Name: Productivity Conf for Devs and Tech Leaders
Start: 2025-03-27T15:00:00.000Z
End: 2025-03-28T20:00:00.000Z

Steve Sewell

Stefan Wille

Lera

Daria Shabala

Luca Mezzalira

Richard Carrigan

Joran Quinten

Thibault Van den Broecke

HT Sahin

David Pugh

Joanna Otmianowska

Mohamad Shiralizadeh

Jeroen van Wissen

David Villamarin

Mangirdas Darguzis

Azadeh Farshidi

Rosario Terranova

Georgi

Jennifer

Robert

Yevheniia Smolanka

Devan

Boris Karaivanov

Fulya Çimendere

JDog

XAVIER ESCUDERO

Andrey Ozornin

Omri Spiguel

Oury DIALLO

khouchane Mahmoud

Nima Postanchi

James Anderson

Denis Radin

Para asistir al webinar, por favor regístrate <a href="https://gitnation.com/contents/free-webinar-building-full-stack-apps-with-cursor">aquí</a>.En este webinar cubriré un proceso repetible sobre cómo iniciar aplicaciones Full Stack en Cursor. Espera entender técnicas como usar GPT para crear requisitos de producto, esquemas de base de datos, hojas de ruta y usar esos en notas para generar listas de verificación que guíen el desarrollo de la aplicación. Profundizaremos más en cómo corregir alucinaciones/errores que ocurren, indicaciones útiles para hacer que tu aplicación se vea y se sienta moderna, enfoques para conectar cada capa y más. Al final, ¡espera poder ejecutar tu propia aplicación Full Stack generada por IA en tu máquina!

Webinar gratuito: Construyendo aplicaciones Full Stack con Cursor

Today's workshop focused on building full-stack apps with Cursor, using Next.js, Tailwind, Prisma, and Postgres. The session covered topics like setting up the app, creating checklists for enterprise apps, exploring the power of Cursor for code indexing and searching, handling errors and improving AI, troubleshooting database setup and styling issues, debugging and validating functionality, implementing a Kanban board, and using AI for bug resolution and interface building. The workshop also discussed refining Cursor rules and file matching rules for better code organization. Overall, the workshop showcased the capabilities of Cursor and its potential in software development.

In this webinar I’ll cover a repeatable process on how to spin up full stack apps in Cursor.  Expect to understand techniques such as using GPT to create product requirements, database schemas, roadmaps and using those in notes to generate checklists to guide app development.  We will dive further in on how to fix hallucinations/ errors that occur, useful prompts to make your app look and feel modern, approaches to get every layer wired up and more!  By the end expect to be able to run your own ai generated full stack app on your machine!

Free webinar: Building Full Stack Apps With Cursor

Mike Mikula

En esta masterclass, exploraremos cómo delegar trabajo a modelos de lenguaje grandes y automatizar la mayor parte del "trabajo ocupado" para maximizar y mejorar la productividad a diario. Lo haremos principalmente explorando cómo los LLMs pueden llamar funciones.

Maximize Productivity with AI Agents

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.

In this talk, we will explore how to offload work to large language models and automate away most "busy work" in order to maximize and enhance productivity on a daily basis. We will do so mainly by exploring how LLMs can callfunctions.

Tejas Kumar

Las herramientas de IA están cambiando la forma en que diseñamos, construimos y probamos interfaces de usuario. Esta masterclass analiza las tecnologías de IA actuales que están mejorando el proceso de desarrollo frontend y cómo utilizarlas, incluyendo:- Usar Visual Copilot para convertir diseños de Figma en código listo para producción;- Implementar agentes de IA para la escritura y prueba de código automatizadas;- Ejemplos del mundo real que muestran cómo las herramientas de IA pueden mejorar la velocidad y la alegría del desarrollo.

Desarrollo Frontend Potenciado por IA: Construyendo Mejores UIs Más Rápido

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.

AI tools are changing how we design, build, and test user interfaces. This talk looks at current AI technologies that are improving the frontend development process and how to use them, including:- Using Visual Copilot to convert Figma designs into production-ready code;- Implementing AI agents for automated code writing and testing;- Real-world examples showing how AI tools can improve development speed and joy.

