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Machine Learning Guide

MLA 023 Code AI Models & Modes

Machine Learning Guide

OCDevel

Artificial, Introduction, Learning, Courses, Technology, Ml, Intelligence, Ai, Machine, Education

4.9848 Ratings

🗓️ 13 April 2025

⏱️ 38 minutes

🧾️ Download transcript

Summary

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

Try a walking desk to stay healthy while you study or work!

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 ModelsOllama 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.

Transcript

Click on a timestamp to play from that location

0:00.0

Machine Learning applied episode 23.

0:03.2

This is Code AI Part 2.

0:05.5

I'm going to cover some more territory within vibe coding.

0:08.0

In particular in this episode, we're going to be talking about models and modes.

0:12.8

Models like the GPT series, 0103, DeepSeek R1, Claude 3.7, Gemini 2.5 Pro.

0:22.2

And he can't talk about models without talking about modes

0:25.2

because the different models work best under different modes.

0:28.6

And the two common modes being architect and code mode.

0:31.7

And these days, most Videcoding tools support at least both architect and code mode

0:36.4

and some of them support additional

0:38.2

custom modes.

0:39.7

Then I'll talk about local models, how you can download an open source model on your local

0:44.0

computer and use that instead of a cloud-hosted model, as well as fine-tuning models on

0:48.6

your codebase.

0:49.7

The next episode, I will cover tool use within the agents, contrast that to MCP or Model Context

0:57.1

protocol servers. And then finally, if you are a veteran of MLG and you're wondering why all the

1:03.3

app development talk can we get back to machine learning, at the end of the next episode,

1:08.0

I'll finally bring it all back to application of this tooling

1:11.4

as a machine learning engineer. Let's start with models. So the last episode I talked about

1:18.0

tools, I'm going to start this episode with models. Similar to the last episode, this episode will

1:24.3

be outdated as soon as it's released because the model landscape is constantly evolving.

1:30.5

They are leapfrogging each other every month, if not every week.

...

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