I remember the conversation about what the maximum practical size of an object is. Since an object is designed for reuse and enhancement, the more it is enhanced, the more specific it becomes to what it is accomplishing. The composition of an object is also limited by available technology.

A GRAYBELT Cog can be visualized as a puzzle piece that, when combined with other puzzle pieces, creates an entire software platform. Now imagine that every puzzle piece was a square—the resulting shape could be anything.

To a Software Architect, a cog is a piece of functionality. It has an interface that connects with other cogs in the system. As far as the architect is concerned, it will always interface with the system the same way. The architect does not have to consider how the cog works internally, only how it connects to the system.

A cog may be made of software libraries that are combined to create a particular functionality. A cog may be delivered as a Docker container running a large language model along with its required software. This type of cog solves the biggest problem for companies that want to run local AI. The reality of running a local large language model is that there must be expertise to manage the different properties and environments for the LLM. Further, expertise is required every time a new release of that LLM is made.

GRAYBELT has the expertise to provide what we call “Thinking Cogs,” but the value in this type of cog may not be obvious. GRAYBELT maintains the thinking cog. Using a large language model as a component in a software platform requires a different kind of thinking regarding updates. The open-source large language models used are created by other companies. The environment for running these models will differ between models or even new versions of the same model. Managing this would mean total dedication to tracking variables created by another company. Just like our original invention of the software library, it would not make sense for every company using a model to go through the pain of updating it. That would be a duplication of work and not a worthwhile endeavor for any but the largest companies.

Another advantage to having one company manage these thinking cogs is avoiding the constant cycle where a model once considered king is replaced by a new king the following week. GRAYBELT may determine that a totally different model will provide better functionality, but that will be completely transparent to the company using the cog. It will fit into their current platform just like a puzzle piece.

GRAYBELT serves as a steward to other companies and has been compared to the model used by ARM and other chipmakers. Though this is not an accurate comparison—because ARM provides a blueprint to build a chip and its users use the blueprint to create a physical chip that meets their own specifications—GRAYBELT delivers the actual chip and promises the pins will never change.

To learn more about GRAYBELT Innovations, visit GRAYBELT.AI.