An attempt to make a list of the supported ways to make a table with checkboxes in Markdown.
Results as of October 2023.
Below is the style element that formats the colors of the colored check mark emojis.
| brew install pandoc | |
| brew tap homebrew/cask | |
| brew install --cask basictex | |
| eval "$(/usr/libexec/path_helper)" | |
| # Update $PATH to include `/usr/local/texlive/2022basic/bin/universal-darwin` | |
| sudo tlmgr update --self | |
| sudo tlmgr install texliveonfly | |
| sudo tlmgr install xelatex | |
| sudo tlmgr install adjustbox | |
| sudo tlmgr install tcolorbox |
| from jax import random, jit | |
| import jax.numpy as jnp | |
| from jax.scipy import stats | |
| from util import ravelize_function, make_log_density | |
| __all__ = ["log_density", "log_density_vec", "init_draw_zero"] | |
| def constrain_parameters(sigma_unc, alpha, beta): |
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.