It doesn't have to be this messy. If I were the maker I would treat this as a good first version and transfer the ownership to the business slowly. This is just like working with any consultant.
The business is run by his wife, and if they had a SWE(-like) already, that person would’ve made this. But instead, the husband did and now owns it. He also open sourced it, so he has to live with the inevitable consequences of that too.
Isn't this proposal closely matching with the approach OpenSpec is taking? (Possibly other SDD tool kits, I'm just familiar with this one). I spend way more time in making my spec artifacts (proposal, design, spec, tasks) than I do in code review. During generation of each of these artifacts the code is referenced and surfaces at least some of the issues which are purely architecture based.
"For this invention will produce forgetfulness in the minds of those who learn to use it, because they will not practice their memory. Their trust in writing, produced by external characters which are no part of themselves, will discourage the use of their own memory within them. You have invented an elixir not of memory, but of reminding; and you offer your pupils the appearance of wisdom, not true wisdom, for they will read many things without instruction and will therefore seem [275b] to know many things, when they are for the most part ignorant and hard to get along with, since they are not wise, but only appear wise."
> the journal system isn't perfect but its the only real check we have left.
I wish I could agree but Nature et al continually publish bad, attention-grabbing science, while holding back the good science because it threatens the research programmes that gave the editorial board successful careers.
Isn't data entry a really good usecase for the LLM technologies? Of course depending on the exact usecase. But most "data entry" jobs are data transformation jobs and they get automated using ML techniques all the time. Current LLMs are really good at data transformation too.
If your core feature is data entry, you probably want to get as close to 100% accuracy as possible.
"AI" (LLM-based automation) is only useful if you don't really care about the accuracy of the output. It usually gets most of the data transformations mostly right, enough for people to blindly copy/paste its output, but sometimes it goes off the rails. But hey, when it does, at least it'll apologise for its own failings.
What difference does it make how many people use it? Complex software exists all over the world for handful of users. I personally work in an industry where anything we create will be used by at max 100 people worldwide. Does it diminish the complexity of code? I think not.
> #3: Limit what they test, as most LLM models tend to write overeager tests, including testing if "the field you set as null is null", wasting tokens.
Heh, I write this for some production code too (python). I guess because python is not typed, I'm testing if my pydantic implementation works.
Maybe a dumb question but does this mean model quality may vary based on which hardware your request gets routed to?
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