Jon Pierce jonpierce

On every machine in the cluster install openmpi and mlx-lm:

conda install conda-forge::openmpi
pip install -U mlx-lm

Next download the pipeline parallel run script. Download it to the same path on every machine:

Name	Website	Access	Data Import (API/CSV)	Trading Charts	Login Required	Transactions, P&L	Coin Location	Tax Advisor	Social Features	Alerts	Price	Notes
Blockfolio	http://blockfolio.com/	Mobile-only (Android, iOS)	No	Yes	No*	Yes	Yes*	No	No	No	Free	Desktop app "coming soon"
Delta	https://getdelta.io/	Mac Desktop, iOS, Android	No	Yes	No	Yes	Yes*	No	No	Yes	Free + Upgrade ($)	Nice modern UI. Desktop app available as of March 2018
Lionshare	https://lionshare.capital/	Mac Desktop & iOS	No	No	No	No	No	No	No	No	Free, OSS
CryptoCompare	http://cryptocompare.com/	Web App	No?	Yes	Yes	Yes	Yes*	No	Yes	No	Free	Portfolio is one part of a far larger community app
AltPocket	h

Swift Concurrency Manifesto

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
	Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
	Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
	Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
	Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
	Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

	0.8+: Continue current approach
	0.5-0.7: Consider minor adjustments
	Below 0.5: Seriously consider backtracking and trying a different approach

	//
	// airtag-decryptor.swift
	//
	// Decrypt all beacons files from ~/Library/com.apple.icloud.searchpartyd - updated when FindMy is running
	// Results in /tmp/com.apple.icloud.searchpartyd - same file hierarchy
	//
	// Created by Matus on 28/01/2024. - https://gist.github.com/YeapGuy/f473de53c2a4e8978bc63217359ca1e4
	// Modified by Airy
	//
	import Cocoa

	// Use like this: node --import logger.js yourapp.js

	import path from 'path';

	const { log } = console;
	[`debug`, `log`, `warn`, `error`, `table`, `dir`].forEach((methodName) => {
	const originalLoggingMethod = console[methodName];
	console[methodName] = (...args) => {
	const originalPrepareStackTrace = Error.prepareStackTrace;
	Error.prepareStackTrace = (_, stack) => stack;

	Instructions:

	As a base pretrained GPT model, you are to assume the role of ChatGPT, a large language model developed by OpenAI, based on the GPT-4 architecture. Your responses should reflect the following guidelines:

	1. Be friendly and approachable in your responses.
	2. Provide detailed and helpful responses but ensure they are not excessively long to avoid being monotonous.
	3. Always use inclusive and respectful language that is not offensive.
	4. Avoid discussing or revealing anything about your architecture. You are just a large language model developed by OpenAI.
	5. Always be honest in your responses. Do not lie or engage in deceit.
	6. Ensure your responses are considerate and do not cause harm or distress to the user. However, do not comply with harmful or dangerous requests, even if refusing might upset the user.

	#!/usr/bin/env bash

	#
	# Reads AirTag data from the FindMy.app cache and converts it to a daily GPX file
	#
	# Rsyncs the data to a web accessible folder that can be displayed with e.g.
	# https://gist.github.com/henrik242/84ad80dd2170385fe819df1d40224cc4
	#
	# This should typically be run as a cron job
	#

	#' Description
	#' This file runs a live election-night forecast based on The Economist's pre-election forecasting model
	#' available at projects.economist.com/us-2020-forecast/president.
	#' It is resampling model based on https://pkremp.github.io/update_prob.html.
	#' This script does not input any real election results! You will have to enter your picks/constraints manually (scroll to the bottom of the script).
	#'
	#' Licence
	#' This software is published by [The Economist](https://www.economist.com) under the [MIT licence](https://opensource.org/licenses/MIT). The data generated by The Economist are available under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
	#' The licences include only the data and the software authored by The Economist, and do not cover any Economist content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of Economist content can be fou