Quantitative Models and Construction Methods Model Name: Jet-Nemotron - **Model Construction Idea**: The model is built using an innovative post-neural architecture search method, focusing on optimizing pre-trained Transformer models[15][16] - **Model Construction Process**: - Start with a pre-trained full-attention model and inherit its multi-layer perceptron weights - Use PostNAS method to determine the optimal placement of full-attention layers by training a "one-time" super network - Evaluate various linear attention modules and select Gated DeltaNet as the base, then design the JetBlock module with dynamic causal convolution kernels - Perform hardware-aware architecture search to ensure efficiency in real hardware deployment[16][17][19] - **Model Evaluation**: The model demonstrates significant performance and efficiency improvements, setting a new benchmark for linear attention design[20][22] Model Backtest Results Jet-Nemotron - **MMLU Accuracy**: 49.6[19] - **Common Sense Reasoning Accuracy**: 62.0[19] - **Throughput Improvement**: 47 times compared to Qwen3-1.7B-Base[19] - **Cache Size Reduction**: Reduced to one forty-seventh of the original size[19] Quantitative Factors and Construction Methods Factor Name: RLCF (Reinforcement Learning from Checklist Feedback) - **Factor Construction Idea**: Use dynamically generated checklists to evaluate model responses, providing a more effective alignment method compared to traditional reward models[48][49] - **Factor Construction Process**: - Define checklist core features: each item must be a verifiable yes/no question - Generate checklists using direct and candidate methods - Sample candidate response pairs from the base policy - Score each checklist item using AI judges and verification programs - Calculate weighted average scores and filter significantly different response pairs - Train using direct preference optimization[49][51][52] - **Factor Evaluation**: The method shows stable improvement in instruction adherence across various benchmarks, particularly excelling in handling "content" constraints[51][52] Factor Backtest Results RLCF - **IFEval Improvement**: 2.8-3.0%[51] - **FollowBench Constraint Satisfaction Level**: 8.2% improvement[51] - **InFoBench Overall Requirement Adherence Rate**: 6.9% improvement[51] - **Content Constraint Hard Satisfaction Rate**: 6.4 percentage points higher than baseline[51]

Core Viewpoint - The article discusses the emerging concept of "Vibe Coding," which allows individuals to create software applications using AI tools without extensive programming knowledge, highlighting its potential to democratize software development and enhance productivity [1][9]. Group 1: Concept of Vibe Coding - "Vibe Coding" is introduced as a method where AI assists in coding, enabling users to develop applications quickly, as demonstrated by Andrej Karpathy's example of creating an iOS app in one hour without prior knowledge of Swift [1][3]. - The rise of AI-assisted coding tools, such as Cursor and Tencent's CodeBuddy, indicates a competitive landscape in the AI programming assistant market, enhancing developers' capabilities [3][4]. Group 2: Success Stories and Frameworks - Developers are sharing their success stories using Vibe Coding, with one user reporting a monthly recurring revenue (MRR) of $7,000 within 30 days of launching an AI product solely using AI tools [5][7]. - The "Vibe Coding entrepreneurial framework" is outlined as a simple process involving one AI tool for building, another for email outreach, and ChatGPT for market insights, showcasing a streamlined approach to product development [7][8]. Group 3: Perspectives on AI in Coding - David Heinemeier Hansson (DHH) emphasizes the importance of maintaining a human touch in coding, arguing that while AI can assist, it should not replace the joy of programming [11][15]. - The article presents contrasting views from developers, with some appreciating AI for alleviating repetitive coding tasks, while others express concern about losing the essence of coding as a creative endeavor [18][21]. Group 4: Market Implications and Future of Coding - The discussion highlights that AI is not just a tool but a new layer of abstraction in programming, suggesting that the future of coding may involve a blend of human creativity and AI efficiency [22]. - The potential of Vibe Coding to lower barriers for non-programmers to engage in software development is noted, indicating a shift towards a more inclusive tech landscape [24].