Videogame speed running has this problem solved. Livestream your 10x engineer LLM usage, a git commit annotated with it's prompt per change. Then everyone will see the result.
This doesn't seem like an area of debate. No complicated diagrams required. Just run the experiment and show the result.
I’m not alone in this - there are tons of other examples of people showing how they use LLMs online; you just need to search for them.
The article provides zero measurement, zero examples, zero numbers.
It's pure conjecture with no data or experiment to back it up. Unfortunately conjecture rises to the top on hackernews. A well built study on LLM effectiveness would fall off the front page quickly.
People always say "you just need to learn to prompt better" without providing any context as to what "better" looks like. (And, presumes that my prompt isn't good enough, which maybe it is maybe it isn't.)
The easy way out of that is "well every scenario is different" - great, show me a bunch of scenarios on a speed run video across many problems, so I can learn by watching.
If I use LLMs to code, say a Telegram bot that summarise the family calendars and current weather to a channel - someone will come in saying "but LLMs are shit because they can't handle this very esoteric hardware assembler I use EVERY DAY!!1"
This thread has hundreds of comments where people are screaming that everyone needs to learn AI coding.
If it was such an edge would they not otherwise keep quiet?
You could say it is a lack of imagination or not connecting the dots, but I think there is a more human reason. A lot of people don't want the disruption and are happy with the status quo. I'm a software engineer so I know how problematic AI may be for my job, but I think anyone who looks at our current state and the recent improvements should be able to see the writing on the wall here.
I for one am more curious than afraid of AI, because I have always felt that writing code was the worst part of being a programmer. I am much happier building product or solving interesting problems than tracking down elusive bugs or refactoring old codebases.
Let's all just muse some and imagine what the next cycle of this wheel will look like.