mbbui
Joined 3 years ago
Karma
10
Submissions
19
Member since
Apr 12
Recent submissions
Comment · 3 years ago
also there is not clear roll out plan either. The system seems to be very useful for large document. I am willing to pay but they don't let me lol
Comment · 3 years ago
Tbh, chatGPT caught the world by surprise. Especially the fact that it can do in-context learning so well is a big surprise even for experts in the field. You can see these from many interviews of experts in the fields for example https://www.therobotbrains.ai/copy-of-raluca-ada-popa. So no one really knows how close LLM to AGI. Most of ppl who has been working on AGI thinks that this is no where near. But again, if they are still processing what chatGPT is capable of, how would they know for sure.
Comment · 3 years ago
Comment · 3 years ago
Yes, it is exactly the point I want to drive. GG has been built on the foundation of being total transparent and engineer/IC drive everything. That has pros and cons. That makes them move fast when they are small and is not an incumbent. But slow them down when they have most to lose and need to make risky decision quick. Amazing how company culture can have so many nuances.
Comment · 3 years ago
Why didn’t the same thing being said about chatGPT?
Comment · 3 years ago
With the dawn of LLM, I think low-code will be mainstream again. Of course it all depends on the applications. For applications where scripting is all you need, you can pretty reliably generate code with prompts.
Comment · 3 years ago
You can also find more example playbooks here https://docs.cnext.io/data-science-tools
Comment · 3 years ago
Sorry, wrong link pls try this one https://apis.cnext.io/codegen?playbook=gqF0o1BMQqF2ujAxR1hOT...
Comment · 3 years ago
Thanks. You can click on the playbook examples in the link above. Or come here directly apis.cnext.io to start. Sorry the doc is still rough. Looking forward to your feedbacks.