AI-Powered Frontend Development: Building Better UIs Faster

En el entorno de desarrollo acelerado de hoy, la atención de un desarrollador está constantemente en alta demanda. Para lograr una productividad máxima, los desarrolladores necesitan mantener un estado de flujo, accediendo sin problemas al conocimiento y las herramientas requeridas para resolver problemas complejos. Sin embargo, la realidad a menudo implica manejar múltiples herramientas, lo que lleva a frecuentes cambios de contexto y a una eficiencia reducida.Esta sesión explorará cómo las herramientas habilitadas por AI pueden revolucionar tu flujo de trabajo de desarrollo, ayudándote a convertirte en un desarrollador 10x. Nos centraremos en los tres pilares principales del desarrollo: codificación en tu IDE, uso de herramientas de colaboración y investigación en tu navegador. Al integrar herramientas de AI, puedes optimizar estas actividades, minimizando los cambios de contexto y maximizando el enfoque.A través de demostraciones prácticas y ejemplos del mundo real, esta sesión te mostrará cómo aprovechar el poder de la AI para manejar la variedad de contextos que encuentras a diario, aumentando en última instancia tu productividad y permitiéndote concentrarte en lo que mejor haces: codificar. Además, si tu empresa tiene preocupaciones sobre la seguridad de los datos al usar las herramientas típicas de AI, no necesitas preocuparte. Esta masterclass se centrará específicamente en el uso de herramientas de AI que funcionan en el dispositivo del usuario y todo el procesamiento ocurre localmente, por lo tanto, para grandes empresas, esto resuelve el mayor desafío de información/seguridad.Fecha y hora: TBD. Remoto a través de Zoom.

10X Tu Productividad como Desarrollador Usando AI Local y Primero en Privacidad

The workshop discusses challenges faced by developers and how AI-powered tools can assist in understanding code and managing context. The use of AI tools like Pieces.app and Copilot can increase productivity by capturing context from different tools and providing relevant suggestions. The PCES tool leverages conversations and data to correlate activities and offers personalized onboarding. AI-powered tools can be used with code editors to understand code and solve coding issues. Copilot can also be used for test coverage, interlinking, and retrieving feedback. Pieces.app allows for code snippet management and integration with Copilot. The workshop demonstrates advanced uses of Copilot for code reviews and unit testing. Overall, these tools aim to enhance productivity and streamline development processes.

In today's fast-paced development environment, a developer's attention is constantly in high demand. To achieve peak productivity, developers need to maintain a state of flow, seamlessly accessing the knowledge and tools required to solve complex problems. However, the reality often involves juggling multiple tools, leading to frequent context switching and reduced efficiency.This session will explore how AI-enabled tools can revolutionize your development workflow, helping you become a 10x developer. We will look into the three primary pillars of development: coding in your IDE, using collaboration tools, and researching in your browser. By integrating AI tools, you can streamline these activities, minimizing context switching and maximizing focus.Through practical demos and real-world examples, this session will show you how to harness the power of AI to handle the range of context you encounter daily, ultimately boosting your productivity and allowing you to focus on what you do best—coding. Moreover if your company has concerns regarding data security while using the typical AI tools, you don't need to worry. This workshop will specifically focus on using AI tools that run on the user's device and all of the processing happens locally, thus for large enterprises, this solves the biggest information/security challenge. Date and time: TBD. Remote via Zoom.

10X Your Developer Productivity Using Privacy-First and Local AI

Shivay Lamba

Imagina reemplazarte a ti mismo con un programador de IA multi-agente para desarrollar tu aplicación web de producción. Eso es exactamente lo que hicimos en mi startup takzyli.pl. Para lograr esto, diseñamos y utilizamos el Clean Coder - marco de agentes de IA para la escritura autónoma de código (https://github.com/GregorD1A1/Clean-Coder-AI), que es un proyecto de código abierto, con suerte. Si funcionó para nosotros, ¿por qué no debería funcionar para ti?En esta masterclass, te mostraré cómo crear una aplicación web completa de manera (casi) autónoma y reducir drásticamente el tiempo que tú o tus empleados pasan escribiendo código.

