therealparmesh

(async () => {
  try {
    const {
      sheet: { cssRules },
    } = document.getElementById('react-native-stylesheet');
    const cssText = Array.from(cssRules)
      .map(({ cssText }) => cssText)
      .filter(Boolean)
      .join('\n\n');
    console.log('--- Plain CSS Output ---');

therealparmesh

bunny() {
  local script
  script=$(bun run | sed 's/\x1b\[[0-9;]*m//g' | rg --replace '$2' '(\s+)\$ bun run (\S+)' | fzf)
  if [[ -n "$script" ]]; then
    bun run "$script"
  fi
}

therealparmesh

autoload -Uz compinit
compinit

setopt append_history
setopt auto_pushd
setopt autocd
setopt cdable_vars
setopt complete_in_word
setopt correct
setopt glob_dots

therealparmesh

The Reality of "Just a React App"

A complete breakdown of the architecture, tooling, and paradigms involved in modern frontend development. This highlights the true scope of work required when React is the default (and often only) option for frontend developers who are effectively React specialists.

---

Core Architecture & Language

• React: Virtual DOM, JSX
• Component Paradigms: Class component lifecycles, Higher-Order Components (HOCs), render props, Hooks
• Advanced Rendering: Suspense, Error Boundaries

therealparmesh

The Reality of "Just Render HTML"

A complete breakdown of the architecture, tooling, and paradigms involved when returning to a server-driven architecture using HTMX, Tailwind, and Vanilla JS. This highlights how drastically the frontend footprint shrinks when the server reclaims state and routing.

---

Core Architecture & Language

• Hypermedia: HTMX (HTML attributes, out-of-band swaps, lazy-loading partials)
• Language: Vanilla JS
• Component Paradigms: Server-side templating (partials, fragments, includes), standard HTML elements

therealparmesh

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.

therealparmesh

# kx - kubectl helpers with fzf
# deps: kubectl, fzf, jq, dpcli (mesh plugin for rollouts)
# flags are ours - short aliases for kubectl's verbose ones
# source from .zshrc

__kx_dim() { printf '\033[2m%s\033[0m\n' "$*" >&2; }
__kx_bold() { printf '\033[1m%s\033[0m\n' "$*" >&2; }
__kx_err() { printf '\033[31m%s\033[0m\n' "$*" >&2; }
__kx_ok() { printf '\033[32m%s\033[0m\n' "$*" >&2; }