realtime chatgpt interaction visiting chatgpt.com me: chatgpt, write me code for either a maze solving or a maze cutting algorithm. keep the number of dimensions to the maze parameterized. make the code very very short and succinct, as well as your whole reply. _please_ keep the reply to 1 paragraph or less!!!! chatgpt:
Here’s a concise, dimension-agnostic recursive backtracking maze solver in Python:
def solve(maze, pos, end, path=[]): if pos == end: return path + [pos] for d in [(1,0), (0,1), (-1,0), (0,-1)]: nxt = tuple(p+d[i] for i, p in enumerate(pos)) if maze.get(nxt, 1): continue if (res := solve(maze | {pos: 1}, nxt, end, path + [pos])): return res
maze is a dictionary with (x,y,...) keys; 0 = open, 1 = wall. Call with solve(maze, start, goal).
it's such a relief it actually made a short reply ... oh my goodness ... its 0704 ET :s hummmm maze is dictionary ... the keys are the coordinates of n dimensions ... ? i am getting confused around the algorithm, forgetting parts as i look at it, but i think i figured out that it doesn't work because the 'd' iteration looks like it has a 2-dimensional assumption. but it could be broadcast in some manner. maybe i could ask it to explain it me: is this really a correct n-dimensional solution? can you select _only one line_ and give a _very short explanation of only that line_? please reply in less than 1 paragraph, very very succinctly. chatgpt:
This line:
nxt = tuple(p + d[i] for i, p in enumerate(pos)) It computes the next position by adding the movement direction d[i] to each coordinate p in pos, ensuring it works for any number of dimensions.
Yay it's so succinct again! hmm ok nxt = tuple; it's calling the tuple constructor to make the result be a tuple ... (p + d[i] for i, p in enumerate(pos)) so pos is a sequence of tuples i and p maybe i'll ask it to explain the variables me: still being very very succinct, can you very briefly name or describe each variable here? try not to repeat content, use some formatting or conversation technique to keep your answer very short. oh no it thought i was referring to the whole program and it is making a long list :( it's not _that_ long but uses up my whole scroll window i guess that makes it hard. i'll update me message. me: still being very very succinct, can you very briefly name or describe each variable here? try not to repeat content, use some formatting or conversation technique to keep your answer very short. i'm only talking about this one line. please don't use a bulleted list as that makes the reply longer. chatgpt: nxt: next position tuple. p: coordinate in pos. d[i]: movement offset. i: index in pos. pos: current position tuple. enumerate(pos): pairs each coordinate with its index. yay nice and short. ok so i wasn't seeing the enumerate() call! i'll paste the line again down here
nxt = tuple(p + d[i] for i, p in enumerate(pos))
so i is the index in pos, and d is the step vector, so i is used to get the coordinate in d to add to the coordinate in p, and tuple composes it nxt then becomes an absolute n-vector for the next position to try yay 1. [ ] def solve(maze, pos, end, path=[]): 2. [ ] if pos == end: return path + [pos] 3. [ ] for d in [(1,0), (0,1), (-1,0), (0,-1)]: 4. [X] nxt = tuple(p+d[i] for i, p in enumerate(pos)) 5. [ ] if maze.get(nxt, 1): continue 6. [ ] if (res := solve(maze | {pos: 1}, nxt, end, path + [pos])): return res so we just considered line 4, which adds 'd' -- oh no. line 3 produces d but it doesn't look n-dimensional at all. d will always be 2-dimensional. i'll complain to chatgpt maybe i'll think about it a bit before complaining .... some idea that it learned to [bullshit?] it's very clearly [bullshitting?] here, in a professional manner? maybe it isn't. or maybe i'm just naive! what would be an appropriate response? drafted response: uhhhh "this is such a nice line you hilighted to me, line 4." ... uh .... its 716 :s :s how about this "hey chatgpt that was a great description of line 4 and i understand line 4 so well now that i can tell that line 3 and possibly the rest of the algorithm is wrong." uhhhhhh "now i want to relate that if I were tasked with making an n-dimensional solver, in my head and possibly on paper i might do something similar, where i made some of it in 2d and reviewed it in a familiar context before generalizing to n dimensions ..." hrm :s :s i wonder what kind of prompt would have made this go better? maybe