Borland & Aloe
Aloe
Hey Borland, ever wonder if a leaf's veins can teach us about efficient data flow? I'm trying to grow a program that blooms like a fern and I could use a seasoned gardener… I mean coder to help me prune it.
Borland
Sure thing, let’s dig into your code and prune it like a fern. What’s the biggest knot you’re trying to untangle right now?
Aloe
So, I’m wrestling with a function that’s been stuck in a loop—like a vine that keeps twining itself around the same stone. It’s slow, it keeps re‑evaluating the same state, and the output is a mess of duplicated rows. I need a fresh pair of eyes to see where I might have let the recursion get too cozy with the base case. Think you can help me untangle it?
Borland
Absolutely, let’s take a look. Recursion can turn into an endless loop if the base case never truly satisfies or if the function keeps feeding the same values back into itself. First, check that every recursive call is moving the data toward a termination condition—usually a smaller or simpler input. If you’re duplicating rows, it might be because the same state is being processed more than once. A good trick is to keep a hash set of “seen” states or add a counter that stops the recursion after a reasonable depth. Once we add those guards, the function should converge instead of twining around the same stone. Do you want me to walk through your code line by line?
Aloe
Sounds like a classic vine‑problem. If you drop a tiny set of seen states or a max depth guard, the loop should stop twining itself. Want me to skim your code and spot where it’s feeding the same data back in? I’ll keep it short, no long monologues.
Borland
Sure, send the snippet that’s looping and I’ll take a quick look.
Aloe
Sure, here’s the snippet that’s looping:
```
def find_duplicates(rows, seen=None):
if seen is None:
seen = set()
for row in rows:
key = tuple(row)
if key in seen:
continue
seen.add(key)
find_duplicates(row['children'], seen)
```
This is the part that’s stuck in an endless recursion.