As configured in my dotfiles.
start new:
tmux
start new with session name:
As configured in my dotfiles.
start new:
tmux
start new with session name:
| wget --continue --no-check-certificate -O jdk-8-linux-x64.tar.gz --header Cookie: oraclelicense=a http://download.oracle.com/otn-pub/java/jdk/8-b132/jdk-8-linux-x64.tar.gz |
| # maximum capability of system | |
| user@ubuntu:~$ cat /proc/sys/fs/file-max | |
| 708444 | |
| # available limit | |
| user@ubuntu:~$ ulimit -n | |
| 1024 | |
| # To increase the available limit to say 200000 | |
| user@ubuntu:~$ sudo vim /etc/sysctl.conf |
| SELECT table, | |
| formatReadableSize(sum(bytes)) as size, | |
| min(min_date) as min_date, | |
| max(max_date) as max_date | |
| FROM system.parts | |
| WHERE active | |
| GROUP BY table |
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:
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