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

This talk explores the use of AI in web development, including tools like GitHub Copilot and Fig for CLI commands. AI can generate boilerplate code, provide context-aware solutions, and generate dummy data. It can also assist with CSS selectors and regexes, and be integrated into applications. AI is used to enhance the podcast experience by transcribing episodes and providing JSON data. The talk also discusses formatting AI output, crafting requests, and analyzing embeddings for similarity.

Setting up the system and separating concerns are important in software development. Modular construction and prefab units are a new trend that makes construction quicker and easier. Architectural complexity can lead to a drop in productivity and an increase in defects. Measuring architectural complexity can help identify natural modules in the code. Best practices for avoiding architectural complexity include organizing code by business domain and using prop drilling. Atomic design and organizing a monorepo are recommended approaches for managing architectural complexity.

The Talk discusses imposter syndrome and reframes it as being a professional imposter. It emphasizes the importance of sharing and starting, embracing imposterism, and building inclusively for the web. The speaker shares personal experiences of being an imposter in various technical disciplines and highlights the significance of accessibility. The Talk concludes with the idea of building a collective RPG game to remove excuses for not making things accessible.

GitHub Copilot is an auto-completion tool that provides suggestions based on context. Research has shown that developers using Copilot feel less frustrated, spend less time searching externally, and experience less mental effort on repetitive tasks. Copilot can generate code for various tasks, including adding modals, testing, and refactoring. It is a useful tool for improving productivity and saving time, especially for junior developers and those working in unfamiliar domains. Security concerns have been addressed with optional data sharing and different versions for individuals and businesses.

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.

Hey, we'll discuss scaling fast and engineering lessons learned in the last 15 years of tech startups. Scaling involves three things: business, team, and tech. Business scalability relies on sales and customer acquisition costs. Engineering is a tool the business uses. Scaling the team is vital as tech problems are often people problems. Team structure affects architecture and product development process. Organize teams based on purpose, not technology. Spend less time being blocked by other teams. Ship features without getting blocked. Own your own mess. Focus on product engineering partnership. Build faster using feedback cycles. Build appropriate solutions for your use case. Let go of ego and experiment with different approaches. Engineers own their own mess. Avoid work in progress. Finish the work and focus on fixing it later. Have a conversation before writing code. Scaling the tech is easier than you think. Pick an off the shelf design. Save innovation for core parts. Pick existing solutions. Focus on solving the problem. Don't waste time trying to predict future scale. Scale will surprise you. Do what works for your business. Push back on unnecessary complexity. Understand the cost of ideas. Modify the situation to fit existing design. Architecture is like a dependency graph on your code. Reduce architectural complexity by organizing code based on what it does. Use vertical models and avoid creating excessive dependencies. On the client, use vertical modules. On the back end, consider a service-oriented architecture. Start with a monolith and transition to microservices if necessary. Use folders instead of microservices when you have a small team. Use vertical models and contract or type-driven development to define clear APIs and interfaces. Avoid highly interconnected code and duplication. Focus on data structures to avoid complexity and the need for translation layers. Building translation layers can lead to slow user experience. Vertical teams aligned with vertical code allow for fast problem-solving, full control of features, and efficient data handling. Understanding the entire domain enables faster development with fewer bugs.