MLA 023 Code AI Models & Modes

Machine Learning Guide

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14d ago 38:15

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Notes and resources at ocdevel.com/mlg/mla-23
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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.
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.

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