Here is Kite’s farewell for you to read.

Kite did open-source many parts of their technology/software stack, though I didn’t check how comprehensive those parts are, and if that is anywhere near enough to fork/continue their work.
I wonder if there already exists an open-source project focusing on ML-based code completion for e.g. Python – let me know in the comments if you know one!

Kite cites two reasons for a shutdown: 1) technology not being quite there yet, and 2) failure to monetize.
Kite had up to 500k daily developers using the platform, but apparently extremely few were willing to pay for it.
If you do look at current ML code assistants, there seems to always exist at least some free tier – I wonder if that is forced by the same lackluster, non-paying developers attitude as for Kite.

Kite’s farewell had another interesting number: 18%.
That is by how much individual developer’s productivity could increase thanks to Kite’s assistance.
This isn’t bad at all; for a team of 5 largely independent developers, it’s almost one extra “affordable” developer.
Kite was striving to achieve a “10x improvement”, but at least to me the 18% improvement sounds good enough for sales.

I’m very curious to try some of these assistants out.
I can imagine them to be very helpful for relatively experienced developers when starting to work with a new library/ecosystem – for example, OpenVision Python bindings.
Even the common autocomplete can significantly simplify “onboarding” to a new library – and a more intelligent autocomplete should be able to help with boilerplate code (that you usually don’t have when you begin), as well as with some idiomatic expressions and statements.

Have you already played with some of the smarter code assistants?
What was your experience?
Please share

Just came across the Akaike’s Infor­ma­tion Criterion (AIC) and Schwarz Bayesian Infor­ma­tion Criterion (BIC). Citing robjhyndman,

Asymp­tot­i­cally, min­i­miz­ing the AIC is equiv­a­lent to min­i­miz­ing the CV value. This is true for any model (Stone 1977), not just lin­ear mod­els. It is this prop­erty that makes the AIC so use­ful in model selec­tion when the pur­pose is prediction.
…
Because of the heav­ier penalty, the model cho­sen by BIC is either the same as that cho­sen by AIC, or one with fewer terms. Asymp­tot­i­cally, for lin­ear mod­els min­i­miz­ing BIC is equiv­a­lent to leave–v–out cross-​​validation when v = n[1-1/(log(n)-1)] (Shao 1997).

Want to try AIC and maybe BIC on my models. Conveniently, both functions exist in R.