I have the opposite experience where Gemini (even the flash models) has the only useful model for my reverse engineering related use case. My hunch is Google utilizes its free access to entire Google search indices to train itself from niche non-English speaking community websites, much frequently and in a "relevant" manner, which in the end gives these models the most up to date info for this particular kind of work. Every other model is just either 10 years outdated with their answers or simply hallucinates like waaaay crazy.
That analogy may not be suitable for this case because value proposition between the aesthetics vs the function is different for visual art projects compared to software. There is also the maintainability factor where most aged software (especially the closed source ones in private sector) change maintainers every few years. Old maintainers most often lose access to the source code and become unreachable after leaving their job.
> I'm not quite sure why you'd want to run Erlang on it, but the hardware exists.
Erlang is invented before IoT was a thing to facilitate distributed computing for telecommunication in a highly reliable manner. It makes perfect sense to adapt it for driving fleets of cheap IoT devices.
Low resource footprint, written in Go, embed-able in any Go project as a library, compiles to mobile with little to no modification, supports config change without restart, has plugin API.
These were the reasons why we used it in my previous job.
It would be "artificial" only if LLMs performed badly despite having an equal amount of data containing examples of eastern customs in its training set. Even that's arguable since we don't (didn't) have the benchmarks for this particular case before.
It's no different than GPT answering a prompt with "That's a wonderful idea!", except it's in a different language than English. It's a good thing if LLMs can do this in every language and for any culture with no compromise.
> Linux separates things such that I was looking at C files in drivers/platform/x86 and header files in include/linux/platform_data/x86. And the ACPI code lives other places as well. It’s all very orderly, but at times it felt like navigating a grocery store that arranges products in alphabetical order. Logical, but not exactly cozy.
Phind does one thing and it does it really well. I use it because it has a real, positive impact on my growth. I love the fact that it is not Jarvis, but just a really helpful library assistant. Please don't lose the focus! Stay awesome.
I think this is very accurate. The exception is probably those block buster games. Those probably get direct consultancy from NVIDIA during the development to make them NVIDIA-ready from day 1.