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Markdown Buttons

A collection of SVG buttons for displaying custom "buttons" in Markdown content. You are free to use these buttons wherever you like. All buttons were created in Figma, with (most) icons provided by the Iconify plugin.

The SVG files can be found on this gist. They have been separated in order to reduce the amount of lag when loading this README file.

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#include <X11/Xlib.h>

int main() {
    XEvent event;
    Display* display = XOpenDisplay(NULL);
    Window w = XCreateSimpleWindow(display, DefaultRootWindow(display), 50, 50, 250, 250, 1, BlackPixel(display, 0), WhitePixel(display, 0));
    XMapWindow(display, w);
    XSelectInput(display, w, ExposureMask);

    for (;;) {

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#Configuration Proxy

    #Git 
    git config --global http.proxy http://<username>:<password>@<proxy-port>
    git config --global https.proxy http://<username>:<password>@<proxy-port>
    

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import gymnasium as gym
import numpy as np
from gymnasium.envs.mujoco.mujoco_env import MujocoEnv

# Env initialization
env = gym.make("Swimmer-v4", render_mode="human")
# Wrap to have reward statistics
env = gym.wrappers.RecordEpisodeStatistics(env)
mujoco_env = env.unwrapped
n_joints = 2

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git clone https://github.com/cpacker/MemGPT.git
conda create -n memgpt python=3.10
conda activate memgpt
cd memgpt
pip install -r requirements.txt
export OPENAI_API_KEY="YOUR_API_KEY"
# download .txt docs from: https://huggingface.co/datasets/MemGPT/example-sec-filings/tree/main
# place in "memgpt/personas/examples/preload_archival/*.txt"
python3 main.py --archival_storage_files_compute_embeddings="memgpt/personas/examples/preload_archival/*.txt" --persona=memgpt_doc --human=basic

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# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin
wget "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/${MODEL}"

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GitHub Search Syntax for Finding API Keys/Secrets/Tokens

As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.

Search Syntax:

(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))

Examples:

**1.

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delhist() {
    local num_lines="$1"
    local file="$HOME/.zsh_history"

    if [[ ! "$num_lines" =~ ^[0-9]+$ ]]; then
        echo "Invalid number of lines: $num_lines"
        return 1
    fi

    local total_lines=$(wc -l < "$file")

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brew update && brew upgrade && brew cleanup

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#pip install git+https://github.com/huggingface/transformers.git

import datetime
import sys
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_microphone_live

pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=0)
sampling_rate = pipe.feature_extractor.sampling_rate