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💡 About

Source: InvolveMedia

In this About section we will talk about the following 3 topics that seem to be important in Today's day and age ;) Also, it shed some light on why we make these Bookstack Instances in the first place.

  • Future of Documentation
  • Future of App Development
  • Future of Education

Yes—DeepWiki’s documentation should be treated as a starting point, not authoritative documentation. AI-generated explanations can be incomplete or incorrect, so a human should check important details against the actual code before relying on or publishing them.

That separation matters: reviewed docs make explanations more consistent and human-centered, while the live repository keeps them connected to the implementation. The result is less “chat with a pile of files” and more “learn from a curated guide, with the code available as evidence.”

Right. GitHub remains the source of truth for the repository; DeepWiki is a generated interface for exploring and understanding it, not the authority that defines what the code is.

Your broader idea adds a second layer: human-reviewed documentation could be the authoritative source for how the project should be taught, while GitHub remains authoritative for what the implementation contains. The tutor’s role is to connect those sources and explain them clearly—not to replace either one.

Throughout history we have 

For example, Microsoft’s own data shows its Copilot “answer engine” caused click-through rates for The New York Times’ domain to drop as much as 93% compared to traditional Bing search. An internal Microsoft presentation written by Microsoft’s director of Applied Science, Brent Hecht, in January 2024 describes the decline as a “doom loop” that would “hurt the performance of our models and the entire web at the same time.”

OpenAI President Greg Brockman described the models as “excellent at news.” Nadella agreed under oath earlier this year that conversing with chatbots “has substituted … giving you the information right there on the website on the AI platform versus needing to go to the underlying source.”

That kind of language speaks to how the technology could directly compete with, rather than transform, the original work. 

A Microsoft document states that there is a “real risk” that generative AI could “significantly disrupt the employment of the very people who generated the data on which the foundation model was trained.”

DeepWiki provides a variety of tools designed to enhance the understanding of code repositories. Here’s a summary of its key features:

Auto-Generated Documentation

  • Structured Overviews: Automatically generates documentation that outlines the repository's structure and functionality.
  • Purpose and Scope Summaries: Offers concise explanations of what each component does.

Visual Diagrams

  • Module Relationships: Creates diagrams that illustrate how different modules interact within the codebase.
  • Data Flow Paths: Visualizes the flow of data through the system, helping users understand dependencies and architecture.

AI-Powered Q&A Interface

  • Interactive Questions: Users can ask specific questions about the codebase and receive context-grounded responses.
  • Natural Language Processing: Allows for easy querying in plain language, making it accessible for all users.

Additional Features

  • Rapid File Scanning: Quickly scans repository files to provide immediate insights and summaries.
  • Deep Research Mode: Offers in-depth analysis for complex projects, ideal for thorough investigations.

These tools collectively aim to streamline the process of understanding and navigating open-source code, making it easier for developers to work efficiently.

Generative AI can be used to create examples s.t people not familiar with a topic can more easily understand it. Now, the job of the human using AI to create these examples is then to check if the generated examples actually explain the concept or not. Here, again a human with knowledge about the topic is essential. 

Over the past year something interesting has started happening online. AI tools are producing more documentation, guides, and explanations than ever before. At the same time, people seem to be reading documentation less than they used to. Instead of digging through help pages, knowledge bases, or developer guides, we increasingly ask AI tools for answers. The AI reads the documentation for us, summarizes it, and gives us the result.

At first glance this makes documentation feel less important. If nobody reads it anymore, why bother writing it? But when you look closer, the opposite seems to be happening. Documentation is quietly becoming one of the most important pieces of infrastructure behind modern software, websites, and digital systems. Humans may read it less directly, but the machines helping us build and use technology rely on it more than ever.

Documentation is essential to a global digital commons because shared infrastructure is only truly shareable when people can understand, use, maintain, and improve it. As the article suggests, documentation is increasingly read not only by people but also by AI systems that help them navigate software and build on it. Clear, open documentation—covering how systems work, how to connect them, and how to contribute—makes common infrastructure more accessible and dependable, rather than leaving knowledge locked inside a handful of companies or expert teams.

Writing that documentation collectively is part of building the commons itself. When communities document their tools, decisions, and practices together, they make it easier for others to adopt and adapt those tools, and to move away from proprietary platforms without losing essential knowledge along the way. Shared documentation can turn isolated open-source projects into an interoperable ecosystem: one that people can understand, trust, and shape together.

Goal 1: All information on this instance and any other does not provide any new information that is not already out on the internet. On top of that, LLM's and AI agents can provide you with great instructions to learn and build basically anything i.e they are great personal Tutors. However, these open source Tutors like Deeptutor.info work well when they are provided with curated learning material on a subject. Since, general LLM's do know everything but they cannot each you a subject like Quantum Mechanics., at least not yet ;)

However, it must be admitted that  soon figures out that if you have no background knowledge, don't know what to watch out for or search for that it gets quite hard for you to learn anything of substance. For instance the prompt: "Teach me quantum mechanics" simply provides you with a summary of what quantum mechanics entails it does not teach you the course obviously. This brings us with the first role of these instances and that is simply collecting collectively knowledge related to a global digital commons and represent this in a human teachable form. 

Goal 2: Finally, these instances are also created to create human communities and keep the so called human in the loop. Even if in the near term future AI can setup equivalent knowledge hubs on any field than we still need humans that can confirm that the information is correct and to add new information. We could of course completely trust AI and skip the human checks. The instances are community instances that are constantly updated with new input of how us humans understand the digital world. Hence, this is likely here to stay as a place that keeps track of the truth as far as we know it ;). New inputs constantly, need to be judged by humans to check if we understand what is going on under the hood for safety purposes. 

Goal 3: The objective for all instances is to create a collective truth on how the digital commons could work.

Warning 1: If you have one collective human truth wouldn't this be biased and won't you miss other ways of doing things ?

This Bookstack instance discusses from zero to hero how you create a HomeLab at home. Simply said how can you replace all the for profit corporate applications with open source alternatives and host them in your own home on your own hardware. In other words  your data is now stored on servers from BIG TECH e.g Facebook, Google, Apple, Amazon, Microsoft etcetera but with a HomeLab your data is stored in your own Home. 

Future Teachers

Documentation on any project has always been made to teach users about a certain topic, how to build something or 

We always say in the future, but actually today there are already

We have all experienced the rise of LLM's and AI in all aspects of our lives and it constantly growing. 

Open tutorAI

deeptutor.info

ANYTHING LLM