Author: Matthew Sadler

March 14, 2024 Matthew Sadler 10 comments

A little while back, I published some blog articles and some videos on Dragon’s creative 5.Qd2!? idea in the Pirc (https://matthewsadler.me.uk/openings/a-stunning-dragon-idea-against-the-pirc-5-qd2/ and https://youtu.be/DuFfJwBgMD8 to start you off!) A lively Twitter discussion followed with Ali Mortazavi (English IM) and Peter Heine Nielsen (GM and of course Magnus Carlsen’s second) that moved on to debate the relative merits of the Pirc

February 28, 2024 Matthew Sadler 4 comments

n this post, I continue my adventures with Crazy Leela’s Grob Semi-Slav! After being rather marmelised when taking the gambit pawn, I decided to refuse the offered pawn in a couple of games. Let‘s see how that went! A video of these games is available from my Silicon Road YouTube channel: https://youtu.be/EK9_iA63Nvo while engine analysis of these games is available from the usual place: http://cloudserver.chessbase.com/MTIyMTYx/replay.html

February 22, 2024 Matthew Sadler 4 comments

In a previous post, I described how to set up the new Leela feature called WDL Contempt (described here by the Leela team: The Lc0 v0.30.0 WDL rescale/contempt implementation – Leela Chess Zero (lczero.org)) In this post I look at one of the opening discoveries I made using a Leela optimised to search for promising lines against a prospective (fictitious!) opponent rated 400 ELO points below me! (A roughly 2700 ELO vs 2300 ELO scenario)

February 18, 2024 Matthew Sadler 31 comments

I was recently triggered to investigate (finally!) a new Leela feature called WDL Contempt. This feature was beautifully described in this blog article (The Lc0 v0.30.0 WDL rescale/contempt implementation – Leela Chess Zero (lczero.org)) by the Leela team, but the implementation is slightly fiddly so you need an hour of quiet time to set things up right.

February 11, 2024 Matthew Sadler 8 comments

Just a few days ago, I was pointed to a new scientific paper by Google DeepMind. It’s amazing as it’s from Google DeepMind, it’s about chess, but it’s not about AlphaZero! The Google DeepMind researchers were trying to build a “searchless” chess engine: an engine that doesn’t calculate at all but finds great moves by “understanding” the position through evaluation only.