AI-Powered Development: From Curiosity to Creativity

Today's Talk discusses the future of performance tooling, focusing on user-centric, actionable, and contextual approaches. The introduction highlights Adi Osmani's expertise in performance tools and his passion for DevTools features. The Talk explores the integration of user flows into DevTools and Lighthouse, enabling performance measurement and optimization. It also showcases the import/export feature for user flows and the collaboration potential with Lighthouse. The Talk further delves into the use of flows with other tools like web page test and Cypress, offering cross-browser testing capabilities. The actionable aspect emphasizes the importance of metrics like Interaction to Next Paint and Total Blocking Time, as well as the improvements in Lighthouse and performance debugging tools. Lastly, the Talk emphasizes the iterative nature of performance improvement and the user-centric, actionable, and contextual future of performance tooling.

This Talk discusses building a voice-activated AI assistant using web APIs and JavaScript. It covers using the Web Speech API for speech recognition and the speech synthesis API for text to speech. The speaker demonstrates how to communicate with the Open AI API and handle the response. The Talk also explores enabling speech recognition and addressing the user. The speaker concludes by mentioning the possibility of creating a product out of the project and using Tauri for native desktop-like experiences.

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.

AI is transforming software engineering by using agents to help with coding. Agents can autonomously complete tasks and make decisions based on data. Collaborative AI and automation are opening new possibilities in code generation. Bolt is a powerful tool for troubleshooting, bug fixing, and authentication. Code generation tools like Copilot and Cursor provide support for selecting models and codebase awareness. Cline is a useful extension for website inspection and testing. Guidelines for coding with agents include defining requirements, choosing the right model, and frequent testing. Clear and concise instructions are crucial in AI-generated code. Experienced engineers are still necessary in understanding architecture and problem-solving. Energy consumption insights and sustainability are discussed in the Talk.

Web AI in JavaScript allows for running machine learning models client-side in a web browser, offering advantages such as privacy, offline capabilities, low latency, and cost savings. Various AI models can be used for tasks like background blur, text toxicity detection, 3D data extraction, face mesh recognition, hand tracking, pose detection, and body segmentation. JavaScript libraries like MediaPipe LLM inference API and Visual Blocks facilitate the use of AI models. Web AI is in its early stages but has the potential to revolutionize web experiences and improve accessibility.