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Rodrigo Bondoc rbondoc96

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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.

@t3dotgg
t3dotgg / model-prices.csv
Last active April 30, 2026 01:06
Rough list of popular AI models and the cost to use them (cost is per 1m tokens)
Name Input Output
Gemini 2.0 Flash-Lite $0.075 $0.30
Mistral 3.1 Small $0.10 $0.30
Gemini 2.0 Flash $0.10 $0.40
ChatGPT 4.1-nano $0.10 $0.40
DeepSeek v3 (old) $0.14 $0.28
ChatGPT 4o-mini $0.15 $0.60
Gemini 2.5 Flash $0.15 $0.60
DeepSeek v3 $0.27 $1.10
Grok 3-mini $0.30 $0.50
bind-key C-b send-prefix
bind-key C-o rotate-window
bind-key C-z suspend-client
bind-key Space next-layout
bind-key ! break-pane
bind-key " split-window
bind-key # list-buffers
bind-key $ command-prompt -I #S "rename-session '%%'"
bind-key % split-window -h
bind-key & confirm-before -p "kill-window #W? (y/n)" kill-window