> You appear to be confusing "produce working code" with "exclusively produce working code".
The confusion is not mine own. From the article cited:
Dave Treadwell, Amazon's SVP of e-commerce services, told
staff on Tuesday that a "trend of incidents" emerged since
the third quarter of 2025, including "several major"
incidents in the last few weeks, according to an internal
document obtained by Business Insider. At least one of
those disruptions were tied to Amazon's AI coding assistant
Q, while others exposed deeper issues, another internal
document explained.
Problems included what he described as "high blast radius
changes," where software updates propagated broadly because
control planes lacked suitable safeguards. (A control plane
guides how data flows across a computer network).
It appears to me that "Amazon's SVP of e-commerce services" desires producing working code and has identified the ramifications of not producing same.
> That's why I'm writing a guide about how to use this stuff to produce good code.
Consider the halting problem[0]:
In computability theory, the halting problem is the problem
of determining, from a description of an arbitrary computer
program and an input, whether the program will finish
running, or continue to run forever. The halting problem is
undecidable, meaning that no general algorithm exists that
solves the halting problem for all possible program–input
pairs.
Essentially, it identifies that mathematics cannot prove an arbitrary program will or will not terminate based on the input given to it. So if math cannot express a solution to this conundrum, how can any mathematical algorithm generate solutions to arbitrary problems which can be trusted to complete (a.k.a. "halt")?
Put another way, we all know "1 + 2 = 3" since elementary school. Basic math assumed everyone knows.
Imagine an environment where "1 + 2" 99% of the time results in "3", but may throw a `DivisionByZeroException`, return NaN[1], or rewrite the equation to be "PI x r x r".
Why would anyone trust that environment to reliably do what they instructed it to do?
> You appear to be confusing "produce working code" with "exclusively produce working code".
The confusion is not mine own. From the article cited:
It appears to me that "Amazon's SVP of e-commerce services" desires producing working code and has identified the ramifications of not producing same.
That's why I'm writing a guide about how to use this stuff to produce good code.
> That's why I'm writing a guide about how to use this stuff to produce good code.
Consider the halting problem[0]:
Essentially, it identifies that mathematics cannot prove an arbitrary program will or will not terminate based on the input given to it. So if math cannot express a solution to this conundrum, how can any mathematical algorithm generate solutions to arbitrary problems which can be trusted to complete (a.k.a. "halt")?
Put another way, we all know "1 + 2 = 3" since elementary school. Basic math assumed everyone knows.
Imagine an environment where "1 + 2" 99% of the time results in "3", but may throw a `DivisionByZeroException`, return NaN[1], or rewrite the equation to be "PI x r x r".
Why would anyone trust that environment to reliably do what they instructed it to do?
0 - https://en.wikipedia.org/wiki/Halting_problem
1 - https://en.wikipedia.org/wiki/NaN
2 replies →