[Powershell] Powershell script compilation (Turning PS1 into EXE)

 C:\tmp>powershell -File hello.ps1

Hello World

C:\tmp>powershell -Command "Install-Module -Name ps2exe -Scope CurrentUser"

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PowerShellGet requires NuGet provider version '2.8.5.201' or newer to interact with NuGet-based repositories. The NuGet

 provider must be available in 'C:\Program Files\PackageManagement\ProviderAssemblies' or

'C:\Users\Chaoyi-H\AppData\Local\PackageManagement\ProviderAssemblies'. You can also install the NuGet provider by

running 'Install-PackageProvider -Name NuGet -MinimumVersion 2.8.5.201 -Force'. Do you want PowerShellGet to install

and import the NuGet provider now?

[Y] Yes  [N] No  [S] Suspend  [?] Help (default is "Y"): Y

Untrusted repository

You are installing the modules from an untrusted repository. If you trust this repository, change its

InstallationPolicy value by running the Set-PSRepository cmdlet. Are you sure you want to install the modules from

'PSGallery'?

[Y] Yes  [A] Yes to All  [N] No  [L] No to All  [S] Suspend  [?] Help (default is "N"): A

C:\tmp>Powershell -Command "Invoke-ps2exe -inputFile "hello.ps1" -outputFile "Hello.exe""

PS2EXE-GUI v0.5.0.34 by Ingo Karstein, reworked and GUI support by Markus Scholtes

Reading input file C:\tmp\hello.ps1

Compiling file...

Output file C:\tmp\Hello.exe written

C:\tmp>Hello.exe

Hello World

[LLM] Skill Markdown (md) samples

 https://github.com/anthropics/skills/

[AI] [LLM] Prompt Engineering

4 pillers: Action, Background, Outcome, and Constraint

Shot Spectrum: Give it some examples

Chain-of-Thought: "and explain your reasoning step by step"

Prompt Templates: Customization

[certificate] [NAU] M.CIT. degree

 

[LLM] llmfit dynamically selecting appropriate models

 Environment:

# uname -a

Linux demonalex-VirtualBox 7.0.0-14-generic #14-Ubuntu SMP PREEMPT_DYNAMIC Mon Apr 13 11:09:53 UTC 2026 x86_64 GNU/Linux

Installation:

# curl -fsSL https://llmfit.axjns.dev/install.sh | sh

Running:

# llmfit

Check Gmail Postmaster Tools

 Register your domain at postmaster.google.com to monitor:

Domain reputation

IP reputation

Authentication pass rates

Delivery errors in detail

Homoglyph Sproofing / Typo

 "rn" -> "m"

"cl" -> "d"

"ci" -> "a"

"vv" -> "w"

"Il" -> "ll"

"1" -> "l"

"I" -> "l"

"nn" -> "m"

"M0N" -> "MON"

[LLM] llmster

 Switch to a working folder:

# cd /usr/local/sbin

Show what kernel my VPC owns:

# uname -a

Linux demonalex-VirtualBox 7.0.0-14-generic #14-Ubuntu SMP PREEMPT_DYNAMIC Mon Apr 13 11:09:53 UTC 2026 x86_64 GNU/Linux

Install llmster:

# curl -fsSL https://lmstudio.ai/install.sh | bash

Add the following into /etc/profile:

export PATH="/root/.lmstudio/bin:$PATH"

Open a new terminal and execute the following to see the model list:

# lms get

Press Ctrl+C to end the showing.

Download the model:

# lms get google/gemma-4-e2b

Check if the model has already been downloaded:

# lms ls

Have an interactive chatbot:

# lms chat

Type "/exit" can exit the chatbot conversation.

Delete a downloaded model:

# cd ~/.lmstudio/models/lmstudio-community

# rm -Rf ./[MODEL]

[AI][ML][LLM]Run Llama 3B through Docker Model

 Install necessary plugin:

# curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

# echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# apt-get update

# apt-get install docker-model-plugin

Pull:

# docker model pull ai/llama3.2

Run in Interactive mode:

# docker model run ai/llama3.2

Exit Interactive mode:

> /bye

Run in Single-prompt mode:

# docker model run ai/llama3.2 "Explain how Docker containers work in one sentence."

-----Failed to carry out from here:

Enable OpenAI mode:

# docker desktop enable model-runner --tcp 12434

Test OpenAI:

# curl http://localhost:12434/engines/v1/models

Remotely use through OpenAI:

# curl http://localhost:12434/engines/v1/chat/completions \

  -H "Content-Type: application/json" \

  -d '{

    "model": "ai/llama3.2",

    "messages": [{"role": "user", "content": "Hello!"}]

  }'

[Windows][Sniffer] Microsoft Pktmon command-based sniffer

#Add filter:

pktmon filter add MyFilter -i 192.168.215.11

#OR

pktmon filter add MyFilter -i 192.168.1.100 -p 443

pktmon filter add MyFilter -i 192.168.1.100 -p 443 -t tcp

#Start:

pktmon start --capture --log-mode real-time

#Stop:

pktmon stop

#Remove filter:

pktmon filter remove

[Linux][Shell] tmux substituting the screen command

 Installation:

#apt-get update

#apt-get install tmux

How to use:

#tmux

Shortcut:

Ctrl+b c: creation

Ctrl+b x: Exit

Ctrl+b %: Split screen

Ctrl+b ;: Switch screen

[Weka][Machine Learning][Kmeans]Weka labelling result of Kmeans

### The Step-by-Step Process

1. **Run your Clusterer:**

* Go to the **Cluster** tab.

