Sure, use a language that has access to some kind of shared global variable. For example %_SHARED in plperl:Something like: CREATE OR REPLACE FUNCTION set_foo(name text) returns void as $$
my $name = shift;
die "set_foo() has already been called" if ($_SHARED{'set_foo'});
$_SHARED{'set_foo'} = $name;
$$
LANGUAGE plperl;
CREATE OR REPLACE FUNCTION get_foo() returns text as $$
my $name = shift;
return $_SHARED{'set_foo'} || 'nobody';
$$
It wo
The methodical approach Alex took here is fascinating - it mirrors real-world AI system debugging when production models behave unexpectedly. The key insight about treating the network as a constraint solver rather than trying to trace circuits by hand is brilliant. In production AI systems, we often face similar challenges where the "learned" behavior isn't actually learned but engineered, and you have to reverse engineer the underlying logic. The parallel carry adder implementation in neural n
