-1 time because it would have never have happened without AI

The specs would not likely have happened at all, since this is a solo project; although this experience has led me to want to write these things out more thoroughly, even for myself. It's impressive how little work I need to put in going this route to have fairly thorough actionable specs for pretty much every major decision I've made through the process.

The commits - some would be detailed, plenty would have been "typo" or "same as last commit, but works this time"

The tests - Probably would have been decent for the API, but not as thorough. Likely non-existent for the UI.

As for time - I agree with the other response - I wouldn't have taken the time.

I'm ok on speed. Not 10x or anything, but I've been writing full-stack web apps and websites from scratch for a quarter century.

The issue is getting the LLM to write _reasonably decent_ code without having to read every line and make sure it's not doing anything insane. I've tried a few different methods of prompting, but setting up a claude sub-agent that's doing TDD very explicitly and ensuring that all tests pass after every iteration has been most effective.

My first attempt was so fast, it was mind-bending. I had a "working" App and API running in about a day and a half. And then when I tried to adjust features, it would start changing things all over the place, LOTS of tests were failing, and after a couple prompts, I got to a point where the app was terribly broken. I spent about a day trying to prompt my way out of a seemingly infinite hole. I did a more thorough code review and it was a disaster: Random code styles, tons of half-written and abandoned code, tests that did nothing, //TODOs everywhere, and so, so many tweaks for backwards compatibility - which I did NOT need for a greenfield project

At that point I scrapped the project and adjusted my approach. I broke down the PRD into more thorough documentation for reference. I streamlined the CLAUDE.md files. I compiled a standard method of planning / documenting work to be done. I created sub-agents for planning and implementation. I set up the primary implementation sub-agents to split up the spec into bite-sized portions of work ("30-45 minute tasks").

Now I'm at the opposite side of the spectrum - implementation is dog slow, but I rarely have to read what was actually written. I still review the code at large after the primary tasks are finished (comparing the feature branch against main in my IDE), but for the most part I've been able to ignore the output and rely on my manual app tests and then occasionally switch models (or LLMs) and prompt for a thorough code-review.

Sometimes I spend half an hour writing a prompt and realize that I’ve basically rubber-ducked the problem to the point where I know exactly what I want, so I just write the code myself.

I have been doing my best to give these tools a fair shake, because I want to have an informed opinion (and certainly some fear of being left behind). I find that their utility in a given area is inversely proportional to my skill level. I have rewritten or fixed most of the backend business logic that AI spits out. Even if it’s mostly ok on a first pass, I’ve been doing this gig for decades now and I am pretty good at spotting future technical debt.

On the other hand, I’m consistently impressed by its ability to save me time with UI code. Or maybe it’s not that it saves me time, but it gets me to do more ambitious things. I’d typically just throw stuff on the page with the excuse that I’m not a designer, and hope that eventually I can bring in someone else to make it look better. Now I can tell the robot I want to have drag and drop here and autocomplete there, and a share to flooberflop button, and it’ll do enough of the implementation that even if I have to fix it up, I’m not as intimidated to start.

I've found it works really well for exploration as well. I'll give it a new library, and ask it to explore the library with "x goal" in mind. It then goes and agents away for a few minutes, and I get a mini-poc that more often than not does what I wanted and can also give me options.

I am certain it has much to do with being in the training data or not.

I have loved GPT5 but the other day I was trying to implement a rather novel idea that would be a rather small function and GPT5 goes from a genius to an idiot.

I think HN has devolved into random conversations based on a random % of problems being in the training data or not. People really are having such different experiences with the models based on the novelty of the problems that are being solved.

At this point it is getting boring to read.

Days instead of weeks, basically. Hard to truly quantify but I'm bloody minded enough to reimplement things three times to check and even with foresight the AI is faster.

Now I'm trying to find a text-to-Ginsberg translator. Maybe he's who I sound like in my head.

None but it's on the list! Actually using it to prototype a complete audio visual tracking and annotation tool, so feeding it back into itself is a logical next step.