Cómo Crear una Aplicación Web de Manera (Casi) Autónoma Usando Clean Coder

Today, we will use the clean coder framework to write a simple web application. Clean coder is a framework that aims to replace human programmers with AI by offloading most programming tasks. It is designed to be an autonomous programmer that can build applications overnight. It includes intelligent agents, such as a two-step planner agent and a front-end feedback agent. To configure CleanCoder, provide the Antropic API key, OpenAI API key, project folder path, and Todoist API key. CleanCoder can be used with Next.js and other applications. We can automate coding tasks, create projects, and set code and product rules. CleanCoder also has debugging and code control features. It allows for semantic search and enhances the coding process. CleanCoder is compatible with Next.js, FastAPI, Vue.js, and potentially C-Sharp. Future updates will include an improved manager agent and regular application updates.

Imagine replacing yourself with a multi-agent AI programmer to develop your production web application. That's exactly what we did at my startup takzyli.pl. To achieve this, we designed and used the Clean Coder - AI agent framework for autonomous code writing (https://github.com/GregorD1A1/Clean-Coder-AI), which is hopefully open-source project. If it worked for us, why shouldn't it work for you?In this workshop, I'll show you how to create an entire web application in an (almost) autonomous way and drastically reduce the time you or your employees spend on writing code.

How to Create a Web Application in an (Almost) Autonomous Way Using Clean Coder

Grigorij Dudnik

"Test, Code, Repeat: Dominando el Desarrollo Asistido por AI" introduce a los desarrolladores a una forma transformadora de codificación con AI como un socio colaborativo. Esta masterclass se centra en cómo los flujos de trabajo iterativos, como la técnica de emparejamiento ping pong, permiten una interacción mejorada entre la creatividad humana y la eficiencia de AI.

Test, Code, Repeat: Dominando el Desarrollo Asistido por AI

Today's workshop focused on using AI tools in software development, particularly the AI tool Copilot. The session highlighted the benefits and limitations of AI tools, emphasizing the importance of context and specific test cases for optimal results. AI tools like Copilot can be useful for solving data structure and algorithmic problems, especially in job interviews. They are most effective when working in familiar languages or frameworks and can assist with boilerplate code and repeating patterns. However, it is essential to be mindful of code quality and tech debt and not overly rely on AI suggestions. Pair programming with AI and following TDD principles can enhance code quality and knowledge sharing. The session also covered refactoring and implementing the Fibonacci sequence, exploring AI model options, facilitating breakout rooms for collaboration, and using property testing to validate code. Overall, the workshop highlighted the potential of AI tools in software development while emphasizing the need for human guidance and review.

"Test, Code, Repeat: Master AI-Assisted Development" introduces developers to a transformative way of coding with AI as a collaborative partner. This workshop focuses on how iterative workflows, such as the ping pong pairing technique, enable an enhanced interaction between human creativity and AI efficiency.

Test, Code, Repeat: Mastering AI-Assisted Development

Marco Pierobon

Construir aplicaciones de IA generativa requiere una mentalidad de GenAIOps que te lleva en un viaje desde la selección de modelos hasta la ideación de prompts, la evaluación de prototipos y el despliegue de aplicaciones. En esta masterclass, exploraremos el viaje de GenAIOps desde la perspectiva de un Ingeniero de IA e identificaremos los desafíos de productividad, así como las soluciones de herramientas que pueden mitigar estos problemas y ayudar a optimizar su experiencia como desarrollador.

Flujo de Trabajo de Ingeniero de IA de Extremo a Extremo: Desafíos de Productividad y Sus Soluciones

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.

Building generative AI applications requires a GenAIOps mindset that takes you on a journey from model selection to prompt ideation, prototype evaluation, and app deployment. In this talk, we’ll explore the GenAIOps journey from an AI Engineer perspective and identify productivity challenges, and the tooling solutions that can mitigate these issues and help streamline their developer experience.

