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You cannot manage effectively what you do not understand effectively.

Welcome to Managen.ai,

Our Mission is to help people to effectively understand, build, use and manage Gen()AI.

Our Method uses Generative AI itself helping to build the site to keep it useful and relevent.

Our success will depend on you helping to guide it to be as self-accurate as possible.

Working as a relevant information hub, Managing Generative AI will provide an expansive seed that will allow for us to keep up with the rapidly evolving technologies and techniques.

How to use this site

Right now, you can learn from the present understandings where you can learn the deep and wide components of both building Generative AI, and building with Gen()AI. Eventually, you'll be able to work with ManagenAI to both create and help you to create the GenAI-solution that works best for your needs.

How will it improve?

Contribute to this project.

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We need your help.

It presently requires many brilliant scientists and engineers to understand the way Gen()AI works and how to use. We want to make it so MORE can do this.

Ideally, this will provide the core kernel of insight, a 'GenAI Oracle' if you will, that will both explain and enable the safe, ethical, and effective use of Gen()AI.

As the complexity of our engineering and science expands, the degree of understanding will become increasingly deep not unlikely to the point fewer may be able to solidly understand the whole picture. Coupled with the reality that some information may need to rapidly, this site will need to be agile, which will be best enabled with automation and Generative AI assistance.

This is, a self building site by the way.

Self building

GenAI to help explain GenAI, AI to help create GenAI.

As this is intended to be Gen()AI self-explaining knowledgebase, we aim to start with an version that enables the implementation's of Gen()AI, via internal explanation, referencing good tutorials and blogs, and important papers and repositories.

There will be several componentes to it, which we recommend you reading the build plan to better understand how this will be possible

Audience

Initially, the intendend audience of this will be initially for more technically focused folks. It will then evolve into broader audiences, to share information that is more generally useful for their particular needs to use Gen()AI. This may eventually include direction to public commercial solutions that might minimize the technichal requirements.

Knowledge Scope

Knowledge rests utop the mountains atop the mountains. We will not initially be focusing on base-level components of using and understanding Generative AI. This includes basics of 1. programming, 2. matrix mathemtatics, 3. Calculus-mathematics. We will aim to build these in over time, or provide solid references to them, the site will assume a general degree of understanding requisite to complete the general tasks at hand.

Our Strategy

Our strategy can be separated project strategy and community strategy

Project strategy

  1. Establish a knowledge core of information that is heirarchichally related in documentaiton. Expand to Graph-representations, and Knowledge graphs to enable greater flexibility.
  2. Build with Github Mkdocs - Material. This will make something that has minimal overhead to provide the needed understanding. It will allow for content generation to be focused while minimizing added complexity of rendering with more dynamic and aesthetic websites and GUIS. Longer-term, with the help of AI, we anticipate being able to re-represent the information contained in this repository in other formats.
  3. Use Github Workflows to minimize unecessarily manual processes. While we will enable people to assist in the creation of this site, in a controllably but dynamic fashion

Community strategy

  1. Make this interesting and useful Make this as useful as possible to help people both build and build with Gen()AI.
  2. Publish, publish, publish Aim to create content that can be shared, using the information herein as a background.

Potential challenges

  1. Input method. Markdown can be clunky, especially for images.
  2. Platform method. Github is more technichally oriented, and may make it challenging for potential contributors to help with the design.
  3. Costs. The cost of continuously calling LLMs, even open-sourced and smaller models, will incur both financial and carbon costs. It will be necessary to maintain appropriate balance of value to expenditures.
  4. Scaleability. While the present design may be reasonable for smaller community-enabled projects, it is possible that it will become increasingly difficult to scale the solution to handle the wide-variety of information, especially as the degree of components begins to build, potentially requiring super-linear effort. Hence it will be necessary to stay focused, incorporating both historical and modern information that is of highest relevancy to the effective creation and use of Generative AI.