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.

Tobias Koppers explains chunking in Bundlers, emphasizing efficient module grouping for performance optimization. Strategies involve smart module grouping, caching, and balancing between request counts and download sizes. CSS delivery via link tags poses complexity in chunking due to order dependencies. Balancing CSS chunking to reduce requests involves overshipping techniques. Evolving chunking strategies consider compile time challenges and the impact of React server components. Future optimizations may prioritize pages based on analytics data. Tailwind offers a solution for CSS chunking, while the need for future CSS features with native dependency declarations is highlighted.

Evan Yeo discusses Vite's growth, challenges with dependencies like ES Build and Rollup, and the creation of the bundler Rolldown. The JavaScript ecosystem faces fragmentation, but the company aims for a unified JavaScript stack. Rust is chosen for lower-level development, while JavaScript and TypeScript for high-level APIs. Roldown offers advanced bundling features, outperforming existing tools. Integration with Vite leads to tailored optimizations and significant speed improvements. Vite+ development focuses on creating a comprehensive toolkit. VitePlus integrates TS-down for library bundling, ViteTest for testing, and OX-Lint for linting. Future plans include monorepo awareness, build orchestration, and framework-like features.

Hi there! I'm Anderson Mancini, a creative developer from Brazil. Let's dive into 3JS game development and how you can start your journey. React ThreeFiber offers a more organized and compact approach to building scenes using components. Explore exciting examples of what you can build with React ThreeFiber and 3JS. Learn the creative process of building a game using 3JS and recommended courses and learning resources. Performance tips for creating games with 3JS will be discussed in tomorrow's session.

Joshua introduces Transformers JS and Hugging Face, emphasizing community collaboration and pre-trained models. Transformers JS evolution led to 1.4 million monthly users, supporting 155 architectures. The library's browser-based capabilities offer real-time processing, cost-efficiency, and scalability. Integration enhancements include native web GPU execution and React Native implementation. Web ML implementation focuses on Onyx Runtime for device execution and web GPU for resource optimization. Browser-based ML applications cover vision, speech recognition, and text-to-speech. Advanced implementations include multimodal applications and educational tools. Interactive AI demonstrations showcase semantic search and conversational AI scenarios. Model licensing transitions to ECMAScript for efficiency and model redownloading factors are discussed.

PlayCanvas is an open-source game engine used by game developers worldwide. Optimization is crucial for HTML5 games, focusing on load times and frame rate. Texture and mesh optimization can significantly reduce download sizes. GLTF and GLB formats offer smaller file sizes and faster parsing times. Compressing game resources and using efficient file formats can improve load times. Framerate optimization and resolution scaling are important for better performance. Managing draw calls and using batching techniques can optimize performance. Browser DevTools, such as Chrome and Firefox, are useful for debugging and profiling. Detecting device performance and optimizing based on specific devices can improve game performance. Apple is making progress with WebGPU implementation. HTML5 games can be shipped to the App Store using Cordova.

The Talk delves into motion control with multimodal AI, exploring TensorFlow.js models for gesture recognition and enhancing user interactions. It discusses leveraging LLMs for gesture-based interaction, investigating Gemiini for gesture recognition, and controlling light states with Gemini functions. The conversation includes webcam-based gesture recognition, custom gesture databases, and the future of personalized AI assistance with acoustic recognition.

Product-market fit is like being pushed by the market, needing more. A game analogy illustrates teamwork dynamics in handling tasks. Communication is key in team growth, allowing delegation for collective success. Experienced team members focus on clarity, delegation, and accountability. Prioritize assigning tasks to team members based on expertise. Embrace the role of a side quest specialist for long-term or short projects. Engage in mentorship and explain reasoning to foster team independence and growth. Mentorship occurs organically between tasks, influencing work approaches. Trust your team to make decisions by explaining your rationale. Mentor one skill at a time to ensure effective learning and application. Focus feedback, encourage newbies, address weak points of experts to enhance performance. Focus on feedback for beginners and experts, emphasize outcomes and timely feedback to enhance performance. Realize the importance of quick utility, user stories, and outcome-focused work. Utilize user stories, documentation, and flexible roadmaps for effective planning and decision-making. Embrace change, document ideas, focus on user stories, and leverage backlogs for effective planning and adaptation.

Nico introducing himself and his work in machine learning and AI. Exploring the essence of Kit and its human-like features, and the use of Transform.js for machine learning in the browser. Understanding the significance of model parameters and the availability of open source models like Apertus and GEMMA3. Creating a fine-tuned version of the AI pipeline using prompt engineering with a new system prompt for improved behavior and responses. Utilizing Reddit to extract scenes, transforming screenplays into structured JSON, and creating an LLM summarizer for semantic similarity comparisons. Introduction to Agentic Rack System and Autonomous Task Completion by AA Agents. Utilizing TensorFlow and Transformer JS for Various Tasks and Models. Running LLMs in the browser is not practical due to large model sizes. Device performance matters in browser-based AI processing.