AI Engineer End-to-End Workflow: Productivity Challenges and Their Solutions

Nitya Narasimhan

Las herramientas de revisión de código impulsadas por AI están transformando el ciclo de vida del desarrollo al automatizar verificaciones repetitivas y resaltar olores de código. Esta sesión explorará cómo AI reduce el esfuerzo manual, mejora la precisión de detección y mejora la calidad general del código.

Cómo AI Puede Automatizar el 70% de la Revisión de Código: Reduciendo la Carga y Mejorando la Calidad del Código

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.

AI-driven code review tools are transforming the development lifecycle by automating repetitive checks and highlighting code smells. This session will explore how AI reduces manual effort, improves detection accuracy, and enhances the overall quality of code.

How AI Can Automate 70% of Code Review: Reducing Burden and Improving Code Quality

Manasa Hari

En esta interesante masterclass, profundizaremos en el viaje de construir, probar y desplegar una aplicación web mientras examinamos las capacidades y limitaciones de ChatGPT en el desarrollo web. A través de la lente de este proceso integral, exploraremos cómo las técnicas de desarrollo impulsadas por IA pueden contribuir a un flujo de trabajo más eficiente y discutiremos los desafíos que pueden surgir. Los asistentes obtendrán una comprensión más profunda de cómo incorporar efectivamente ChatGPT en sus proyectos, centrándose en encontrar el equilibrio adecuado entre la automatización y los esfuerzos manuales. Únete a nosotros mientras navegamos por el paisaje en constante evolución del desarrollo web e investigamos el papel de la IA en la configuración de nuestras estrategias de desarrollo.

ChatGPT Desatado: Revolucionando el Desarrollo Web desde la Idea hasta el Despliegue

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.

In this engaging talk, we will delve into the journey of building, testing, and deploying a web app while examining the capabilities and limitations of ChatGPT in web development. Through the lens of this comprehensive process, we will explore how AI-driven development techniques can contribute to a more efficient workflow and discuss the challenges that may arise. Attendees will gain a deeper understanding of how to effectively incorporate ChatGPT into their projects, focusing on striking the right balance between automation and manual efforts. Join us as we navigate the ever-evolving landscape of web development and investigate the role of AI in shaping our development strategies.

ChatGPT Unleashed: Revolutionizing Web Development from Idea to Deployment

Nikolay Advolodkin

Cursor es un entorno de desarrollo integrado de IA revolucionario que permite la creación rápida de prototipos a través de su modo agente compositor. En esta masterclass cubriré cómo maximizar la productividad con Cursor, desde consejos y trucos hasta flujos de trabajo casi automáticos. Al final de esta masterclass, entenderás técnicas como el uso de listas de verificación modulares para construir pruebas, el uso e indexación de documentos, reglas de cursor, cuándo hacer commits, estrategias de retroceso para reducir alucinaciones y scripts de Devops que el LLM activo de cursor puede escribir y usar para mantener el código honesto.

Cómo dominar Cursor para un desarrollo rápido

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.

Cursor is a breakthrough AI integrated development environment that enables rapid prototyping via its composer agentic mode.  In this talk I'll cover how to maximize productivity with Cursor from tips and tricks to complete nearly hands off workflows. By the end of this talk you’ll understand techniques such as using modular checklists to build tests, using and indexing docs, cursor rules, when to commit, roll back strategies to reduce hallucinations and Devops scripts the active cursor LLM can write and use to keep the code honest.

How to Master Cursor for Rapid Development

¿Te sientes abrumado por sistemas de toma de notas complejos o frustrado por la búsqueda interminable de la aplicación "perfecta"? En esta charla, te mostraré cómo crear un sistema de notas personalizado que funcione para *ti*, ayudándote a organizar tus pensamientos, gestionar tu trabajo y concentrarte en lo que realmente importa—ya sea que estés programando, estudiando o planificando tu próximo proyecto creativo.

Sistema de Notas Que Te Dejará Prosperar: Crea Tu Propio Camino hacia la Productividad

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.

