docs: add heap attack

docs/docinpy/index.md

@@ -13,7 +13,7 @@ codebase. It is also important for AI-based agents to understand the codebase an
 ## EvoNote
 
 EvoNote is using DocInPy to document its codebase. You can have a good visualization of EvoNote's codebase by
-running [EvoNote visualization](/html/project_tree.html).
+running [EvoNote visualization](https://evonote.org/html/project_tree.html).
 
 ## How to use
 

docs/writings/4.1 Interface of continuous and discrete.md

@@ -1,5 +1,34 @@
 # Interface of continuous and discrete
 
+## Heap attack
+
+### Paradox of the heap
+
+::: tip Story
+If you remove one grain of sand from a heap of sand, it is still a heap. If you keep removing grains of sand, eventually you will have only one grain of sand left. Is it still a heap?
+:::
+
+The key point of this paradox lies in the continuous nature of the concept - `heap`. The concept `heap`, is actually never well-defined when we use it as natural language. It also does not have a formal definition in any way. You knowledge about the heap might vary infinitesimally. For a same object, you might think that is a heap today and not a heap tomorrow.  
+
+### Attack to any continuous concept
+
+For any continuous concept that does not have a clear definition. You can always attack it by the following way:
+
+::: tip Protocol
+- Find an object that is an instance of the concept. 
+- Find way to vary the object, so it is still an instance of the concept.
+- Show that along the way, the object will eventually not be an instance of the concept.
+- You know that along the way there must be a "sweet point". However, the sweet point should not exist because infinitesimal variation should not change the concept, even on the sweet point. Therefore, the concept is not well-defined.
+:::
+
+For example, you can attack the concept `machine learning` by the following way:
+
+::: tip Example
+- Optimizing neural network on machines is an instance of `machine learning`.
+- You can adjust the process by replacing some of the learning steps with human-made steps. It is still an instance of `machine learning`.
+- You can adjust the process by replacing all the learning steps with human-made step except for one machine-made noise step. It is ridiculous if there are 100000 human-made steps and 1 useless machine-made step. However, it is still an instance of `machine learning`.
+:::
+
 ## Topology
 
 Topology is an example where continuous entities can be unambiguously represented by discrete entities.