* Select **SimpleKMeans** and configure your  (number of clusters).

* Run the algorithm. Ensure it completes successfully.

2. **Add the Labels via Filter:**

* Switch back to the **Preprocess** tab.

* Click **Choose** under the Filters section.

* Navigate to: `filters` -> `unsupervised` -> `attribute` -> **AddCluster**.

3. **Configure the Filter:**

* Click on the **AddCluster** text to open its configuration box.

* In the `clusterer` field, click "Choose" and select **SimpleKMeans** again (ensure the settings match what you used in the Cluster tab).

* Click **OK**.

4. **Apply and Save:**

* Click **Apply**. You will see a new attribute appear at the end of your attribute list, typically named "cluster."

* Click **Save...** to export your new labeled dataset as an `.arff` or `.csv` file.

---

### Pro-Tips for Labeling

* **The "Ignored" Attributes:** If you want to cluster based on certain features but keep an ID or Name column in the final file, make sure to use the `ignoredAttributeIndices` property within the SimpleKMeans settings inside the **AddCluster** filter.

* **Result Verification:** Before saving, you can click the **Edit...** button in the Preprocess tab to view the data table and confirm that the cluster assignments look correct (e.g., Cluster 0, Cluster 1, etc.).

---

[perl][ML][Machine Learning]K-means in Perl

#!/usr/bin/perl -w

use strict;

use warnings;

use PDL::LiteF;

use PDL::Stats;

# 1. Define 4 separate arrays (Features)

my $f1 = pdl(1, 2, 1, 10, 11, 12); # Feature 1

my $f2 = pdl(1, 1, 2, 10, 12, 11); # Feature 2

my $f3 = pdl(0, 1, 0, 15, 14, 15); # Feature 3

my $f4 = pdl(2, 2, 1, 20, 21, 22); # Feature 4

# 2. Combine them into a 4-column matrix

# cat() joins them, transpose() makes each row a "person/object"

my $data = cat($f1, $f2, $f3, $f4)->transpose;

# 3. Run K-means for 2 groups

my $k = 2;

my %result = $data->kmeans({k => $k, NCLUS => 2});

# 4. Display which group each of the 6 items belongs to

print "Data Matrix (Rows = Items, Cols = Features):\n$data\n";

print "Cluster Assignments:\n", $result{cluster}, "\n";

print "Final Centroids (The average of each group):\n", $result{centroid}, "\n";

Install Snort 2.9 at Ubuntu 24.02 on 20260210

 Installation:

#apt-get update

#apt-cache policy snort

#apt-get install snort -y

Testing:

#snort -T -c /etc/snort/snort.conf

Execution:

#snort -c /etc/snort/snort.conf -A full -D -s

Install and deploy OSSEC at Ubuntu 24.04 on 20260210

Installation:

#sudo apt update

#sudo apt install -y build-essential make gcc wget tar \

libpcre2-dev zlib1g-dev libssl-dev libevent-dev \

libsystemd-dev libsqlite3-dev systemd-dev

#cd /usr/local/sbin

#wget -q -O - https://updates.atomicorp.com/installers/atomic | bash

#apt-get update

#apt-get install ossec-hids-server

[Docker] Docker Compose

 Step 0: Download the Container

docker pull testcontainers/sshd:1.3.0

Step 1: Save the File

Copy the code below and save it as a file named docker-compose.yml in a folder on your computer.

YAML

-----

services:

  sshd:

    image: testcontainers/sshd:1.3.0

    environment:

      - PASSWORD=your_secure_password

    ports:

      - "10022:22"

-----

Step 2: Run the Command

Open your terminal or command prompt, navigate to the folder where you saved the file, and run:

Bash

-----

docker compose up -d

-----

up: Tells Docker to create and start the containers defined in the file.

-d: Runs the container in "detached" mode (in the background) so it doesn't lock up your terminal window.

Step 3: Verify it's Running

You can check if the SSH server is active by running:

Bash

-----

docker compose ps

-----

This should show the sshd service running and mapped to port 10022.

How to actually "use" this specific container

Since this is an SSH server (testcontainers/sshd), its main purpose is to let you log in or create tunnels.

To log in to the container's shell:

Bash

-----

ssh root@localhost -p 10022

-----

(When prompted for a password, use your_secure_password as defined in the YAML file.)

To create a reverse tunnel (the "Testcontainers" way): If you have a service running on your actual computer at port 8080 and you want this Docker container to see it, run this from your computer:

Bash

-----

ssh -R 8080:localhost:8080 root@localhost -p 10022

-----

Now, if you were to enter the container (using the login command above), you could run curl http://localhost:8080 and it would reach the app on your physical machine.

How to Stop It

When you are finished, go back to the terminal in that folder and run:

Bash

-----

docker compose down

-----

This will stop the container and clean up the internal network it created.