staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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DarrenN

# Version key/value should be on his own line
PACKAGE_VERSION=$(cat package.json \
  | grep version \
  | head -1 \
  | awk -F: '{ print $2 }' \
  | sed 's/[",]//g')

echo $PACKAGE_VERSION

gagarine

Installing Fish shell on MacOS (Intel and M1) using brew

Fish is a smart and user-friendly command line (like bash or zsh). This is how you can instal Fish on MacOS and make your default shell.

Note that you need the https://brew.sh/ package manager installed on your machine.

Install Fish

brew install fish

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.