This page begins with the following statements:

Using C++ for opencog is possible, but not generally recommended. This is 
because the whole point of OpenCog is to represent knowledge in terms of 
hypergraphs in the atomspace, and to manipulate and grow that knowledge 
using deduction, PLN, learning and various evolutionary algorithms. Of 
course, writing new evolutionary algorithms can mean writing in C++; and 
certainly fixing existing low-level, core code requires working with C++. 
However, *newcomers to OpenCog are strongly encouraged to think in terms of 
hypergraphs*, and representing their ideas in terms of hypergraphs, and in 
terms of the first-order logic that the PLN and pattern-matching engines 
can process.


Although most new code should really be "new hypergraphs", and new patterns 
(such as the SatisfactionLink and the BindLink), sometimes its just plain 
hard to avoid writing new algorithms. For this, scheme is recommended. In 
other cases, one might need to implement a new control process, aka a new 
"MindAgent" that controls inferencing, or possibly interfaces to external 
systems and sensors. For this, using python is recommended.


In the following tutorials, .....


My analysis:

Do the authors actually mean that most new code should really be those that 
"creates" a new knowledge-representation base using a hypergraph, or do 
they really mean "code that is a new hypergraph"? My perspective of a 
hypergraph is the equivalent of a database. It makes sense to write new 
code that adds content to that database (or hypergraph). But new code being 
the hypergraph (or database) itself... now that's something that could use 
clarity.


Also the text above states to use 'Scheme' to create hypergraph(s) and use 
'Python' to write a mind-agent. How does this compare with an earlier post 
suggesting (and recommending) using the 'Scheme' shell to do most 
interactions with opencog. A scenario I can imagine where this applies is 
when opencog is used as a library inside python. Is that the case?

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