1 2 A retrospective A AUTODESK Long history of leading market on major technology shifts Future: AI-enabled Future: AI loud-native Design and Make Design, Make, assisted design features SaaS products and Operate Shift away from deign Creating unique IP Build out new Leveraging commodity Expand our paradigm to the applications that are cloud through foundation only to design and make AI tools and custom operation of buildings, native and integrate with by expanding into models that drive AI foundation models ...

Core Viewpoint - The article introduces Flame, an open-source multimodal large model solution aimed at modern front-end code generation, addressing the complexities and requirements of contemporary front-end development [1][25]. Group 1: Model Capabilities - Flame generates code that adheres to modern front-end development standards, featuring clear external styles and a modular component structure [4]. - Unlike top models like GPT-4o, which produce static components, Flame's approach allows for dynamic rendering and proper definition of component states and event responses [5][7]. Group 2: Data Challenges - The primary challenge for large visual language models (LVLM) in generating professional front-end code is the scarcity of high-quality training data [9][12]. - Existing datasets, such as websight, are inadequate as they only cover static HTML, failing to meet the needs of modern front-end frameworks like React [13]. Group 3: Data Synthesis Solutions - Flame's team proposes data synthesis as a solution to the data scarcity issue, employing a self-reflective intelligent workflow to generate high-quality data for front-end development [16]. - Three synthesis methods are designed: - Evolution-Based Synthesis, which generates diverse code variants through random evolution [18]. - Waterfall-Model-Based Synthesis, which ensures clear structure and logical consistency in generated code [20]. - Additive Development Synthesis, which incrementally adds functionality to existing code [22]. Group 4: Performance Evaluation - Flame's performance is evaluated using a high-quality test set of 80 items, with a focus on code that compiles correctly and adheres to coding standards [26]. - In comparison to leading models like GPT-4o, which achieved a maximum Pass@1 of only 11%, Flame reached over 52% under similar conditions, demonstrating significant potential [27]. - Flame accomplished this with approximately 200,000 data points, validating the effectiveness of its data synthesis methods [27].