Readme formatting

README.md

@@ -1,14 +1,14 @@
 What are the benefits we seek by networking?
 ---
 
-Another node can provide accountability -- are we accessible to the outside world? Do our results agree with others?
-Schedule a task to be performed when we may not be available -- nodes can coordinate and attempt to ensure the task is performed!
-Store backup copies of our data -- if we consider some data valuable, we want to protect its continuity! -- integrity, availability
-We can benefit from innovations that others may share with us! -- algorithms, data, commentary
-We have a potential audience for our contributions -- we may be heard and can receive feedback
-Networking can help us handle more demand for our services
-than we can provide with a single node, or even with all the nodes we can muster!
-Or similarly, to perform more computations in a given amount of time!
+- Another node can provide accountability -- are we accessible to the outside world? Do our results agree with others?
+- Schedule a task to be performed when we may not be available -- nodes can coordinate and attempt to ensure the task is performed!
+- Store backup copies of our data -- if we consider some data valuable, we want to protect its continuity! -- integrity, availability
+- We can benefit from innovations that others may share with us! -- algorithms, data, commentary
+- We have a potential audience for our contributions -- we may be heard and can receive feedback
+- Networking can help us handle more demand for our services
+  than we can provide with a single node, or even with all the nodes we can muster!
+- Or similarly, to perform more computations in a given amount of time!
 
 How can we structure the application?
 ---
@@ -29,25 +29,25 @@ Application will need its own core operational data store. This can be achieved
 Nodes should be able to engage in clustering configurations, perhaps especially those in close physical/virtual proximity.
 
 So let's take an example node with some local storage, RAM, CPU, and network.
+
 Let us take as the foundation of our data model, the "rhizomatic database" structure, which can be summarized as follows:
 
-We establish a key space which we refer to as "domain entities".
-Each domain entity is identified by a unique key, e.g. a UUID;
-A node may curate one or more collection of "deltas".
-Each delta contains a set of "pointers".
-To obtain views of the domain entities, "lossless" or "lossy" reducers may be applied to a (filtered) set of deltas.
-Each pointer contains a "local context", "target", and "target context".
-Each domain entity is modeled as having a set of "properties", where each property is a key-value pair.
-Pointers are interpreted as assertions about the values of domain entiity properties.
-A lossless view shows all the values asserted by each relevant delta in the (filtered) set.
-A lossy view uses a given reducer to resolve each requested domain entity property to a value.
-
-The first domain entities we need to model are our network peers -- other possible rhizome nodes.
-There is a balance to manage. We must not make unwarranted assumptions about out network peers.
-However, we are seeking a situation where we benefit by mutual trust among at least some network peers.
-So we will want to keep track of information we gather that may affect our evaluation of the trustworthiness of a given peer.
-That also raises the question of how we want to think about identifying peers.
-Of course, asymmetric key cryptography: we identify a peer by using their public key to check their signatures.
+- We establish a key space which we refer to as "domain entities".
+- Each domain entity is identified by a unique key, e.g. a UUID;
+- A node may curate one or more collection of "deltas".
+- Each delta contains a set of "pointers".
+- To obtain views of the domain entities, "lossless" or "lossy" reducers may be applied to a (filtered) set of deltas.
+- Each pointer contains a "local context", "target", and "target context".
+- Each domain entity is modeled as having a set of "properties", where each property is a key-value pair.
+- Pointers are interpreted as assertions about the values of domain entiity properties.
+- A lossless view shows all the values asserted by each relevant delta in the (filtered) set.
+- A lossy view uses a given reducer to resolve each requested domain entity property to a value.
+- The first domain entities we need to model are our network peers -- other possible rhizome nodes.
+- There is a balance to manage. We must not make unwarranted assumptions about out network peers.
+- However, we are seeking a situation where we benefit by mutual trust among at least some network peers.
+- So we will want to keep track of information we gather that may affect our evaluation of the trustworthiness of a given peer.
+- That also raises the question of how we want to think about identifying peers.
+- Of course, asymmetric key cryptography: we identify a peer by using their public key to check their signatures.
 
 Applications will interface with one or more nodes, and use the information obtained to make decisions;
 including the option of further participation in the network; how clear is the distinction between node and application?