Do you feel overwhelmed by complex note-taking systems or frustrated by the endless search for the "perfect" app? In this talk, I'll show you how to create a personalized notes system that works for *you*, helping you organize your thoughts, manage your work, and focus on what really matters—whether you're coding, studying, or planning your next creative project.

Notes System That Will Let You Thrive: Craft Your Own Path to Productivity

¿Crees que ser un IC limita tu impacto? Piensa de nuevo. En esta charla, exploraremos la Ingeniería de Productividad de Desarrolladores (DPE), un campo en crecimiento que se trata de hacer a los desarrolladores más felices y ayudarles a enviar mejor software más rápido. Esta sesión presentará los principios fundamentales de la DPE, explorará el marco SPACE y te enseñará cómo aplicar tus habilidades de ingeniería más allá del código. Espera navegar a través de estudios de caso del mundo real y obtener información de experiencias personales. Esta sesión es tu guía para aumentar tu influencia mientras te mantienes fiel al camino de IC.

Aprovechando las Prácticas de Ingeniería de Productividad de Desarrolladores para un Mayor Impacto como IC

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.

Think being an IC limits your impact? Think again. In this talk, we’ll explore Developer Productivity Engineering (DPE)—a growing field that’s all about making developers happier and helping them ship better software faster. This session will introduce the foundational principles of DPE, explore the SPACE framework, and teach you how to apply your engineering skills beyond just code. Expect to navigate through real-world case studies and gain insights from personal experiences. This session is your guide to leveling up your influence while staying true to the IC path.

Leveraging Developer Productivity Engineering Practices for Greater Impact as an IC

Mikhail Levchenko

La IA promete revolucionar la productividad para los gerentes, desde ayudar en la resolución creativa de problemas y la investigación hasta mejorar la priorización y el apoyo en la toma de decisiones. Pero, ¿puede la IA realmente ayudarnos a mantenernos imparciales? ¿Y cómo aseguramos su uso seguro sin arriesgar datos sensibles de la empresa? Esta charla explora estrategias accionables para aprovechar las herramientas de IA de manera efectiva mientras se mantienen estándares éticos y se protege la información. Saldrás con ideas sobre si la IA puede realmente optimizar tu trabajo y cómo integrarla de manera segura y responsable en tus prácticas gerenciales.

¿Puede la IA Realmente Mejorar la Productividad de los Gerentes?

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.

AI promises to revolutionise productivity for managers, from aiding in creative problem-solving and research to improving prioritisation and decision-making support. But can AI really help us stay unbiased? And how do we ensure its safe use without risking sensitive company data? This talk explores actionable strategies for leveraging AI tools effectively while maintaining ethical standards and safeguarding information. You'll leave with insights on whether AI can truly optimise your work and how to integrate it safely and responsibly in your managerial practices.

Can AI Truly Enhance Manager Productivity?

Vasilika Klimova

Cómo Windsurf Rompe el Techo para la Recuperación

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.

How Windsurf Breaks Through the Celling for Retrieval

Kevin Hou

La IA ya ha cambiado el desarrollo de software. Herramientas como GitHub Copilot se han convertido en partes ubicuas del flujo de trabajo de millones de desarrolladores. Al igual que otras características bien conocidas del editor (como autocompletar o tooltips), la IA en el editor aumenta la productividad, reduce el cambio de contexto requerido y permite a los desarrolladores mantenerse en el flujo mientras se enfocan en las partes importantes de su trabajo. Sin embargo, este fue solo un primer paso—con los rápidos desarrollos de los modelos de lenguaje grande (LLMs), que se han vuelto más poderosos y conocedores con cada lanzamiento y tienen excelentes capacidades de razonamiento, podemos hacer más. Hoy en día, vemos herramientas de IA capaces de resolver tareas complejas a nivel de repositorio o incluso generar aplicaciones completas. En la charla, haré una demostración de algunas nuevas herramientas de IA que vienen de GitHub Next, como Copilot Workspace o GitHub Spark. Discutiremos patrones emergentes de UX, daremos consejos sobre cómo construir tu propia herramienta de IA y echaremos un vistazo al futuro de las herramientas de desarrollo. Todo eso mientras intentamos responder la pregunta más importante—cómo diseñar aplicaciones utilizando IA con los humanos en mente.

