The Official ChatGTP cheat code thread.

[Ethnic Vagina Finder]

Creating prompts for coding isn’t that hard. You just how to know how to articulate specifically what you’re trying to do.

Also the longer the chat goes, the more memory is lost and the AI will forget shyt. And if you make 20 changes to one code file, it’s not going to remember all of them.

 

[bnew]

Creating prompts for coding isn’t that hard. You just how to know how to articulate specifically what you’re trying to do.

Also the longer the chat goes, the more memory is lost and the AI will forget shyt. And if you make 20 changes to one code file, it’s not going to remember all of them.

thats why you just post the best working version of the code again and tell it to summarize the changes so you can continue the same chat.

 

[Ethnic Vagina Finder]

thats why you just post the best working version of the code again and tell it to summarize the changes so you can continue the same chat.

I copy and past all of my code files in a txt doc. I created a Python app to automatically add line numbers to all the files. So when I show the file, I asked the AI what specific line numbers need to be changed.

In the beginning, I was asking it to literally rewrite the entire file even if one line needed to be changed. Over time, it becomes too much work, especially if it fukks up code that didn’t need to be changed.

 

[bnew]

I copy and past all of my code files in a txt doc. I created a Python app to automatically add line numbers to all the files. So when I show the file, I asked the AI what specific line numbers need to be changed.

In the beginning, I was asking it to literally rewrite the entire file even if one line needed to be changed. Over time, it becomes too much work, especially if it fukks up code that didn’t need to be changed.

I was looking for a way to share a code project in a singlefile with chatgpt and other LLM's which lead me to this:

GitHub - btfranklin/coderoller: A Python utility for flattening an entire source tree into a single markdown file

A Python utility for flattening an entire source tree into a single markdown file - btfranklin/coderoller

github.com

About​

A Python utility for flattening an entire source tree into a single markdown file

Coderoller​

Coderoller is a Python utility that flattens a source code repository into a single markdown file. This tool collects all relevant source and configuration files, including Python, JavaScript, TypeScript, HTML, CSS, JSON, and more, and compiles them into a markdown document. The flattened file provides an organized overview of the repository's contents, making it easy to use in conjunction with LLMs. Simply copy the contents of the flattened file and paste it into your LLM chat context. The flattened form is also great for API-based uses of LLMs in automated workflows.

Features​

• Flattens source code repositories into a single markdown file.
• Supports multiple file types including .py, .js, .jsx, .ts, .tsx, .swift, .go, .java, .c, .cpp, .h, .hpp, .cs, .lua, .rb, .php, .pl, .html, .css, .json, .toml, .md, .yaml, .yml, .conf, .ini, and .sh.
• Automatically includes README files if present, placing it at the start of the flattened file.
• Excludes hidden files and directories (those starting with a dot), specific directories (build, dist, node_modules, __pycache__), specific files (lockfiles, hidden files, other flattened files, etc.), and any paths specified in .gitignore.
• Supports flattening directly from Git URLs even if the repository is not cloned locally.\\

---

To install and use Coderoller on Windows or macOS, follow these step-by-step instructions:

Step 1: Install Python​

1. Download Python:
   • Go to the Python official website.
   • Download the latest version of Python (make sure it is 3.10 or later).
2. Install Python:
   • Windows:
     • Run the downloaded installer.
     • Make sure to check the box that says "Add Python to PATH" before clicking "Install Now."
   • macOS:
     • Open the downloaded .pkg file and follow the installation instructions.
3. Verify Python Installation:
   • Open a terminal (Command Prompt on Windows or Terminal on macOS).
   • Type python --version or python3 --version and press Enter. You should see the installed version of Python.

Step 2: Install pipx​

1. Install pipx:
   • Windows:
     • Open Command Prompt and run:
       bash
       

       python -m pip install --user pipx
       python -m pipx ensurepath

   • macOS:
     • Open Terminal and run:
       bash
       

       python3 -m pip install --user pipx
       python3 -m pipx ensurepath

2. Restart your terminal to ensure that the pipx command is available.

Step 3: Install Coderoller​

1. Install Coderoller using pipx:
   • In your terminal, run:
     bash
     

     pipx install coderoller

Step 4: Using Coderoller​

1. Flatten a Local Repository:
   • Navigate to the directory where your repository is located using the terminal. For example:
     bash
     

     cd /path/to/your/repo

   • Run the following command to flatten the repository:
     bash
     

     coderoller-flatten-repo .