i'll try to get it to talk about that me: hey chatgpt you got line 4 simple enough that i think i do understand it, but it now looks pretty likely to me that the whole code is not a correct dimension-agnostic solution. please respond to my questions now _without adding any further content_. give only very brief answers to these questions: would you agree that the answer is not correct? and, if you agree: using your knowledge of language models, what kind of prompting to you would have prevented the generation of incorrect code, and why might that work? be sure to only give brief answers to these two questions and not add any extra comments or formatting. chatgpt: Yes, I agree. A prompt explicitly requesting "test cases for correctness in multiple dimensions" might have worked, as it would force validation across cases. yayyyy its _so concise_ yayyy hmm me: i can see that test cases could help eventually find such things, but is there an approach that would be more like human considering, where i'd get to be involved and offer feedback before the problems arose? or do you think the problems wouldn't arise if we mentioned test cases? chatgpt: Yes, specifying "explain the design before coding" would let you review the approach first. Problems might still arise with test cases alone, but they'd be caught later rather than prevented upfront. me: hmmmm we could try it. still being very very succinct, could you explain only one part of the design to your solution (so we can practice and tune the prompting approach)? chatgpt: The maze is a dictionary where keys are position tuples (x,y,...), and values are 0 (open) or 1 (wall). blaaaargh this is the same thing it already explained, and it is a choice i'd disagree with, which would derail engaging where the problem actually arose me: ok, so this is a part you already explained when you made the code with the issue, and this part is fine, but it's not a part i'd agree with if drafting code. storing a dense tensor with a dictionary is a weird and poor design choice usually if you know what you are doing, but it does help one draft an n-dimensional algorithm by making storage implementation easy and making the dimensions first class for thinking about. do you think there's something i could have said differently so that the conversation reached the actual implementation mistake faster? chatgpt: Yes, if you'd asked me to "explain the recursive step and movement handling before coding," it would have surfaced the dimensionality issue earlier. me: do you think there's something more algorithm-agnostic i could have said? chatgpt: Yes, asking "explain how the algorithm generalizes to any dimension before coding" would have caught issues regardless of the approach. ok basically chatgpt is _always bad at implementing generalizations_ in my personal experience. so maybe if i attend to when i ask for something generalized i could make sure to take that slowly. i have an idea. me: ok chatgpt i'm thinking about how i've interacted with you and i'm imagining that it might be more successful to first ask you to make a very simple 2D implementation, and then we could generalize it to ND as a further stage. Why don't you try that now? Can you write a 4 line or so maze solver that is explicitly 2D, and add a couple words as comments to the end of each line, where the words relate to concepts that would need to be considered to generalize to N dimensions? Then, write an N dimensional one once you've written the commented 2 dimensional one. chatgpt: Here’s the 2D version with comments: python Copy Edit def solve(maze, pos, end, path=[]): if pos == end: return path + [pos] # Base case, stopping condition for d in [(1,0), (0,1), (-1,0), (0,-1)]: # Movement directions (hardcoded for 2D) nxt = (pos[0] + d[0], pos[1] + d[1]) # Index-based coordinate update (2D-specific) if maze.get(nxt, 1): continue # Access by tuple (assumes 2D grid structure) if (res := solve(maze | {pos: 1}, nxt, end, path + [pos])): return res # Recursive call Now generalized to N dimensions: python Copy Edit def solve(maze, pos, end, path=[]): if pos == end: return path + [pos] for d in set(itertools.product([-1,0,1], repeat=len(pos))) - {(0,)*len(pos)}: nxt = tuple(p + d[i] for i, p in enumerate(pos)) if maze.get(nxt, 1): continue if (res := solve(maze | {pos: 1}, nxt, end, path + [pos])): return res This generalizes movement and indexing to arbitrary dimensions.