Construyendo la Próxima Generación de Herramientas de Desarrollo de IA

Hello, everyone. My name is Krzysztof, and I will be talking today about developer tools, AI, and how we use both of those to create the next generation of developer tools. GitHub Copilot, released in 2021, revolutionized developer tools by automating boring parts of coding and keeping developers in a state of flow. Suggestions are shown directly in the editor and clearly separated from the real code. Chat interfaces, like Chat GPT, solve quality of response issues and are useful when flow doesn't exist. The next generation of tools focuses on structured exchange, user control, and iteration. GitHub Spark allows users to create small applications without focusing on code. Designing applications with the user in mind and handling ambiguity is important. Real-world software tasks require human guidance, and exploring new ideas beyond chat-based AI is necessary. Always consider the human aspect and ethical considerations when using AI-powered features.

AI has already changed software development. Tools like GitHub Copilot have become ubiquitous parts of the workflow of millions of developers. Like other well-known editor features (like autocomplete or tooltips), AI-in-editor increases productivity, reduces required context switching, and lets developers stay in the flow while focusing on important parts of their job. However, this was just a first step—with the rapid developments of large language models (LLMs), which have become more powerful and knowledgeable with every release and have excellent reasoning capabilities, we can do more. Nowadays, we see AI tools able to solve complex, repository-wide tasks or even generate whole applications. In the talk, I will demo some new AI tools coming from GitHub Next, such as Copilot Workspace or GitHub Spark. We will discuss emerging UX patterns, give advice on how to build your own AI tool and peek into the future of developer tools. All that while trying to answer the most important question—how to design applications using AI with humans in mind.

Building the Next Generation of AI Developer Tools

Krzysztof Cieślak

Esta masterclass demuestra cómo los desarrolladores pueden aprovechar al máximo los asistentes de codificación AI gestionando efectivamente el contexto que proporcionan. A través de ejemplos prácticos con tres asistentes populares—Aider, Cursor y Windsurf—mostraré cómo configurar el contexto global y local, gestionarlo en tiempo real y superar limitaciones comunes. Los asistentes también aprenderán cómo extender las capacidades de los asistentes con un servicio de conocimiento centralizado para maximizar su productividad y acelerar la entrega de software.

Gestionando Contexto en Asistentes de Codificación AI: Logra Mejores Resultados con Menos Alucinaciones

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.

This talk demonstrates how developers can get the most out of AI coding assistants by effectively managing the context they provide. Through practical examples with three popular assistants—Aider, Cursor, and Windsurf—I’ll show how to configure global and local context, manage it in real-time, and overcome common limitations. Attendees will also learn how to extend the assistants’ capabilities with a centralized knowledge service to maximize their productivity and accelerate software delivery.

Managing Context in AI Coding Assistants: Achieve Better Results with Fewer Hallucinations

Tim Kitchens

En esta masterclass, cubriremos las características clave de Amazon Q Developer y demostraremos cómo puede mejorar el flujo de trabajo de un desarrollador. La sesión incluirá demostraciones en vivo de:<ul><li>/dev</li><li>@workspace</li><li>Code Scan</li><li>@git y @history de Q-Cli</li></ul>También mostraremos estudios de caso de clientes del mundo real para resaltar las aplicaciones prácticas de estas características.

Supercargando Tu Flujo de Trabajo de Desarrollador con Amazon Q Developer

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.

In this talk, we'll to cover key features of Amazon Q Developer and demonstrate how it can enhance a developer’s workflow. The session will include live demos of:<ul><li>/dev</li><li>@workspace</li><li>Code Scan</li><li>@git and @history from Q-Cli</li></ul>We’ll also showcase real-world customer case studies to highlight the practical applications of these features.