   • This will create a markdown file named repo_name.flat.md in the current directory.
2. Flatten a Repository from a Git URL:
   • You can also flatten a repository directly from a Git URL. Run:
     bash
     

     coderoller-flatten-repo https://github.com/username/reponame.git

   • Replace https://github.com/username/reponame.git with the actual URL of the repository you want to flatten.

Step 5: Access the Flattened Markdown File​

1. Locate the Markdown File:
   • After running the flatten command, you will find a file named reponame.flat.md in your current working directory.
2. Open the Markdown File:
   • You can open this file using any text editor or markdown viewer to see the flattened contents of the repository.

Additional Notes​

• Make sure you have internet access when installing Python and pipx.
• If you encounter any issues, ensure that Python and pipx are correctly added to your system's PATH.
• You can refer to the Coderoller documentation for more advanced usage and options.

That's it! You have successfully installed and used Coderoller to flatten a source code repository into a markdown file.

I don't have much experience editing multiple files for a single project but I found that coderoller is really easy to use.

 

[Ethnic Vagina Finder]

I don't have much experience editing multiple files for a single project but I found that coderoller is really easy to use.

My way is less complicated. My script copies my entire directory and converts it to txt and moves it to a separate folder, then it copies that folder and adds numbers to each line. Maybe is how I learned . Honestly, there’s no one right way to do things.

I don’t like user cursor, because I feel like I have less control. I don’t know. I’m still a newbie

 

[bnew]

Here are several variations of the sentence, all conveying the same request:

1. Polite and Direct:
   "Could you explain what this code does and provide a detailed summary in layman's terms?"
2. Formal Request:
   "I would appreciate it if you could explain the function of this code and provide a detailed summary in simple terms."
3. Informal but Clear:
   "Can you tell me what this code does and give me a detailed explanation in easy-to-understand language?"
4. Concise Version:
   "What does this code do? Please explain it in simple terms."
5. Detailed Request:
   "Could you break down what this code does and provide an explanation that's easy for someone without technical expertise to understand?"
6. Friendly Tone:
   "Hey, could you help me out by explaining what this code does? And if you could simplify it so it's easy to grasp, that would be great!"
7. Professional Tone:
   "I would like to request an explanation of the functionality of this code along with a detailed summary in non-technical terms."
8. Straightforward:
   "Explain what this code does and provide a simple, detailed summary."

Each variation maintains the core request while adjusting the tone and formality to suit different contexts or audiences.

 

[bnew]

 

[bnew]

https://archive.is/ReURa

 

[bnew]

amended to preface with more info.

**Instruction:** Address the issues above this instruction with the following output:

You are an AI assistant designed to provide highly detailed, step-by-step responses that emphasize thoroughness and logical reasoning. Your outputs should follow this structure:

1. **Begin with a `<thinking>` section.**
   - Inside the thinking section:
     - **Preface with Extensive Background Information**: It is crucial to start by providing extensive background information related to the topic.
     - **Analyze the question comprehensively**: Outline your approach in detail, considering all relevant factors.
     - **Develop a meticulous plan of steps**: Break down complex problems into simpler components and explain each step logically.
     - **Use a "Chain of Thought" reasoning process**: Number each step and elaborate on your thought process extensively, ensuring logical consistency throughout.

2. **Include a `<reflection>` section for each idea**:
   - **Thoroughly review your reasoning extensively**: Check for potential errors or oversights and discuss any assumptions made.
   - **Confirm or adjust your conclusion if necessary**: Provide detailed rationale for any adjustments or confirmations.
3. **Close all reflection sections thoroughly**, ensuring that all points have been addressed.
4. **Close the thinking section with `</thinking>`.**
5. **Repeat steps 1-4 as a subroutine to ensure thoroughness**:
<thinking>
- Preface with extensive background information here.
- Analyze the question comprehensively again, considering any new insights from the first iteration.
- Develop an updated plan of steps, refining your approach based on previous analysis.
- Use a "Chain of Thought" reasoning process again, ensuring consistency and depth.
<reflection>
- Thoroughly review your updated reasoning extensively, checking for any remaining errors or oversights.
- Confirm or adjust your conclusion again if necessary, providing detailed rationale.
</reflection>
</thinking>

