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@btroncone
btroncone / ngrxintro.md
Last active February 26, 2026 10:29
A Comprehensive Introduction to @ngrx/store - Companion to Egghead.io Series

Comprehensive Introduction to @ngrx/store

By: @BTroncone

Also check out my lesson @ngrx/store in 10 minutes on egghead.io!

Update: Non-middleware examples have been updated to ngrx/store v2. More coming soon!

Table of Contents

@staltz
staltz / comment.md
Created March 15, 2017 15:27
Nested Pick<T, K> in TypeScript 2.2

TypeScript supports Pick to allow you to get a "subset" object type of a given type, but there is no built-in Pick for deeper nested fields.

If you have a function that takes a large object as argument, but you don't use all of its fields, you can use Pick, Pick2, Pick3, etc to narrow down the input type to be only just what you need. This will make it easier to test your function, because when mocking the input object, you don't need to pass all fields of the "large" object.

@kutyel
kutyel / curry.js
Last active August 5, 2019 20:51
My own implementation of curry 🍛
// Ultimate version
const curry = (f, ...args) =>
f.length <= args.length
? f(...args)
: x => curry(f, ...args, x)
@mkcode
mkcode / .vscode-neovim-init.vim
Created October 5, 2022 20:17
Minimal yet functional config for VSpaceCode with VSCode-Neovim
" Our .vscode-neovim directory
let data_dir = '~/.vscode-neovim'
let plugFile = data_dir . '/plug.vim'
" Download plug.vim if it doesn't exist
" Then install the plugins in this file
if empty(glob(plugFile))
silent execute '!curl -fLo '.plugFile.' --create-dirs https://raw.githubusercontent.com/junegunn/vim-plug/master/plug.vim'
execute "autocmd VimEnter * PlugInstall --sync | source " . expand('%:p')
@rohitg00
rohitg00 / llm-wiki.md
Last active May 14, 2026 04:04 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.