334 karma · joined August 1, 2017
It's also worth looking at https://supabase.io/. Imo their route generation and automatic documentation is great and could be an inspiration.
I'm an indie dev who doesn't make any money from my side projects so pricing is always a factor as well. Read: I always use a free tier.
Git tar
It will undoubtedly sound naive to some, but I've been preferring the ability to "drive the car" over how to build one from scratch. Using fastai and their book/videos I've been able to go from dropping calculus and quitting a web dev bootcamp to building an ML product in about a a year. (and would really be much fast if I didn't have repetitive strain injury)
GP, keep searching for your starting point.
Drive the car, learn to change the oil and brakes as you go. And if you're super interested, mod it and then make one.
I have found this research paper[0] that describes using a machine translation with a CNN and a r n n. it seems one of the main problems that come up with is a lack of training data so to progress this further it seems like we would need a big corpus of parallel text between natural English describing what the code does in the actual source code. I only have some novice experience with training machine learning models through transfer learning with fast AI but I have found a repository of tutorials[1] that teach you how to do machine translation between two languages and it seems maybe this approach might be applicable. There is a small corpus of text[2] with python code with annotations but and the paper itself at States. It probably needs more to get a higher than a 74% accuracy.
the only thing I can really think is to scrape GitHub make a website where you can get people to crowdsource annotations for code samples. I'm not sure on how to deal with how much and how little code would be for a sampling. I eat how much of the code would you need for a good training sample versus just using isolated functions. Perhaps that something like this might be used in correspondence with GPT.
[0]https://www.groundai.com/project/machine-translation-from-na... [1]https://github.com/bentrevett/pytorch-seq2seq [2]https://ahclab.naist.jp/pseudogen/
I'd be interested in code running on events but would like to know how many different event types are these headsets are capable of handling.