I've been in similar situations, I've realized, you make these llms accomplish lots of difficult tasks if you prompt it correctly, and that is a form of art! If a colleague of mine, who's incredible at prompting, did impressive things with gpt-3, so I am sure o3 can do even more wilder stuff.

What did he do with gpt-3?

It was mostly coding related tasks we had

gpt-3 could not do any coding tasks.

What makes you say so?

Because gpt-3 was not trained to do coding tasks. It could do a simple autocomplete. Perhaps you are confusing it with gpt-3.5?

I vividly remember it being in the same period as OpenAis Codex

Codex paper confirms that GPT-3 could not do any coding tasks. It's right there in the abstract: https://arxiv.org/abs/2107.03374

Might be GPT-3.5 then, but I am certain this was before the GPT-4 era. But that's besides the point, prompting it correctly has a huge effect on the outcome and its ability to suffice your need. So saying o3 being very unusable is hard to believe in my experience

o3 is definitely usable, as I said, it solved about half of the coding tasks I tried. My problem with your original comment was "bottlenecks in genAI is not the knowledge or accuracy". Knowledge and accuracy are absolutely the main bottlenecks for LLMs today. Hallucination rate for o3 and o4-mini models have doubled (compared to o1), and OpenAI does not understand why. If my AI model is not accurate, and if it makes up fake knowledge I don't care how fast it is - I will have to spend more time double checking its output than the time I saved by getting that output faster.