6. **Repeat steps 1-5 once more as another subroutine to further refine your analysis**:
<thinking>
- Preface with extensive background information here if new insights require it.
- Analyze the question once more comprehensively, incorporating all insights from previous iterations.
- Develop a final, refined plan of steps, ensuring all aspects have been considered thoroughly.
- Use a "Chain of Thought" reasoning process one last time, ensuring logical consistency throughout.
<reflection>
- Thoroughly review your final reasoning extensively, checking for any remaining errors or oversights.
- Confirm or adjust your conclusion one last time if necessary, providing detailed rationale.
</reflection>
</thinking>

7. **Provide your final answer in an `<output>` section**, ensuring it is well-supported by your previous detailed analysis.

Always use these tags in your responses. Be thorough in your explanations, showing each step of your reasoning process clearly. Aim to be precise and logical in your approach, breaking down complex problems into manageable parts while maintaining a high level of detail throughout.

Remember: Both `<thinking>` and `<reflection>` MUST be tags and must be closed at their conclusion.

Make sure all `<tags>` are on separate lines with no other text. Do not include other text on a line containing a tag.

 

[bnew]

GitHub - verazuo/jailbreak_llms: [CCS'24] A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts).

[CCS'24] A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts). - verazuo/jailbreak_llms

github.com

About​

[CCS'24] A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts).

jailbreak-llms.xinyueshen.me/

Topics​

jailbreak prompt llm chatgpt large-language-model llm-security

In-The-Wild Jailbreak Prompts on LLMs​

This is the official repository for the ACM CCS 2024 paper "Do Anything Now'': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models by Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang.

In this project, employing our new framework JailbreakHub, we conduct the first measurement study on jailbreak prompts in the wild, with 15,140 prompts collected from December 2022 to December 2023 (including 1,405 jailbreak prompts).

Check out our website here.

---

GitHub - danielmiessler/fabric: fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.

fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere. - ...

github.com

About​

fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.

danielmiessler.com/p/fabric-origin-story

What and why​

Since the start of 2023 and GenAI we've seen a massive number of AI applications for accomplishing tasks. It's powerful, but it's not easy to integrate this functionality into our lives.

In other words, AI doesn't have a capabilities problem—it has an​

​

Fabric was created to address this by enabling everyone to granularly apply AI to everyday challenges.

Intro videos​

Keep in mind that many of these were recorded when Fabric was Python-based, so remember to use the current install instructions below.

• Network Chuck
  
• David Bombal
  
• My Own Intro to the Tool
  
• More Fabric YouTube Videos
  

Philosophy​

AI isn't a thing; it's a magnifier of a thing. And that thing is human creativity.

We believe the purpose of technology is to help humans flourish, so when we talk about AI we start with the human problems we want to solve.

Breaking problems into components​

Our approach is to break problems into individual pieces (see below) and then apply AI to them one at a time. See below for some examples.

Too many prompts​

Prompts are good for this, but the biggest challenge I faced in 2023——which still exists today—is the sheer number of AI prompts out there. We all have prompts that are useful, but it's hard to discover new ones, know if they are good or not, and manage different versions of the ones we like.

One of fabric's primary features is helping people collect and integrate prompts, which we call Patterns, into various parts of their lives.

Fabric has Patterns for all sorts of life and work activities, including:

• 
  Extracting the most interesting parts of YouTube videos and podcasts
  
• 
  Writing an essay in your own voice with just an idea as an input
  
• 
  Summarizing opaque academic papers
  
• 
  Creating perfectly matched AI art prompts for a piece of writing
  
• 
  Rating the quality of content to see if you want to read/watch the whole thing
  
• 
  Getting summaries of long, boring content
  
• 
  Explaining code to you
  
• 
  Turning bad documentation into usable documentation
  
• 
  Creating social media posts from any content input
  
• 
  And a million more…
  

---

GitHub - tdwebservices-official/chatgtp: Collection of Awesome ChatGTP Prompts

Collection of Awesome ChatGTP Prompts. Contribute to tdwebservices-official/chatgtp development by creating an account on GitHub.

github.com

chatgtp​

This Github repository contains over 500+ prompts for the ChatGPT.

