Nyimbi Odero nyimbi

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

Lean Canvas

The Lean Canvas is a version of the Business Model Canvas adapted by Ash Maurya specifically for startups. You can read more about it here.

Problem

Electron apps

Slide deck frameworks supporting Markdown source

	[
	{
	"name": "Andorra",
	"countryCode": "AD",
	"countryCodeAlpha3": "AND",
	"phone": "376",
	"currency": "EUR",
	"flag": "https://www.geonames.org/flags/x/ad.gif",
	"symbol": "\u20AC",
	"stateProvinces": [

	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

	# ref https://zenn.dev/syoyo/articles/9a159ee747835a

	import sys

	from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

	max_length = 512

	# ref https://huggingface.co/facebook/nllb-200-distilled-1.3B
	# The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation.

	"""
	This file demonstrated using the "FetchedValue" feature of SQLAlchemy for
	values which are generated by the DB, without knowing how they are generated
	in the DB.
	"""
	from sqlalchemy import Column, DateTime, FetchedValue, Unicode, create_engine
	from sqlalchemy.ext.declarative import declarative_base
	from sqlalchemy.orm import sessionmaker

	Base = declarative_base()

	# import logging
	# import sys

	# Uncomment to see debug logs
	# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
	# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

	from llama_index import SimpleDirectoryReader, StorageContext
	from llama_index.indices.vector_store import VectorStoreIndex
	from llama_index.vector_stores import PGVectorStore

	1. # create new .py file with code found below
	2. # install ollama
	3. # install model you want “ollama run mistral”
	4. conda create -n autogen python=3.11
	5. conda activate autogen
	6. which python
	7. python -m pip install pyautogen
	7. ollama run mistral
	8. ollama run codellama
	9. # open new terminal

	#!/bin/bash

	if (( $(id -u) )) ; then
	echo "This script needs to run as root"
	exit 1
	fi

	if [[ -z "$1" ]] ; then
	echo "Usage: $(basename $0) [username] [realname (optional)]"
	exit 1