Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
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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.
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.
| body { | |
| font-family: Arial, sans-serif; | |
| display: flex; | |
| justify-content: center; | |
| align-items: center; | |
| height: 100vh; | |
| margin: 0; | |
| background-color: #331F19; /* Fundo neutro */ | |
| } | |
| .container { |
| Hey Guys!! | |
| This is a gist which teach you how to install VIM in your terminal or termux. | |
| [_]----[_] | |
| Just Follow my setp. | |
| pkg install vim | |
| vim installed successfully but it's not compatible to install PlugIn's. | |
| For that we need to install VundleVIM.Here we go |
| <!DOCTYPE html> | |
| <html lang="pt-BR"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Página inicial</title> | |
| </head> | |
| <body> | |
| <div> | |
| <h1>Boas vindas!</h1> |
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