Comment by saxenaabhi

That's not my experience. Over christmas I re wrote a restaurant POS application from laravel to vue/wrangler using bolt + chatgpt. My exact steps:

1) I took my db schema and got chatGPT to convert it to typescript types and stub data.

2) Uploaded these types to bolt and asked it one by one to create vue components to display this data(Catalog screen, Payment Dialogs, Tables page etc) and to use fetcher functions that return stub data.

3) Finally I asked it replace stub data with supabase.rpc calls, and create postgres functions to serve this data.

While I had most of the app done in a few days, testing, styling and adding bug fixing took a month.

Some minor stuff was done manually by me: Receipt printer integration integration because bolt wasn't good at epson xml or related libraries that time.

Finally we released early feb and we received extremely good feedback from our customers.

However now I'm using claude and even higher percentage of code is generated by it now. Our feature velocity is also great. After launch we have added following features in 6m

1) Split Table Payments 2) Payment Terminal Integration 3) Visual Floor plan viewer 4) Mobile POS for waiters without tablet 5) Reports, Dashboard, Import/Export 6) Loyalty programs with many different types of programs 7) Self-service Webshop with realtime group ordering 8) Improved tax handling 9) Multicourse orders("La'suite") 10) Many other smaller features

This would be very hard to achieve without AI for most one-person engineering teams. Although tbf not impossible.

> The new way: The entire premise of AI coding tools is to automate the thinking, not just the typing. You're supposed to describe a problem and get a solution without understanding the details. That's the labor-saving promise.

I think here the OP introduces a strawman since as many people have pointed out, the labour saving happens in automating menial tasks and no one sane should give up "understanding the details".

> >I was thinking of all the classic exploratory learning blog posts. Things that sounded fun. Writing a toy database to understand how they work, implementing a small Redis clone. Now that feels stupid. Like I'd be wasting time on details the AI is supposed to handle.

On the contrary. Reading ToyDB[1] source code helped me understand MVCC and Isolation levels. That's knowledge that's valuable for a systems architect since at the end LLMs are just fancy word generators.

[1] https://github.com/erikgrinaker/toydb