Productivity Conf for Devs and Tech Leaders

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.

Today's Talk introduces the use of large language models (LLMs) to enhance front-end development. LLMs can act like our brains by maximizing the good parts and minimizing the bad parts. A demo in Cursor, an IDE, showcases how LLMs can be used with the builder.io Figma plugin. The Talk emphasizes the automation of tasks, such as adding a settings button and resolving errors, with the AI agent. Feedback and manual verification are crucial to ensure desired results. Tests and continuous iteration are recommended for stronger guarantees of correctness. Monitoring and guiding the AI agents is important to stay on track. Connecting to other tools like Figma and using AI prompting can further enhance code generation. The CLI enables code base integration and parallel development. Visual prototyping and seamless updates are possible with the Builder tool. Overall, the Talk highlights how LLMs can revolutionize front-end development by automating tasks, improving efficiency, and facilitating collaboration.

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.

Hello, welcome to my presentation on building your own Node system. I want to share my tips and mistakes so you can learn from them. Having a personalized system is valuable. I use a combination of Bullet Journal and Apple Notes. Logstick is my current system and I love it because it allows easy creation of notes and tags. The four core things that make a system work are engagement, easy search and retrieval, frictionless addition of new notes, and feeling supported. Note-taking provides peace of mind and eliminates the need to remember and search for information. Customize your note system to suit your preferences. Experiment with different note-taking apps and find one that works for you. Consistency matters more than the tool you use.

Hello, everyone. I'm Mansa Hari, and I'm very excited to open the session on how AI can automate 70% of code review and reduce the burden on developers. AI can cut review time, automate PR suggestions, and identify code issues. Implementing AI code reviews can reduce manual review time, ensure high code quality, and accelerate development cycles. The future of AI in code reviews includes understanding business logic, intuitive code suggestions, and hybrid AI-human reviews. Tools like Co-Pilot can be used to automate code review tasks and improve code quality.

Today's Talk discussed the potential of AI to enhance managerial productivity. The importance of frameworks and Git in coding was emphasized, as well as the need to stay updated with new technologies. Various AI tools and their use cases were mentioned, with a focus on data security. The benefits of using AI agents and case templates for project management were highlighted. AI tools for presentations, automation, and bias in AI models were also discussed. The limitations of AI and ethical considerations were mentioned, emphasizing the need for human creativity and accountability. Overall, the Talk provided valuable insights into the role of AI in software development and engineering.

I'll walk through some of the ways I set up Cursor to help speed up my full stack workflow. The main thing to check is indexing the code base. Cursor also looks at the get graph file relationships. Another important setting is large context, which allows absorbing larger files and multiple files of context in the prompt window. Iterating on lints helps find and fix errors faster. Web search tools and uploading your own docs are also available to enhance the prompt window. These are all preferences, including auto select, YOLO mode, and models. Rules are also crucial as they guide the model to understand your workflow. Implement best practice error handling and adjust rules as the models and system prompts change. MCPs like sequential thinking, Postgres SQL, and Puppeteer help refine thoughts and automate workflows. Creating a product requirements document in a .md file allows AI to ground itself in project information. The game was successfully implemented and all items were checked off. Debugging strategies involve adding debug logs to help identify issues. Use commit messages to provide clear documentation for changes. Demonstrating the use of AI in debugging by setting up and utilizing debug logs. Showcasing the benefits of providing debug information to resolve issues. Overview of various topics including indexing rules, linting, MCPs, PRDs, tech stacks, and checklists.

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.

Hello, Productivity Conference. We are Winsurf, a brand new AI-native code editor. Today, I'm going to show you how the product works, the guiding principles behind its development, and some tips and tricks on using AI in development workflows. Our agent is a powerful tool that abstracts away grunt work, making developers focus on building and shipping great products. It performs background research, predicts next steps, and automates decision-making. Windsurf integrates deeply into the application, understanding what you're doing and providing tools to achieve your goals. The agent can remember instructions and behave like an extension of yourself. Building for the future of intelligence, Windsurf aims to improve productivity and revolutionize coding with AI agents.

Today's Talk discussed integrating context into AI coding tools using the MoloContext protocol (MCP). The demos showcased the use of Conduit, an MCP tool, for bug tracking and feature addition in software development. The Talk also highlighted the use of Fetch in Cursor to summarize GitHub issues and create Jira bug reports. It demonstrated how Conduit can be used to manage issues, make code changes, and perform Git commits. Additionally, it explored the integration of Conduit with Windsurf to add a user interface for the conduit application. The Talk also touched upon implementing MCP in the AIDER AI coding system and using MCP and Windsurf for Conduit integration. Overall, it emphasized the importance of context and choosing the right tools for efficient software development workflows.

Hello, I'm Vitaliy Shchur. AI is revolutionizing technical documentation, creation, aimement, and maintenance. Precise, well-structured technical documentation is crucial for successful projects. With AI-powered tools, we can automate documentation processes, ensuring consistency, clarity, and efficiency. AI can create epics, user stories, and test cases. It applies a standardized glossary, detects outdated content, and improves readability. Digital assistants and tools like Miro Assist help gather requirements and enhance meetings. Automating epics and user stories creation from the SDD reduces manual effort and keeps documentation up to date. AI simplifies test case generation, improves documentation readability, and detects ambiguities. AI tools like GitHub Copilot and Microsoft Copilot automatically generate API documentation, code comments, and suggestions. AI eliminates the need for users to read documentation through personalized chat bots and virtual assistants. Challenges include manual review, context limitations, security concerns, and initial productivity drop. Best practices involve combining AI with human expertise and training AI models. The future of AI in documentation includes improved accuracy, intuitive integration, and increased automation.

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.

Today's Talk introduces Amazon Q Developer, an AI-powered conversational assistant that assists with software development lifecycle (SDLC) tasks. The speaker demonstrates building a 2048 game using Q Developer, which automatically detects the codebase and implements logic for the front end. The agent also generates tests for the code and helps identify bugs. Q Developer can generate documentation by analyzing the code and facilitate code reviews to find security vulnerabilities. The session covers error handling, deployment using AWS SAM, and debugging with Q Developer and CloudWatch. The speaker plans to deploy the application on EC2 in the future.

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.

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.