We've put it on KGS as "somebot", and it can play at somewhere from 3-5d level, although amusingly it still can't do ladders, and almost always plays 3-3. Our strength seems to have plateaued recently, and we suspect it's because of value net overfitting issues.
3-5d is about the strength of LeelaZero (which I presume you already knew about). Can you disclose how long have Minigo been training, how many games were generated and which hardwares were used?
About a month using K80s. We have something like a million games played.
So, um, what would you have to do to it to make it play at about 25 kyu? (I'm, um, asking for a friend...)
We're working on making all the data (models, selfplay SGFs) available to download - you could download an older generation.
puts "#{rand(19)}, #{rand(19)}"
(I'm not much better :)
Ladders sound hard for neural networks. IIRC the original AlphaGo had a ladder-solver hardcoded that would determine if a ladder position is winning or losing and feed this bit into the neural network.
I'm pretty sure it didn't, though I can't find it explicitly for Alpha Go. For Zero they specifically mention that it took the self playing a long time to learn ladders well.
I can confirm that the original 2015 Nature paper for AlphaGo mentions setting ladder capture / ladder escape bits as input to the neural network.