MLA 023 Code AI Models & Modes

Machine Learning Guide

Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes.

Links

• Notes and resources at ocdevel.com/mlg/mla-23
• Try a walking desk stay healthy & sharp while you learn & code

Model Current Leaders

According to the Aider Leaderboard (as of April 12, 2025), leading models include for vibe-coding:

• Gemini 2.5 Pro Preview 03-25: most accurate and cost-effective option currently.
• Claude 3.7 Sonnet: Performs well in both architect and code modes with enabled reasoning flags.
• DeepSeek R1 with Claude 3.5 Sonnet: A popular combination for its balance of cost and performance between reasoning and non-reasoning tasks.

Local Models

• Tools for Local Models: Ollama is the standard tool to manage local models, enabling usage without internet connectivity.
• Best Models per VRAM: See this Reddit post, but know that Qwen 3 launched after that; and DeepSeek R1 is coming soon.
• Privacy and Security: Utilizing local models enhances data security, suitable for sensitive projects or corporate environments that require data to remain onsite.
• Performance Trade-offs: Local models, due to distillation and size constraints, often perform slightly worse than cloud-hosted models but offer privacy benefits.

Fine-Tuning Models

• Customization: Developers can fine-tune pre-trained models to specialize them for their specific codebase, enhancing relevance and accuracy.
• Advanced Usage: Suitable for long-term projects, fine-tuning helps models understand unique aspects of a project, resulting in consistent code quality improvements.

Tips and Best Practices

• Judicious Use of the @ Key: Improves model efficiency by specifying the context of commands, reducing the necessity for AI-initiated searches.
  • Examples include specifying file paths, URLs, or git commits to inform AI actions more precisely.
• Concurrent Feature Implementation: Leverage tools like Boomerang mode to manage multiple features simultaneously, acting more as a manager overseeing several tasks at once, enhancing productivity.
• Continued Learning: Staying updated with documentation, particularly Roo Code's, due to its comprehensive feature set and versatility among AI coding tools.