ruvnet

How To Train Your Own AI Models for Free Using Google AI Studio

Introduction: Why Fine-Tuning AI Models Matters

This year, we've seen some remarkable leaps in the world of Large Language Models (LLMs). Models like O1, GPT-4o, and Claude Sonnet 3.5 have shown how far LLM capabilities have come, pushing the boundaries of coding, reasoning, and self-reflection. O1, in particular, is one of the best models on the market, known for its self-reflection capabilities, which allows it to iteratively improve its reasoning over time. GPT-4o offers a wide range of capabilities, making it incredibly versatile across tasks, while Claude Sonnet 3.5 excels at coding, solving complex problems with higher efficiency.

What many people don’t realize is that these high-performing models are essentially fine-tuned versions of underlying models. Fine-tuning allows these models to be optimized for specific tasks, making them more useful for things like analysis, coding, and decision-making

texchi2

Setting up a Remote AI Code Assistant: Ollama + Continue in VS Code

Figure 1: Remote development setup with MacStudio as server and VS Code integration

A step-by-step guide to creating a powerful, private AI coding assistant using Ollama and Continue extension in VS Code, with remote server capabilities.

Introduction

KGHague

function ConvertTo-PackedGuid {
    <#
	.SYNOPSIS
		https://gist.github.com/MyITGuy/d3e039c5ec7865edefc157fcd625a20a
        Converts a GUID string into a packed globally unique identifier (GUID) string.
	.DESCRIPTION
		Takes a GUID string and breaks it into 6 parts. It then loops through the first five parts and reversing the order. It loops through the sixth part and reversing the order of every 2 characters. It then joins the parts back together and returns a packed GUID string.
	.EXAMPLE
		ConvertTo-PackedGuid -Guid '{7C6F0282-3DCD-4A80-95AC-BB298E821C44}'
	

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.