Best AI Model for Complex Coding

Claude Code

• Claude Code is recommended for complex architecture discussions and questions.
• It can handle large changes, although some users feel it changes code too much and is too opinionated.

Swe 1

• Swe 1 is often used for most tasks due to its cost efficiency.
• It may not be the best model but offers unlimited usage during promotional pricing.

Gemini 2.5 Pro

• Gemini 2.5 Pro is valued for its intelligence and capabilities.
• It is preferred for complex tasks that require high-level performance.

Memory Bank Prompt in Cline

• Cline's Memory Bank Prompt is highlighted for its ability to maintain project knowledge and context across sessions.
• It helps with consistent development, self-documentation of projects, and works with any tech stack or language.

ChatGPT and LLMs

• ChatGPT and other LLMs are regarded as valuable tools for certain tasks but are seen as inefficient when it comes to actual work due to limitations.
• They are banned on platforms like Stack Overflow for generating incorrect or harmful content.
• Users note that LLMs struggle with multi-step instructions and character-level reasoning.

Training Considerations

• Users recommend being cautious with learning rate, schedule, and optimizer choices during training, as these can impact model performance.
• Fine-tuning models like DiTs from scratch may require specific optimizations such as warmup and constant learning rate schedules.
• Suggestions such as adjusting AdamW parameters and considering the distribution of timesteps for training are beneficial for improving model performance.

Additional Tools and Techniques

• Tools like Context Portal and Context7 are mentioned for managing large codebases efficiently.
• Users discuss the effectiveness of paradigms like Swe 1 Cognitive Load and Claude Code Thinking which offer varying benefits.
• Various strategies like Memory Banks and Prioritizing Feedback Loops are highlighted for enhancing AI interactions and project outcomes.
• Experimentation with Diff2Flow for training and fine-tuning models, and exploring different LR schedules for optimization are encouraged for better results.

Model Selection and Finetuning

• Users stress the importance of selecting the right model for specific tasks to achieve desired outcomes.
• Thoughts on lr tuning, warmup, and constant learning rate schedules and the impact of different parameters on model convergence and generalization are shared.
• Experiments with prior coding challenges to assess model diversity and generalization, and the significance of using real negative samples for improving CFG are acknowledged.

This comprehensive summary covers user experiences, recommendations, and best practices related to utilizing AI models for complex coding tasks.