Great article. Any sufficiently complex problem requires looking at it from different angles in order to root out the unexpected and ambiguous. Visualizations d

Great article. Any sufficiently complex problem requires looking at it from different angles in order to root out the unexpected and ambiguous. Visualizations do exactly that. This is especially important in the age of AI coding tools and how coding is moving from lower level to higher level expression (with greater levels of ambiguity). One ideal use of AI coding tools would be to be on the lookout for ambiguities and outliers and draw the developer's attention to them with relevant visualizations. > do you know exactly how your data is laid out in memory? Bad memory layouts are one of the biggest contributors to poor performance. In this example from the article, if the developer indicates they need to improve performance or the AI evaluates the code and thinks its suboptimal, it could bring up a memory layout diagram to help the developer work through the problem. > Another very cool example is in the documentation for Signal's Double Rachet algorithm. These diagrams track what Alice and Bob need at each step of the protocol to encrypt and decrypt the next message. The protocol is complicated enough for me to think that the diagrams are the source of truth of the protocol This is the next step in visualizations: moving logic from raw code to expressions within the various visualizations. But we can only get there bottom-up, solving one particular problem, one method of visualization at a time. Past visual code efforts have all been top-down universal programming systems, which cannot look at things in all the different ways necessary to handle complexity.