WinterTC is working on standardizing JS runtimes to avoid vendor lock-in and provide a universal API. This involves defining which web APIs should be included, such as URL and WebAssembly. The goal is to create a definitive list of APIs for server-side JS runtimes.

Hi, everyone. Thanks for joining this talk on the next chapter of Dev Productivity, aligning experience with excellence. My name is Jeff Schnitter. I'm a Solution Architect at Cortex, and frankly, I'm not sure if I'm qualified to give the talk about Dev Productivity and aligning experience with excellence across the entire industry. So, good developer experience make developers happy. Great NGX makes the business thrive, and this ties it back to developers being happy. NGX excellence is really a constant journey. The adoption of those tools makes sense. It's still really difficult to make sense through all of that. Think about a developer whose job is really to understand the complexity of writing code and deploying it in different environments. Developer productivity is about reducing friction and enabling them to write better, performing, and secure code. Customers are on different paths in their journey to engineering excellence. AI can play a huge role in the engineering ecosystem. Metrics and a broader perspective beyond tools are necessary for engineering excellence. DevEx and engineering excellence are essentially the same, focusing on freeing up developers to write feature code and get ideas to production while addressing challenges like cost and security. Cortex invites you to join engineering excellence activities and share thoughts on completing the journey from DevEx to engineering excellence. The conversation and journey never end, so being prepared and adaptable is crucial.

Welcome to my talk on AI powered development. We'll explore the implications of AI tools and where the value lies in software development. We're transitioning to a new age where AI plays a significant role in coding. The new workflow involves asking AI for code, verifying it, and continuing to build. AI tools are available in the market, offering different options for developers. It's important to choose the right approach that aligns with your goals. Keeping IDE and AI separate allows for better control. The value of AI-powered development lies in architecture, scalability, and creativity. We're in the early stages of AI-powered development, presenting a significant advantage for those entering the field now.

I'm Tejas Kumar, a software developer with over 20 years of experience. AI agents are defined as entities that act on behalf of users or groups to produce specific effects. Agents consist of an orchestration layer, a language model, and tools represented as JSON functions. Langflow is an open-source tool that allows users to build their own AI agents by connecting language models and tools. Composio is a tool that enhances agent capabilities by offering integrations and apps, such as Google Calendar integration. MCP (Model Context Protocol) is a way to share context with models and extend their capabilities. It allows functions to be made available to models over standard input/output or an HTTP endpoint. MCP can be used with GitHub to perform various tasks like searching and fixing code issues. The Talk covered the basics of AI agents, building agents with Langflow and enhancing them with Composio, and using MCP with GitHub. The speaker encouraged audience questions and exploration of these concepts.

Welcome to Chad GPT, Unleashed, Revolutionizing Web Development. I'll share my journey and lessons learned about GPT-03-Mini-High. TLDR: Good with slower-changing technologies, poor with rapidly moving ones. Let's test the application by building a vision board. User stories, requirements, acceptance criteria, and design principles were well-executed. Mobile responsiveness, accessibility, security, and scalability are important. LLM struggled with error handling and installation, but we managed to troubleshoot. The app creation process was painful, but we created a better version. Some end-to-end tests failed. Automated tests didn't consider challenges and project structure. Continuous integration failed during deployment, but manual deployment worked. Majority of LLM tools worked. AI's impact on hiring: current roles remain, but new hires are affected. AI enhances my abilities, reducing the need for hiring. Thank you for your time.

Today's Talk explores productivity challenges and solutions for AI engineers, focusing on an application called Contoso Chat. The end-to-end workflow is examined from a productivity perspective, introducing the concept of Exposure to AI. Building an AI application involves ideation, augmentation, and operationalization stages. Provisioning and setup are crucial steps, with infrastructure as code being a productivity tool. GitHub Codespaces and development containers provide consistent development environments. Azure AI Inference API allows easy iteration and prototyping. AI-assisted evaluation involves training AI models and using evaluators for grading responses. Custom evaluators can be created. Overall, the Talk emphasizes the importance of productivity throughout the AI engineer's journey.

Today's Talk discusses the transformation of performance monitoring with AI in Node.js. AI-powered tools can analyze massive amounts of data and provide actionable insights, making performance debugging faster and more efficient. Traditional profiling techniques are often time-consuming and require deep expertise. AI-powered diagnostics analyze data and provide recommendations for optimizing performance. AI-driven performance analysis correlates performance issues with system metrics and generates code optimization suggestions. Advancements in performance debugging include detecting CPU bottlenecks, memory leaks, and combining multiple data sources for context-aware insights. The AI-powered demo showcases the detection and optimization of performance issues in a Node.js application. Integrating AI-powered performance monitoring is straightforward and requires a mindset of continuous learning and curiosity in performance optimization.

Hey, my name is Mikhail Levchenko. I've changed my job and became a full-time developer productivity engineer. Let me share a little story. Imagine being a wealthy trader in 15th century Europe. Delivering spices for profit was not an easy task. Middle East nations heavily taxed any traders. But the Portuguese sailed around Africa using the Mariner's Astrolabe. Fast forward, Gerardo Mercator invented a map perfectly suited for deep ocean navigation. The Dutch East India Company became the new king of the spice trade. Developer productivity is about building a cozy environment for developers, solving hard problems, and identifying process bottlenecks. It focuses on the whole development process as a product. By leveraging this perspective, you can drive change among developers and other roles. Observing developer processes as a product involves meeting stakeholders, formulating a vision, creating a model and metrics, finding productivity problems, and proposing solutions. The stakeholders include developers, CEOs, CTOs, engineering managers, and other roles involved in software development. The goal is to make everyone more productive, guided by the motto of happier developers building better software faster. There are three highlighted frameworks in this talk: DORA, DEVX, and Mertrix. DORA focuses on speed and reliability with metrics like acceleration frequency, lead time, failure rate, and recovery time. DEVX addresses the human factor with metrics related to feedback loops, cognitive load, and flow state. DX4 is a recent metric that combines DORA and DEVX, introducing proprietary metrics for top management. It includes a failure rate to encompass reliability and a percentage of time spent on new features. Engineer-developed metrics like the reliability metrics from Dora and the percentage of time spent on new features are valuable additions. Talking to top management about changes is made easier with a cool metric. Research concerning metrics, consult stakeholders, and read literature to improve understanding. Try prototypes before developing custom tools as a last resort. Confirm success with empirical metrics. Release managers often feel constantly interrupted and overwhelmed by the task of supervising a release. Sharing the role with the entire team proved to be ineffective, leading to forgotten procedures. Automation and streamlining of the release process improved satisfaction and reduced delayed releases. Make developer productivity part of company goals and culture code. Advocate for developers to top management. Drive change by starting small, experimenting, and using tools as a last resort. Find ways to make teammates more productive.

In this Talk, the speaker discusses the importance of analyzing bugs in software development. Analyzing bugs goes beyond just counting them; it helps improve processes, understand root causes, and gain insights through machine learning. The Talk emphasizes the need for proper communication with bugs and the importance of preparing the bug analysis environment. Automation and visualization can improve bug analysis efficiency, and a comprehensive analysis of different metrics is necessary to gain insights for improvement. The Talk also highlights examples of bug analysis and the use of machine learning for pattern recognition. Bug severity estimation using machine learning algorithms and the use of clustering for bug analysis are also mentioned. Overall, the Talk provides valuable insights on bug analysis and its role in improving software quality and productivity.