Codota/TabNine CEO here.
TabNine's memory requirements are derived from the inherent memory consumption of a large neural model. We are actively working on a lightweight model. You can try our Pro plan which uses GPU-accelerated cloud servers (2 weeks free trial) instead of predicting the model on your machine
Dror from TabNine here.
We've introduced some big improvements in TabNine's resource consumption in the last few weeks, and there's more to come. In addition, TabNine's Professional version (free trial available) lets you use our GPU cloud servers which means dramatically reduced load on your machine.
(Dror from TabNine here)
If you're lucky enough to have bought the perpetual license for TabNine back in 2018, you don't have to purchase a subscription as all perpetual licenses are honored! Just reach for [email protected] if you need help.
IMO, The question whether the scientist gets to keep his PhD is secondary.
The more important question is how do we make sure that we base our research on sound results.
Agree. I also prefer high level operations that are ought to be optimized by the runtime. Python list comprehensions let you do things that are closer to the "mathematical sum notation" without you having to specify the implementation details. I am not sure how optimized it really is though :)