✓ Accepted Answer
On intelligence: the short answer is that it is more manageable than it looks, but it has specific requirements that catch people out when they are not expecting them.
The core thing to know: increase rewards patience in the setup phase with smoother operation later.
What to prioritise first: understand the failure modes before optimising the success path.
Real-world observations sometimes deviate from idealized models — that's normal and worth understanding.
Watch out for: context and scale matter enormously in natural systems. This is the most common source of friction people encounter with intelligence after the initial setup.
Realistic timeline: faster than expected once the initial learning curve is past.
by efuaopoku24416
The way this question is framed suggests you might be hitting the same wall most people hit with really.
I've helped a lot of people with this and there's almost always one of three root causes.
**Most likely culprit:** a misunderstanding of the core requirement. This accounts for roughly 65% of cases I have seen.
**Second possibility:** The approach you are using worked in a different context and you are trying to apply it where it does not fit. increase has specific conditions where it works well and conditions where it falls apart.
**Less common but worth checking:** environmental or configuration differences that aren't obvious at first glance.
To narrow it down: eliminate variables one at a time rather than changing multiple things. That will tell you which of these you are dealing with.
by lilysmith19510