MachineBurning

Game design is hard. Back in the day I released 4 flash games. 2 completely tanked, 1 did ok, and one went quite well (hundreds of years total time spent in game).

There's a lot to getting it right, and like all software, you have to built it for your target market. There's no easy AI solution to getting a fun and engaging core loop. Nor is there one for building the right level of complexity and balancing the learning curve.

I think a lot of people who can't/don't code see themselves as game designers and had thought that AI would let them make games, and are now finding it wasn't really about the code after all. That, and if you can't code, vibe coding alone isn't really good enough for much beyond flash-level games (yet).

Not sure why you're picking apart the wording. They're clearly stating an opinion, and writing "seems like" makes it clear that it's an opinion. There is no "to me" but IMO it's implicit.

There's also:

4. Tell claude what to do instead, which will update the plan base on what you say.

5. Add comments to the plan directly - similar to 4 - but you can comment on specific parts.

Note: I use the VSCode extension, not sure if it differs in terminal mode.

IMO it was mostly that people didn't want to rewrite (and maintain) their code for a new proprietary programming model they were unfamiliar with. People also didn't want to invest in hardware that could only run code written in CUDA.

Lots of people wanted (and Intel tried to sell, somewhat succesfully) something they could just plug-and-play and just run the parallel implementations they'd already written for supercomputes using x86. It seemed easier. Why invest all of this effort into CUDA when Intel are going to come and make your current code work just as fast as this strange CUDA stuff in a year or two.

Deep learning is quite different from the earlier uses of CUDA. Those use cases were often massive, often old, FORTRAN programs where to get things running well you had to write many separate kernels targeting each bit. And it all had to be on there to avoid expensive copies between GPU and CPU, and early CUDA was a lot less programmable than it is now, with huge performance penalties for relatively small "mistakes". Also many of your key contributers are scientists rather than profressional programmers who see programming as getting in the way of doing what they acutally want to do. They don't want to spend time completely rewriting their applications and optimizing CUDA kernels, they want to keep on with their incremental modifications to existing codebases.

Then deep learning came along and researchers were already using frameworks (Lua Torch, Caffe, Theano). The framework authors only had to support the few operations required to get Convnets working very fast on GPUs, and it was minimal effort for researchers to run. It grew a lot from there, but going from "nothing" to "most people can run their Convnet research" on GPUs was much eaiser for these frameworks than it was for any large traditional HPC scientific application.

The person who wrote the post says that they can produce the same quality content in "2-3 days" instead of "several weeks", and that there are two people working in essentially the same position.

If the amount of content to be produced remains constant, then from a purely financial point of view the company should be looking at cutting one of them. AI would have then taken their job.

For the amount of content to be produced to not remain constant either the studio would have to go for increased art quality, or scale up the rest of the business to keep up with the new art productivity. It's not clear they'd make that decision over cutting their art department spending in two. At the very least their job is at risk.

If I understand you correctly, this doesn't work because ChatGPT isn't the only language model in the world. It's only the most popular at this point in time.

Guilds?