I'm a marketer. I write a lot. GPT-4.5 is really good at natural sounding writing. It's nearing the point where it would be worth $200/mth for me to have access to it all the time.
Actually I forgot there's a second one he read all the way through, for which he defined the initial concept and early plot, but then the rest of the plot and the writing were all done by GPT-4.5.
The code is kind of basic, and each chapter is written without the full text of prior chapters, but the output isn't bad.
https://gist.github.com/rahimnathwani/41e5bc475163cd5ea43822...
I find the quality rapidly degrades as soon as I run out of context to fit the whole text of the novel. Even summarizing the chapters doesn’t work well.
This would be fine for a cheap model, but GPT 4.5 was not cheap!
I would have liked to have fewer, longer chapters, but my (few) experiments at getting it to output more tokens didn't have much impact.
I'm firmly convinced that being able to troubleshoot code, even code generated by LLMs, and to write guidelines and tests to make sure it's functioning, is a skill of a shrinking pool
For smaller stuff, great. Everyone's the same. The second your application starts gaining responsibility and complexity, you're going to need to be able to demonstrate reproducibility and reliability of your application to stakeholders.
Like, your job increasingly will be creating interface checkpoints in the code, and then having the model generate each step of the pipeline. That's great, but you have understand and validate what it wrote, AND have a rich set of very comprehensive tests to be able to iterate quickly.
And as mentioned, on top of that, large swaths of the field of new people have their brains completely rotted by these tools. (certainly not all new/young people, but i've seen some real rough shit)
If anything, I see a weird gap opening up
- people who dont adopt these tools start falling out of the industry - they're too slow
- people who adopt these tools too early stop getting hired - they're too risky
- people who have experience in industry/troubleshooting/etc, who adopt these tools, become modern day cobol programmers - they're charging $700 an hour
the real question to me is this: does the amount of people taken out of the pool by being slow or risky due to these tools, outpace the reduction in jobs caused by these tools?
Well, today only scientists can make stone tools.
It’s like if I was hiring a mathematician. I’d expect them to use a calculator or CAS package but I’d also expect them to be able to do everything by hand. I wouldn’t ever waste their time by making them do that, of course.
I was trying to say that dropping old technologies isn't always bad.
> It’s like if I was hiring a mathematician.
Do you expect candidate to memorize all theorems up to date. Usually people forgetting things they don't actively use. But they are able to refresh their knowledge if needed. I've learned quite a lot, but no, I don't remember even key theorems from partial differential equations (used them in my diploma). I can refresh and relearn quickly, I'm sure.
Using LLM without understanding disqualifies the candidate, even monkey can do it. But if he deeply understands the subject and uses LLM for like handbook for minor details.. that's different.
Completely missing the point. I expect them to have enough knowledge to briefly study the theorems and understand how to apply them. I’m not trying to quiz people, I’m trying to get things done - and done well.
And for the stuff I’m doing, it’s required that any engineer understand what they’re building and why.
> Using LLM without understanding disqualifies the candidate, even monkey can do it. But if he deeply understands the subject and uses LLM for like handbook for minor details.. that's different
The problem is that they don’t understand the subject and overly rely on LLMs. Completely falling apart during in person interviews. Surface knowledge of everything and no depth.
Using LLMs isn’t inherently bad but I’ve seen severe side effects from students and junior engineers who over rely on it.
A great developer + an AI = productive.
A shitty developer + an AI = still shit.
AI does not make all developers the same. And it doesn't make all marketers the same.