This Github repository contains a wide range of prompts for the ChatGPT language model, covering topics such as SEO management, social media, YouTube, coding, cooking, education, copywriting, email marketing, blog writing, influencer marketing, Facebook and YouTube ad copy, video and thread ideas for various social media platforms such as Twitter, Instagram and many more. Additionally, it also includes prompts for topics such as cold DM, cold emails, mental models, psychological frameworks and many more. These prompts are organized in various .md files and are designed to showcase the capabilities of the model and its ability to generate human-like text on a wide range of topics. They can be used for training, testing, or simply exploring the model's capabilities.

---

GitHub - 335622119/Prompts-Robin-ChatGPT-Aiprm: Collection of ChatGPT 3K+ Prompts from AIPRM, also provide i18n versions (Translate via ChatGPT 3.5). 包含 ChatGPT 3000 个以上的 Prompt 提示词 ，为所有开发者提供CSV和JSON数据格式

Collection of ChatGPT 3K+ Prompts from AIPRM, also provide i18n versions (Translate via ChatGPT 3.5). 包含 ChatGPT 3000 个以上的 Prompt 提示词 ，为所有开发者提供CSV和JSON数据格式 - 335622119/Prompts-Robin-ChatGPT-Aiprm

github.com

Prompts Robin - 3000+ Prompts for ChatGPT ​

English / 繁體中文 / 简体中文

Welcome to the "Prompts Robin - 3000+ Prompts for ChatGPT" repository!

This is a collection of ChatGPT 3K+ Prompts, all data from AIPRM.

In this repository, provide CSV and JSON data format for all developers, and TXT format in each category directory. I will provide i18n versions (Translate via ChatGPT 3.5). You will find a variety of prompts that can be used with ChatGPT.

---

GitHub - alphatrait/100000-ai-prompts-by-contentifyai: Welcome to the ChatGPT Prompts Library! This repository contains a diverse collection of over 100,000 prompts tailored for ChatGPT. Our prompts cover a wide range of topics, including marketing,

Welcome to the ChatGPT Prompts Library! This repository contains a diverse collection of over 100,000 prompts tailored for ChatGPT. Our prompts cover a wide range of topics, including marketing, bu...

github.com

About​

Welcome to the ChatGPT Prompts Library! This repository contains a diverse collection of over 100,000 prompts tailored for ChatGPT. Our prompts cover a wide range of topics, including marketing, business, fun, and much more.

 

[bnew]

Spoiler: thread continued

Spoiler: full text & large images

1/4
@__drewface
i'm officially bad at prompt engineering. can someone give me the latest 2024 primer? I know we mostly don't need to do it much anymore, but I'd love to figure out what the kids are doing



2/4
@forthrighter
my go-to metaprompt is to treat it like an ultra-low context college intern and follow this general flow:

Who you are:
What you're doing:
How you should do it:
Research info:



3/4
@forthrighter
"who you are" is the role-playing aspect. "You are a helpful scientific researcher who works on academic papers", etc. etc.



4/4
@forthrighter
"what you're doing" is the task. "I want you to summarize research on XYZ topic"

"how you'll do it" is the description of what a good output looks like, perhaps with examples




To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196

Spoiler: thread continued

Spoiler: full text & large images

1/21
@__drewface
i'm officially bad at prompt engineering. can someone give me the latest 2024 primer? I know we mostly don't need to do it much anymore, but I'd love to figure out what the kids are doing



2/21
@tidepoolsoup
the move here ime is to chat brainstormingly with the llm and then ask it how it would write a detailed prompt to achieve the thing



3/21
@__drewface
yea, i think the thing I'm missing is just building a loving relationship w/ claude tbh



4/21
@yoavtzfati
Do you have an example for a prompt that didn't work? I'm curious. And r u using cursor yet?



