>she didn’t find a way to tip the AI into “just do it, write it now” mode
worse, the longer an LLM conversation goes on, but especially with constricted/free models (yes the simple chat interface they are likely using) the harder it is to get an LLM into this mode even *IF* you know the right words to say
at that point the best way forward is to terminate the exchange entirely, and to start off with the right initial message, instantly getting into coding mode. a non technical person will not know this and be stuck in feature theory crafting mode in perpetuity, or worse in an endless "excuses' mode as the LLM diverts ant attempt at coding into reasons why its not going to: "i wont output incomplete/broken code! that would require too many lines of code sorry i wont do it! i wont be able to get it perfect so i wont attempt it! but heres more features and theory crafting"
will a non technical person know to end the conversation and start fresh? not likely unless they have a lot of experience already with LLMs
Also, the correct way to LLM is to constantly trial-and-error in new/branched contexts.
Remember the LLM is not a human employee. You don't have to say "yes and" to whatever crap they produced so as to not hurt their feelings or infringe upon their creative autonomy, nor do you have to defend the correctness of your original instructions so that they don't think less of you for asking them to chase the wrong goose.
I probably generate 20-50 lines of code for every 1 line that I keep.
This is also why I think harnesses and things like Claude Code and OpenCode are false efficiency. The only way I can maintain my pace of branched trial-and-error is by using claude.ai/chat and manually extricating code fragments to and from my codebase. The human is still the best harness for production-level code.
> The only way I can maintain my pace of branched trial-and-error is by using claude.ai/chat and manually extricating code fragments to and from my codebase. The human is still the best harness for production-level code.
That’s been the way I do it.
I suppose that it will be considered “quaint,” soon enough, but I have found it to be effective.
And the best part is, I don't spend more than $20 a month on LLMs. Going manual and constantly branching keeps the contexts super lean.
I’m likely to switch to the $100/month sub, but I want to finish this project on the $20 one first, as a “proof of concept.”
I think it’s valuable enough to justify the price, and I want it to use the better model, as much as possible.
This is the only way I've been able to get LLMs to produce code I will actually use. Manually selecting the context for them and asking for a specific piece or similar
I started using harnesses because they are good for when something breaks and it's not trivial to investigate so I'll have the agent tell me what's happening, then using that to produce my own change
A technical person will also have have met those problems related to context and will know to drop a simple
"update an AGENTS file with relevant information"
To be able to navigate that faster on longer tasks. Meanwhile, the lay person does not even conceive of the LLM as a file reading entity. To them, its machinations are its own, so these types of "dumb" (simple) solutions are not even on the deck of cards.