[schmorptron]

Oh wow, a lot of focus on code from the big labs recently. In hindsight it makes sense that the domain the people building it know best is the one getting the most attention, and it's also the one the models have seen the most undeniable usefulness in so far. Though personally, the unpredictability of the future where all of this goes is a bit unsettling at the same time...

[modeless]

OpenAI and Anthropic are both trying to automate their own AI research, which requires coding.

[doctoboggan]

Along with developers wanting to build tools for developers like you said, I think code is a particularly good use case for LLMs (large language models), since the output product is a language.

[fragmede]

It's because the output is testable. If the model outputs a legal opinion or medical advice, a human needs to be looped in to verify that the advice is not batshit insane. Meanwhile, if the output is code, it can be run through a compiler and (unit) tests run to verify that the generated code is cromulent without a human being in the loop for 100% of it, which means the supercomputer can just go off and do it a thing with less supervision.

[martinald]

Thing is though if you are good at code it solves many other adjacent tasks for LLMs, like formatting docs for output, presentations, spreadsheet analysis, data crawling etc.

[baq]

Congrats! You’re now on the p(doom)-aware path. People have been concerned for decades and are properly scared today. That doesn’t stop the tools from being useful, though, so enjoy while the golden age lasts.

https://en.m.wikipedia.org/wiki/P(doom)

[neuronexmachina]

I think coding is also the area where companies are most likely to buy large team licenses.