nicekate nicekate

Introduction

Apple restricted the access to Xcode LLM (Predictive code completion) feature on China models of Mac. This guide provides a way to bypass that restriction. It's verified on macOS 15.0 Beta (24A5264n), but there is no guarentee that it will always work on later macOS versions.

Prerequisites

Xcode is installed and run at least once.
SIP debugging restrictions are disabled (via csrutil enable --without debug command in recovery mode).

Disclaimer

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

	<!DOCTYPE html>
	<html lang="ja">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Realtime Object Detection</title>
	<style>
	body { font-family: Arial, sans-serif; }
	#canvas { border: 1px solid #000; }
	table { border-collapse: collapse; margin-top: 20px; }

	<artifacts_info>
	The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

	# Good artifacts are...
	- Substantial content (>15 lines)
	- Content that the user is likely to modify, iterate on, or take ownership of
	- Self-contained, complex content that can be understood on its own, without context from the conversation
	- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
	- Content likely to be referenced or reused multiple times

	import json
	import os
	import ollama


	def query_ollama(prompt, model='openhermes:7b-mistral-v2.5-q6_K', context=''):
	response = ollama.generate(
	model=model,
	prompt=context + prompt)
	return response['response'].strip()

	import requests
	import json
	import gradio as gr

	url = "http://localhost:11434/api/generate"

	headers = {
	'Content-Type': 'application/json',
	}

	// WARNING：此脚本仅做学习和演示用途，在不了解其用途前不建议使用
	// 本脚本的用途是将输入内容分页，每次提取一页内容，编辑第二条消息，发送，然后收集结果
	// 使用前，需要有两条消息，参考模板 https://chat.openai.com/share/17195108-30c2-4c62-8d59-980ca645f111
	// 演示视频： https://www.bilibili.com/video/BV1tp4y1c7ME/?vd_source=e71f65cbc40a72fce570b20ffcb28b22
	//
	(function (fullText) {
	const wait = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

	const groupSentences = (fullText, maxCharecters = 2800) => {
	const sentences = fullText.split("\n").filter((line) => line.trim().length > 0);