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VSCode Intellisense also uses a deep learning approach for autocompletion.

How does this compare?

As far as I can tell, it suggests one token at a time and uses its model to help rank these tokens. This is useful, but there is a lot to be gained by suggesting multiple tokens at once.

TabNine has always included a logistic regression model to help rank completions. It uses features such as the occurrence frequency of the token and the number of similar contexts in which it occurs.

Intellisense also outputs multiple suggestions.

Also, TabNine mentions they are using transformers, which is not logistic regression. The context will be inferred using attention.

Sorry, I worded that poorly. What I mean is that each individual suggestion consists of a single token -- at least, that is what I see in https://visualstudio.microsoft.com/services/intellicode/. Compare the videos in https://tabnine.com/blog/deep, where most suggestions consist of multiple tokens.

Deep TabNine (announced today) uses transformers. TabNine (released last year) uses logistic regression.

Interesting, thanks.

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