Tejas Khot tejaskhot

I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:

TLDR: Manus AI Agent Report

Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox

	# http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

	def exp_lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=7):
	"""Decay learning rate by a factor of 0.1 every lr_decay_epoch epochs."""
	lr = init_lr * (0.1**(epoch // lr_decay_epoch))

	if epoch % lr_decay_epoch == 0:
	print('LR is set to {}'.format(lr))

	for param_group in optimizer.param_groups:

	import tensorflow as tf

	"""
	Author : @MikiBear_
	Tensorflow Implementation of Bilinear Additive Upsampling.
	Reference : https://arxiv.org/abs/1707.05847
	"""
	def bilinear_additive_upsampling(x, to_channel_num, name):
	from_channel_num = x.get_shape().as_list()[3]
	assert from_channel_num % to_channel_num == 0

	'''
	This script is based on the original work of Randal S. Olson (randalolson.com) for the Traveling Salesman Portrait project.
	http://www.randalolson.com/2018/04/11/traveling-salesman-portrait-in-python/

	Please check out the original project repository for information:

	https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects

	The script was updated by Joshua L. Adelman, adapting the work of Antonio S. Chinchón described in the following blog post:
	https://fronkonstin.com/2018/04/17/pencil-scribbles/

	import torch


	def chamfer_distance_without_batch(p1, p2, debug=False):

	'''
	Calculate Chamfer Distance between two point sets
	:param p1: size[1, N, D]
	:param p2: size[1, M, D]
	:param debug: whether need to output debug info

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference

	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