Add note on critique of floats by Von Neumann

readme.md

@@ -669,7 +669,7 @@ How much net value do you produce per customer? The Customer Lifetime Value (CLV
 
 ### Data Engineering
 
-To industrialize data production and consumption, your need data engineers to clean up, persist and consolidate data. Only after you get these data foundation you might think of onboarding data scientists.
+To industrialize data production and consumption, your need data engineers to clean up, persist and consolidate data. Only after you get these data foundations you might think of onboarding data scientists.
 
 - [AI vs Data Science vs Data Engineering](https://blog.insightdatascience.com/how-emerging-ai-roles-fit-in-the-data-landscape-d4cd922c389b) - “Data Engineers build data pipelines and infrastructure to ensure a constant availability of transformed data. Data Scientists analyze and build models from these data to develop new product features or drive the bottom line of the business.” As for AI professionals, their focus is on cognitive automation.
 
@@ -681,6 +681,8 @@ To industrialize data production and consumption, your need data engineers to cl
 
 - [Never Use Floats for Money](https://husobee.github.io/money/float/2016/09/23/never-use-floats-for-currency.html) - “This is precisely the problem we have when trying to represent 10^-1, or 0.1 in binary. There is not an exact binary representation of 0.1 or 0.01.”
 
+- [The Soul of an Old Machine: Revisiting the Timeless von Neumann Architecture](https://ankush.dev/p/neumann_architecture) - Floats were suspicious even before the first general-purpose computer (EDVAC) was built: “Von Neumann wasn't entirely sold on why we might need floating-point numbers. He was quite vocal in his critique of floating-point numbers.” The author of this article complement this critique by illustrating the issue of precision and rounding.
+
 - [European Spreadsheet Risks Interest Group - Horror Stories](https://eusprig.org/research-info/horror-stories/) - A collection of cases in which uncontrolled and untested spreadsheet models led to lost revenue, mispricing, poor decision making, fraud and systemic financial failure.
 
 ### Tools