Supercharging Your Developer Workflow with Amazon Q Developer

Vikash Agrawal 

Abeetha Bala

Como Ingeniero de QA Senior con más de una década de experiencia en pruebas y optimización de procesos, entiendo la importancia crítica de una documentación técnica precisa y bien estructurada para impulsar proyectos exitosos. Desde repositorios de casos de prueba y especificaciones de productos hasta referencias de API y manuales de usuario completos, la calidad de la documentación impacta directamente en la eficiencia del equipo, la usabilidad del producto y la satisfacción del cliente.En esta masterclass, desglosaré cómo herramientas impulsadas por AI como ChatGPT, Atlassian Intelligence y GitHub Copilot están transformando el panorama de la documentación técnica. Nos centraremos en cómo los Modelos de Lenguaje Grande (LLMs) permiten la creación rápida de contenido estandarizado y accesible en diversos formatos.

Revolucionando la Documentación Técnica con AI

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.

As a Senior QA Engineer with over a decade of experience in testing and process optimization, I understand the critical importance of precise, well-structured technical documentation in driving successful projects. From test case repositories and product specifications to API references and comprehensive user manuals, the quality of documentation directly impacts team efficiency, product usability, and client satisfaction.In this talk, I’ll unpack how AI-powered tools like ChatGPT, Atlassian Intelligence, and GitHub Copilot are transforming the landscape of technical documentation. We’ll focus on how Large Language Models (LLMs) enable the rapid creation of standardized, accessible content across diverse formats.

Revolutionizing Technical Documentation with AI

Vitali Shchur

Exploraremos cómo la IA ha remodelado la curva de aprendizaje, acelerado el desarrollo y enfatizado la creatividad en la resolución de problemas. Saldrás con ideas prácticas sobre cómo construir una arquitectura escalable, dominar el arte de la solicitud y divertirte creando soluciones innovadoras mejoradas por IA.Además, examinaremos herramientas modernas de IA como Windsurf, Cursor, Bolt, Replit y v0, destacando cuáles de estas he encontrado que son las más productivas en uso práctico.

Desarrollo Potenciado por IA: De la Curiosidad a la Creatividad

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.

We’ll explore how AI has reshaped the learning curve, expedited development, and emphasized creativity in problem-solving. You’ll leave with actionable insights on building scalable architecture, mastering the art of prompting, and having fun creating innovative, AI-enhanced solutions. Additionally, we will examine modern AI tools such as Windsurf, Cursor, Bolt, Replit, and v0, highlighting which of these I have found to be the most productive in practical use.

AI-Powered Development: From Curiosity to Creativity

corbin

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.

Most organizations have already taken strides to improve developer experience, streamlining workflows and adopting modern tooling. Yet many still grapple with operational blind spots, security risks, and escalating costs. In this forward-looking session, we explore how to move beyond a narrow focus on developer satisfaction and integrate core pillars of engineering excellence—security, reliability, efficiency, and velocity—into every facet of the development process. Building on the foundation of your existing DevX successes, we’ll dive into practical strategies for enforcing production-ready standards, automating security checks, and optimizing performance without sacrificing developer autonomy. Learn how to evolve your DevX efforts into a holistic approach that not only keeps your teams happy and productive, but also ensures that your software remains stable, cost-effective, and ready for the future. Join us to discover how to write the next chapter in developer productivity—one that truly balances experience with excellence.

The Next Chapter of Dev Productivity: Aligning Experience with Excellence

Jeff Schnitter

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.

Is communication with bugs possible? It is important to understand them because there is valuable information underlying the bugs of software, and it promises quality improvements. Let's talk about how we can analyze reported issues to improve the future of the product by means of Artificial Intelligence.

Can Machines Learn Bug Language?

Mesut Durukal

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Productivity Conf for Devs and Tech Leaders

FocusReactive

Cortex

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Cortex is an Internal Developer Portal that transforms the onboarding experience by providing a clear path from hire to productivity, ensuring every developer has the tools and information they need to succeed.Consolidate all relevant information in one place for easy access.Review how new employees are progressing against onboarding tasks.Automate tasks to standardize and streamline the onboarding process.<a href="https://www.cortex.io/solutions/developer-productivity?utm_medium=events&utm_campaign=TS-2503-Productivity-Conf_C/A" target="_blank" rel="noopener noreferrer">Learn more</a>.

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