance, maintainability, and quality, keeping outputs concise and decision-oriented.Personas (combine insights into one answer)1. Senior Archit ...

Future of Engineering - The industry anticipates an increase in the number of engineers over the next 5 years [1] - Engineering is considered an elastic role, suggesting adaptability and growth potential [1] - AI is expected to amplify the productivity and value of engineers, rather than diminish it [1] Impact of AI - If engineers become 10 times more efficient, the industry would likely build 100 times more software [1] - AI will drive more features and iterations on algorithms [1]

Core Viewpoint - The article presents humorous illustrations related to programming, highlighting the challenges and quirks faced by developers in their daily tasks. Group 1 - The first illustration depicts the humorous consequences of interns modifying legacy code, emphasizing the risks associated with inexperienced developers handling critical systems [2] - The second illustration showcases six different approaches programmers take to fix bugs, reflecting the diverse problem-solving strategies within the software development community [3] - The third illustration contrasts backend and frontend development, illustrating the different skill sets and challenges faced by developers in these two areas [4]