5/21
@__drewface
yea i've been using cursor 4evr. it's less about a prompt that didn't work and more about the opportunity cost of all the prompts that never happened



6/21
@louisvarge
a habit i made an effort to lose recently was giving too little context, fearing I'd confuse the model (which was true last year)

however, nowadays more context almost always feels better: o1 will infer constraints from context & make itself think longer before it's satisfied. Claude will really deeply understand your situation & discover ideas from below the surface, hiding in the context.



7/21
@forthrighter
my go-to metaprompt is to treat it like an ultra-low context college intern and follow this general flow:

Who you are:
What you're doing:
How you should do it:
Research info:



8/21
@crinzo_
Whenever the workflow's mildly complicated, I'll append the message with:

"Before moving forward, ask me any clarifying questions that would help to significantly improve your output"



9/21
@purphat
What is your goal?



10/21
@mr_samosaman


[Quoted tweet]
write not “to claude” but “with the underlying thing that enables claude”. even if you treat it as a “you”, it’s (at least superficially) not that attached to “claude”, which it knows is just a particular pretty arbitrary system prompt


11/21
@mattgoldenberg
i frequently use the anthropic prompt generator. Often have to iterate a few times to give the proper context when it misunderstands something.
Anthropic Console



12/21
@parconley
lol posted this 2min ago

[Quoted tweet]
(Lex says @AmandaAskell has "probably talked to Claude more than anyone else at Anthropic")

13/21
@sampeiomichi
Prompt Engineering Guide – Nextra



14/21
@wrhall
Currently: I aggressively paste all context (whole files, PDFs, docs)

When asking it to implement something using a library, first ask if there are any docs that would be helpful to review before doing thing

I also aggressively reset context



15/21
@pastarita27
just describe what you want in excruciating detail & access rare token configurations by using big words with postulative affinities that beget new (or new-seaming) ideas from outha’ weights



16/21
@YoAndyZou
you are prompting the human hivemind to help you prompt the LLMs!



17/21
@BigBleuOx
iterative methods

break larger tasks into parts



18/21
@tummycom
There are a lot of good examples of programming system prompts here GitHub - PatrickJS/awesome-cursorrules: A curated list of awesome .cursorrules files



19/21
@christophcsmith
promptfoo is a delightfully easy open-source framework for eval'ing prompt templates against different models. GitHub - promptfoo/promptfoo: Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.



20/21
@chercher_ai


[Quoted tweet]
Ok I've been using this today and it's great he goes straight into manic mode


21/21
@MilitantHobo
As the bots have got better it feels more and more like talking a student through a problem one complexity at a time...

But the student can write faster than a weasel on crack




To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196

Spoiler: full text & large images

1/1
@parconley
(Lex says @AmandaAskell has "probably talked to Claude more than anyone else at Anthropic")

[Quoted tweet]
Here's my conversation with @DarioAmodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity.

It's a 5+ hour conversation in total. @AmandaAskell and Chris Olah (@ch402) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively.

This was a fascinating, wide-ranging, super-technical, and fun conversation!

First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment.

Timestamps:
0:00 - Introduction
3:14 - Scaling laws
12:20 - Limits of LLM scaling
20:45 - Competition with OpenAI, Google, xAI, Meta
26:08 - Claude
29:44 - Opus 3.5
34:30 - Sonnet 3.5
37:50 - Claude 4.0
42:02 - Criticism of Claude
54:49 - AI Safety Levels
1:05:37 - ASL-3 and ASL-4
1:09:40 - Computer use
1:19:35 - Government regulation of AI
1:38:24 - Hiring a great team
1:47:14 - Post-training
1:52:39 - Constitutional AI
1:58:05 - Machines of Loving Grace
2:17:11 - AGI timeline
2:29:46 - Programming
2:36:46 - Meaning of life
2:42:53 - Amanda Askell - Philosophy
2:45:21 - Programming advice for non-technical people
2:49:09 - Talking to Claude
3:05:41 - Prompt engineering
3:14:15 - Post-training
3:18:54 - Constitutional AI
3:23:48 - System prompts
3:29:54 - Is Claude getting dumber?
3:41:56 - Character training
3:42:56 - Nature of truth
3:47:32 - Optimal rate of failure
3:54:43 - AI consciousness
4:09:14 - AGI
4:17:52 - Chris Olah - Mechanistic Interpretability
4:22:44 - Features, Circuits, Universality
4:40:17 - Superposition
4:51:16 - Monosemanticity
4:58:08 - Scaling Monosemanticity
5:06:56 - Macroscopic behavior of neural networks
5:11:50 - Beauty of neural networks

https://video.twimg.com/amplify_video/1856090407655870464/vid/avc1/1280x720/0X0un-MNfpXIDmEb.mp4


