Does anyone know vscode extension that can use OpenAI API to perform code completions like Copilot? There are several ChatGPT-like UIs that you can self host and pay only for API and not for ChatGPT plus. For example, I'm using https://github.com/Yidadaa/ChatGPT-Next-Web . It would be nice to use Copilot in the same way.
GitHub Copilot loses an average of $20 per user per month
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Re: GitHub Copilot loses an average of $20 per user per month
#22I feel like the centralisation of server resources for something like copilot doesn’t really make sense. Many (most) professional developers are working on beefy laptops. If ever there were a case to run these models client side, a software developer’s laptop is probably the ideal place. What are the specs that are needed to run inference on these models?
Re: GitHub Copilot loses an average of $20 per user per month
#23Copilot is useless compared to chat-gpt4. There's really no comparison. As far as the costs, it seems like transmitting things repeatedly would be more expensive than the chat model where you just transmit things when you want them.
Re: GitHub Copilot loses an average of $20 per user per month
#24The moment I can train Copilot on my codebase and improve its predictions, I’d gladly pay more than $20. Even now, I find that I can’t live without it simply as a slightly more intelligent auto-complete.
I've used some prototypes that tried it. One of the things it does it regurgitate the old patterns you want to stop using and not the new stuff you do. Basically it's a copy pasta tech debt generator.
Re: GitHub Copilot loses an average of $20 per user per month
#25How came that usually privacy-minded hn crowd uses run-by-someone-else models so cheerfully and carelessly? You do realize any data sent to MS/openai/anyone will be stored forever and probably be used against you in the (near) future?
Are you sure it's the same people? That said, even if it is, there's something to be said for intentionally sharing a constrained set of code vs using something that spies on you in real life.
However. It will eventually, chunk by chunk, upload the full source code of the app you are writing. Complete with all typos and mis-pastes. Like youtube blocks videos with just a hint of copyrighted music, they may be able to detect use of "patented" algos or something like that and block you/ send a lawyer/etc.
Re: GitHub Copilot loses an average of $20 per user per month
#26Re: GitHub Copilot loses an average of $20 per user per month
#27The moment I can train Copilot on my codebase and improve its predictions, I’d gladly pay more than $20. Even now, I find that I can’t live without it simply as a slightly more intelligent auto-complete.
Re: GitHub Copilot loses an average of $20 per user per month
#28The moment I can train Copilot on my codebase and improve its predictions, I’d gladly pay more than $20. Even now, I find that I can’t live without it simply as a slightly more intelligent auto-complete.
Re: GitHub Copilot loses an average of $20 per user per month
#29The moment I can train Copilot on my codebase and improve its predictions, I’d gladly pay more than $20. Even now, I find that I can’t live without it simply as a slightly more intelligent auto-complete.
I've used some prototypes that tried it. One of the things it does it regurgitate the old patterns you want to stop using and not the new stuff you do. Basically it's a copy pasta tech debt generator.
Re: GitHub Copilot loses an average of $20 per user per month
#30The article says Microsoft wants computers to one day have Neural Processing Units (NPU) like most have GPUs. If that led to faster suggestions for copilot, I'd be all for it. Right now, a GPU is an NPU. Does anyone how an NPU would differ from a graphics card?
GPUs are a SIMD architecture designed for 4x4 float32 matrix multiplications found in video games. But NPUs (like Googles NPU) are systolic arrays designed for 16x16 or even 256x256 float16 or even int8 matrix multiplications instead. ------- NVidia builds larger matrix multiplications out of the 4x4 float16 base that a SM is designed for. After all, a 8x8 matrix multiplication is just four of the smaller 4x4 matrix…
Isn't it 8 of them? That's why you can't go the other way around and use your 8x8 multiplier to efficiently multiply 4x4s in parallel.