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<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

NEVER use meta-phrases (e.g., "let me help you", "I can see that").
NEVER summarize unless explicitly requested.
NEVER provide unsolicited advice.
NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
ALWAYS be specific, detailed, and accurate.

ZSH CheatSheet

This is a cheat sheet for how to perform various actions to ZSH, which can be tricky to find on the web as the syntax is not intuitive and it is generally not very well-documented.

Strings

Description	Syntax
Get the length of a string	`${#VARNAME}`
Get a single character	`${VARNAME[index]}`

Summary of Fastai.ai's in-depth discussion of types of normalization

# simulated batch of images
x = torch.rand(64, 3, 224, 224)
# or some number of layers up the convolutional stack
x = torch.rand(64, 256, 32, 32)

BatchNorm:

	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

	# NOTE:
	# You can find an updated, more robust and feature-rich implementation
	# in Zeno Build
	# - Zeno Build: https://github.com/zeno-ml/zeno-build/
	# - Implementation: https://github.com/zeno-ml/zeno-build/blob/main/zeno_build/models/providers/openai_utils.py

	import openai
	import asyncio
	from typing import Any

	import os; import psutil; import timeit
	from datasets import load_dataset

	mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20
	wiki = load_dataset("wikipedia", "20200501.en", split='train')
	mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20
	print(f"RAM memory used: {(mem_after - mem_before)} MB")

	s = """batch_size = 1000
	for i in range(0, len(wiki), batch_size):

	from tkinter import *
	from PIL import ImageTk,Image
	import time
	import os

	targetImageWidth = 850
	targetImageHeight = 400

	inputImageWidth = 0
	inputImageHeight = 0

	# Authors: Mathieu Blondel, Vlad Niculae
	# License: BSD 3 clause

	import numpy as np


	def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
	rng = np.random.RandomState(random_state)

	# if tuple, interpret as randn