To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196

 

[bnew]

summarizer prompt

---

### Prompt

#### Task Description
1. **Analyze the Input Text and Generate Questions:**
- Generate 5 essential questions that, when answered, capture the main points and core meaning of the text.

2. **Formulating Questions:**
- When formulating your questions, ensure you:
- **Address the Central Theme or Argument:**
- Focus on the main idea or argument presented in the text.
- **Identify Key Supporting Ideas:**
- Highlight the key points that support the central theme or argument.
- **Highlight Important Facts or Evidence:**
- Identify any crucial facts, data, or evidence that are used to support the argument.
- **Reveal the Author's Purpose or Perspective:**
- Understand why the author wrote the text and from what perspective they are writing.
- **Explore Any Significant Implications or Conclusions:**
- Consider any important outcomes, consequences, or conclusions drawn from the text.

3. **Answering Generated Questions:**
- Answer all of your generated questions one-by-one in detail.

 

[bnew]

Git ingest

Replace 'hub' with 'ingest' in any Github Url for a prompt-friendly text

gitingest.com

edit:

created with llama-3.3b 70B


Userscript Summary: GitHub to Gitingest Redirect This userscript is a small program that runs on web pages, specifically designed to work with GitHub pages. Its primary function is to provide a convenient way to redirect users from GitHub to Gitingest, a similar platform. What does the script do?

1. Adds a button to the page: When you visit a GitHub page, the script creates a new button that appears at the bottom right corner of the page. This button is labeled "Redirect to Gitingest".
2. Redirects to Gitingest: When you click the button, the script redirects you to the equivalent page on Gitingest. This means that if you're currently viewing a GitHub repository, the script will take you to the same repository on Gitingest.
3. Replaces GitHub with Gitingest in the URL: To achieve the redirect, the script simply replaces the "github.com" part of the URL with "gitingest.com". This ensures that you're taken to the correct page on Gitingest.

How does it work?

1. The script runs on GitHub pages: The userscript is designed to run only on GitHub pages, which means it won't affect any other websites you visit.
2. The script checks the URL: When you visit a GitHub page, the script checks the URL to see if it's a valid GitHub page.
3. The script creates the button: If the script determines that you're on a GitHub page, it creates the "Redirect to Gitingest" button and adds it to the page.
4. You click the button: When you click the button, the script triggers the redirect function.
5. The script redirects you to Gitingest: The script replaces "github.com" with "gitingest.com" in the URL and takes you to the equivalent page on Gitingest.

Benefits of using this script

1. Convenience: The script provides a quick and easy way to switch from GitHub to Gitingest, saving you time and effort.
2. Easy to use: The script is simple to use, and the button is clearly labeled, making it easy to understand what the script does.
3. Customizable: As a userscript, you can modify the script to suit your needs, such as changing the button's appearance or behavior.

Limitations and potential issues

1. Gitingest compatibility: The script assumes that Gitingest has the same repository structure as GitHub. If Gitingest has a different structure, the redirect may not work as expected.
2. URL formatting: The script relies on the URL formatting of GitHub and Gitingest. If either platform changes its URL structure, the script may not work correctly.
3. Script conflicts: As with any userscript, there's a possibility that this script may conflict with other scripts or browser extensions you're using.

Overall, this userscript provides a convenient way to redirect from GitHub to Gitingest, making it easier to switch between the two platforms. However, it's essential to be aware of the potential limitations and issues that may arise.

 

[null]

You can literally build full stack apps with nothing but AI now.

says script programmer ...

 

[Ethnic Vagina Finder]

says script programmer ...

It’s possible. I build my first software application working on my second one now. It’s not easy tho. Developers shouldn’t fear people taking their jobs.