Joe Martinez capitalist

Jujutsu (`jj`) Quick Reference (For Users and Agents)

For Git Users: The Mental-Model Shifts

The command-translation table below is necessary but not sufficient. A few git assumptions don't carry over — internalize these and the rest of jj stops looking weird.

1. No staging area. Your working copy IS a commit (the @ commit). Every jj command auto-snapshots it. There's no add step and no index. When done, jj commit -m finalizes it and opens a fresh empty one. To split changes (the git add -p workflow), commit everything and then jj split.

2. Commits have two IDs: change_id (stable) and commit_id (mutates). In git, rewriting (rebase, amend) creates brand-new SHAs and the originals fall into reflog. In jj, the change_id is a stable identity that persists through rewrites — only the commit_id (the Git SHA equivalent) changes. Bookmarks and references survive rebases/squashes by tracking the change_id. jj evolog shows every prior commit_id of one change_id.

Visualize primary and replicas (inspired by Waterpark)

Mix.install([
  {:kino, "~> 0.16.1"}
], consolidate_protocols: false)

Library modules

How large are large language models? (2025)

This aims to be factual information about the size of large language models. None of this document was written by AI. I do not include any information from leaks or rumors. The focus of this document is on base models (the raw text continuation engines, not 'helpful chatbot/assistants'). This is a view from a few years ago to today of one very tiny fraction of the larger LLM story that's happening.

History

GPT-2,-medium,-large,-xl (2019): 137M, 380M, 812M, 1.61B. Source: openai-community/gpt2. Trained on the unreleased WebText dataset said to 40GB of Internet text - I estimate that to be roughly 10B tokens. You can see a list of the websites that went into that data set here domains.txt.
GPT-3 aka davinci, davinci-002 (2020): 175B parameters. There is a good breakdown of how those parameters are 'spent' here [How d

Generating Code

Use list_generators to list available generators when available, otherwise mix help. If you have to run generator tasks, pass --yes. Always prefer to use generators as a basis for code generation, and then modify afterwards.

Tools

Use tidewave MCP tools when available, as they let you interrogate the running application in various useful ways.

Logs & Tests

View Markup Language (VML) Specification - SwiftUI

Version 1.0-alpha.1

1. Overview

View Markup Language (VML) is a markup language derived from SGML, designed to represent composable UI frameworks for unidirectional, server-driven rendering. This specification defines the conversion from a server-side view model, such as SwiftUI, to VML.

Specification Note 1.1: Scope The VML specification is concerned exclusively with the structure and rendering of views. It is a unidirectional standard (server-to-client). Mechanisms for handling user interactions, actions, and client-to-server state changes are explicitly outside the scope of this document.

Have GPT-4 write a song

Mix.install(
  [
    {:midiex, "~> 0.6.1"},
    {:instructor, "~> 0.0.5"}
  ],
  config: [
    instructor: [

	- name: Overprovision like the pros'
	hosts: all
	tasks:
	- name: Install early OOM killer and zram
	ansible.builtin.apt:
	pkg:
	- earlyoom
	- zram-tools
	- name: Configure early OOM killer
	ansible.builtin.lineinfile:

	defmodule ElixirAiAgent do
	@moduledoc """
	A minimal ai agent implementation showcasing how simple it is to build AI agents in Elixir.
	Combines agent logic and server management in one module.
	"""

	use GenServer

	alias LangChain.Chains.LLMChain
	alias LangChain.Message

	#!/usr/bin/env bash

	# Abort sign off on any error
	set -e

	# Start the benchmark timer
	SECONDS=0

	# Repository introspection
	OWNER=$(gh repo view --json owner --jq .owner.login)