Skip to main content

01. Future of Documentation

Disclaimer: There is no doubt about it that LLM's have been leveraged to write down the Deternet project structure even though it ground work was already created before LLM's were available to the general public. As with any sector LLM's have deeply changed the documentation landscape in many different ways. Today and even more in the future documentation in human readable form for any project will be written by AI. The job of the human expert is then to check if what the AI has written is actually correct. 

Hence, you will not get rid of human technical writers but the job changes from being a writer to being a controller if everything is correct and adjust if something is wrong. You could compare it to a pilot that always flies on automatic pilot but needs to constantly check if everything goes according to plan and adjust if something goes wrong.

To better illustrate this we can look at how LLM's can now write documentation for code that has been created by another AI with a human in the loop. The workflow goes like this in the sense of spec driven development with documentation included.

Diagrams, Graphs, Work Flows, Etcetera

Instead of you manually taking data and making diagrams, graphs, work flows, charts and other forms of representing data you can let your 

Analysis of the data

  1. All the data created can be used together to look for correlations and you can easily do comparisons with different years, percentages, and take extra data from the internet as well to compare with multiple countries for instance or how would this data likely change if we increased variable x or included variable y.

Documentation for Network Engineers

Yes, all documentation is checked by humans before it is published on any Deternet society or hub. But people in the field will more and more use AI agents that can 

Monitoring Systems

People in the field will have the skills to lay the cables, and to splice the fiber, connect it to the right ports, but more and more can be standardized like OPNSense routers, switches, wap's and more. All this knowledge needs to be collected in a central place and updated constantly. 

How is information retrieved

Most often engineers will not move to any of the documentation pages if the documentation is already included in a large LLM or small models can be created completely focused on laws and regulations inside a country or region. Basically, the AI is reading the docs and people have conversations with documentation instead of manually searching through it. Even though, myself I catch myself manually search most of the time anyways.

 

Teachers

The future of teaching is to private AI tutors. It is clear by now that people can learn much more from a private AI tutor, tailored for them instead of in a general class room.

  1. Personally Tailored programs. A student coming from mathematics can already skip certain courses e.g calculus and linear algebra if it wants to switch to a physics degree for instance. On top of that a student will only move on to the next subject if it understand the prerequisites well enough to move on.
  2. Standardized examination: 
  3. Instant Feedback: When an AI

Prompt: If you leave out social skills, enthusiasm, accountability, and research out of the equation what is still the benefit of getting lectures at a university from a professor compared to a private tailored LLM tutor. Also this LLM tutor created a program for you that makes sure it teaches you first the prerequisites before moving on to another subject. For instance first calculus and then thermodynamics in a physics program for instance. Also standardized testing is included by taking exams from top universities and combine them together for training data on a specific subject. 

Private LLM Tutors as the Superior Alternative

When credentialing and social accountability are set aside, private LLM tutors represent a fundamentally more efficient learning system.

  1. They eliminate the artificial constraints of university scheduling, cohort-based pacing, and one-size-fits-all curriculum design.
  2. A student can master prerequisites completely before advancing, receive personalized explanations thousands of times without impatience, and follow a curriculum optimized purely for their understanding rather than administrative convenience.
  3. The ability to compress years of university coursework into months of focused, frictionless learning—with standardized exam validation—suggests that traditional universities may soon become primarily credentialing institutions rather than knowledge-delivery systems.
  4. For learners motivated enough to work without external accountability structures, private LLM tutoring is simply the more rational choice.

Your setup actually has serious advantages: perfect prerequisite sequencing, unlimited patience, personalized pacing, and instant feedback. Universities don't do this well—students often hit calculus or thermodynamics before they're ready, with no mechanism to loop back and shore up gaps without repeating entire courses.