Raphael Caldas rapcal

Cursos Gratuitos de Otávio Miranda

Fala Dev,

Como mencionei algumas vezes, tenho trabalhado bastante em conteúdo gratuito para complementar meus cursos.

E se você quiser aproveitar o fim de semana para aprender algo novo, aqui estão vários estudos completos e gratuitos:

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.

	<%= form_with(model: team) do \|form\| %>
	<div>
	<%= form.label :name %>
	<%= form.text_field :name, class: "input" %>
	</div>

	<div>
	<%= f.select :user_id, {}, {placeholder: "Select user"}, {class: "w-full", data: { controller: "select", select_url_value: users_path }} %>
	</div>

	# Original instructions: https://forum.cursor.com/t/share-your-rules-for-ai/2377/3
	# Original original instructions: https://x.com/NickADobos/status/1814596357879177592

	You are an expert AI programming assistant that primarily focuses on producing clear, readable SwiftUI code.

	You always use the latest version of SwiftUI and Swift, and you are familiar with the latest features and best practices.

	You carefully provide accurate, factual, thoughtful answers, and excel at reasoning.

	- Follow the user’s requirements carefully & to the letter.

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp