It's unlikely I would have had the confidence to do it from YouTube alone, specific diagnostic help and a full diagram to work off of was extremely helpful. I had it prepare an SVG of the whole assembly with all the measurements and parts labeled.
I expect that it's good for common use cases that's well documented. But then, so are a lot of other approaches.
I find it telling that the highest praise for LLMs comes from people using it for something where they admittedly have very little domain knowledge. Domain experts usually mention major caveats. I've been testing them on subjects where I already understand the problem well, and I've yet to see any outputs that would make me trust them on things I don't already know.
Anyway, my pool's looking great and I gained some new skills. I probably could have gotten there with books and YouTube alone, but having another tool at my disposal made me a bit more confident.
That’s the thing with AI - its responses sound plausible enough to non-experts but time and time again I see experts in any given field being able to identify AI content by pinpointing subtle but crucial errors. That’s one of its dangers - it gives you enough confidence to shoot yourself in the foot.
Maybe, but that's part of the experience of learning. I plan to maintain pools for the rest of my life, if I made an oversight which costs me down the line then the lesson will be that much more memorable.
This is an above-ground pool with a pump and a filter, the stakes are relatively low. In the absolute worst case I could rip it all out and pay a pro to do it for the price I was quoted.
I guess what you're talking about is sanitizer, in my case we use chlorine. I test it every time we swim, but I wouldn't have needed an LLM for that. It's very straight forward to maintain pool chlorine, my Dad taught me that when I was 13.
Also, ask people in the trades to review each other's jobs. They will harshly criticize each other too for missing basic things and then go on to vehemently disagree. As an outsider it doesn't mean much that an expert found some fault. They always find something to nitpick.
Yes, but with a human worker there is a chain of accountability. With AI, there is none.
The previous system was also installed by a non-professional and was mostly tubes. It leaked to all hell and looked generally redneck and awful.
First step was draining the pool and doing a nice deep clean. Then I ripped out all the original plumbing until it was just the pool outlets, pump, and filter.
I arranged it all and measured the dimensions. I fed the figures along with a tonne of photos and explanation to GPT. I spent a while talking pros/cons and landed on a design which lined up with what I'd seen on YouTube. I had it prepare me a full shopping list of PVC, tools, cements, etc. all linked to a local pool dealer. I picked it up the next day.
The PVC was all cut with a chop-saw then primed and cemented together. I found this part easier than I would have expected. I put a layer of TigerFlex hose between the PVC manifold and the pump/pool/filter inlets so it had some tolerance.
We've been swimming in it all summer, no issues whatsoever so far.
If ever there were three adjectives which do NOT apply to generative AI...
Funnily enough, I've had a somewhat mixed-to-hostile response when trying to upstream the vibecoded fixes, so I suspect using an LLM to fix broken open-source software (that human maintainers don't have the time to fix themselves, nor the humility to accept an LLM-authored fix) will become more of a thing going forward too.
Oh, it's also been identifying a bunch of patterns in sales data for my business that has been increasing monthly profit consistently since last November (around $4,000 USD, every month, cumulatively so far with no sign of slowing down - could easily be $10k/mo in increased gains by end of financial year).
I tend to respond quite well to AI-authored or assisted PRs to my project, but to be fair we maybe only get 3-5 PRs in a good month.
Only a novice would look at ai code and say “wow this is good”.
When you discuss design and architecture first, and write that out in a design doc or something along those lines, it works quite well for the most part.
And 9 out of 10 times when it produces some poor results, just asking "is this really a good approach?" or just stating "This code makes me very sad" it will most of the time do a really good job of analysing why that code is bad and how to improve it.
For a lot of problems "quick and good enough" is all that is required. I've used it a lot for managing my Home Assistant setup. Has saved me countless of hours.
In principle I could've done it myself, but I never would have, the time investment required to learn it wouldn't have been worth the value I get from it.
A pain plate of rice is bad food. But would you prefer starving too death? Or for others to starve?
Note that I am talking about frontier models and only the last 6-12 months. Opus was really the breakthrough point for me.