Well, the idea is to overcome the computer-age someday. I have the feeling that we are already moving in this kind of direction with different web paradigms being implemented currently:

The semantic web data would already be there: https://www.wikidata.org/wiki/Wikidata:Main_Page

Right, but the interface is only one part of the program (which has already been implemented in practice). Another part is the precision controller.

"

Basically with an app like ChatGPT, you have before you a black box that you can send commands to and that gives you unpredictable answers and consumes huge amounts of energy.

Instead, the semantc web browser with the precision controller starts with a complete white box, where you can control the ontology, have full control of the language, the outcome and consumes much less energy; but you can move on the ratio towards a ChatGPT-like black box with the precision controller.

With the default level, the ACE-syntax is enforced very strictly and semantic web data is expected to have the same syntax as defined. With a lower precision level, an LLM is stuck inbetween, syntax is not enforced as strictly and the ontology is enforced even if the data does not match it, also propagating to other web pradigms like MCP/AI-web and traditional web-services with REST-APIs.

The precision controller basically let’s the user move between a very strict semantic web browser and the lose cannon of a ChatGPT+MCP-app. And I think this moving of ratio is only possible if you start developing a strict semantic web browser, which has a precision controller integrated.

Another merit is that the energy consumption can be adjusted at will. If money/energy is low, for example in a state’s administration, the semantic web browser can still be used, while ChatGPT-like apps become unfeasible. "

It does use LLMs with the precision controller - which determines the degree to which ontologies are applied to not-semantic-web-data. This is done with LLMs as translation tools if the precision controller is adjusted with a low precision degree (ergo more fuzziness, ergo more LLMs).

But yeah, it’s basically meant as a contra-approach to ChatGPT-like apps, giving users back control. And because rule-based systems have always failed in the past, LLMs are used if the user wants to make the app more fuzzy and access more data this way.

I think multi-communities (which have already been improved for funding) will push Lemmy forward big time. https://github.com/LemmyNet/lemmy/issues/818