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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

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Reverse Engineering AntiGravity browser (Step by Step)

AntiGraviti tools parsed based on reverse engineering and ParseToolArgs structs found in the binary. We need strings, the juice is in the DATA block and not in the code.

I have used the following commands on my MacOS:

1. Extract strings to file for faster iterations
# strings /Applications/Antigravity.app/Contents/Resources/app/extensions/antigravity/bin/language_server_macos_arm > ~/Downloads/language_server_macos_arm_strings.txt

2. Search tools

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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

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package main

import (
	"fmt"
	"runtime"
	"testing"
	"unsafe"
)

func toBytes(s string) []byte {

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#include <bcc/proto.h>
#include <uapi/linux/ip.h>
#include <uapi/linux/ipv6.h>
#include <uapi/linux/icmp.h>
#include <uapi/linux/tcp.h>
#include <uapi/linux/udp.h>
#include <uapi/linux/icmpv6.h>
#include <net/inet_sock.h>
#include <linux/netfilter/x_tables.h>

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000000	Officially Xerox
000001	SuperLAN-2U
000002	BBN (was internal usage only, no longer used)
000003	XEROX CORPORATION
000004	XEROX CORPORATION
000005	XEROX CORPORATION
000006	XEROX CORPORATION
000007	XEROX CORPORATION
000008	XEROX CORPORATION
000009	powerpipes?

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• RDMA Aware Networks Programming User Manual
• On the Impact of Cluster Configuration on RoCE Application Design
• RDMA over Commodity Ethernet at Scale
• Design Guidelines for High Performance RDMA Systems
• FaSST: Fast, Scalable and Simple Distributed Transactions with Two-sided (RDMA) Datagram RPCs
• RDMA [1]: A short history of remote DMA networking
• [Slide] RDMA Tutorial
• Understanding the concepts and mechanisms of RDMA
• [InfiniBand RDMA over PCI Express Networ

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0. Introduction

Notes about software RDMA interoperability to communicate with hosts which have RDMA capable network adapters.

1. Documentation for software RDMA

Looked for documentation about how to use software RDMA

1.1 Red Hat Enterprise Linux 8

smallnest

package main

import (
	"fmt"
	"strings"
	"time"
)

func handleEventError(tokenName string, line, pos int) error {
	err := fmt.Errorf("error at line %d, pos %d: %s", line, pos, tokenName)

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;; 作者: 一泽Eze
;; 名称：创意名片生成
;; 版本: 1.0
;; 模型: Claude-3.5-sonnet
;; 用途: 根据公司Logo生成灵活、创意的名片模板，准确反映品牌调性

;; 定义未实现的函数（占位符）
(define (分析 elements)
  ;; 实现分析逻辑
  '分析结果)