Valkand 1.1 - new UCI engine (NNUE) for CCRL testing

[valkand66]

Hello,

I would like to submit Valkand 1.1 for testing in the CCRL lists.

Download (Windows, Linux, macOS; 64-bit; net built into the executable): https://github.com/valkand66/valkand-ch ... s/tag/v1.1

- Protocol: UCI. Options: Hash, Threads (1-256), Ponder, Move Overhead, Contempt (default 15 cp, repetitions only), SyzygyPath, OwnBook (small built-in book, off by default). Pondering (UCI Ponder) is supported; no MultiPV in this version.
- Binaries:
Windows x64 and Linux x64: avx2 (recommended) and popcnt versions.
Linux ARM64 (needs dotprod: Raspberry Pi 5, Graviton2+...; not Raspberry Pi 4).
macOS Apple Silicon (M1 to M6).
The neural net is embedded in the executable: no extra files are needed.
MD5 sums of every archive are in the README of the repository (https://github.com/valkand66/valkand-chess).
- Engine: written in C by Krassimir Kandjikov (Val Kand): bitboards, alpha-beta search, NNUE evaluation written from scratch
(768 features x 16 king zones -> 256 -> 16 -> 1, 8 output buckets, int8). Developed with the help of an AI assistant (Claude).
- Net: trained by me on ~830M positions labelled with Stockfish 16 (depth 14) evaluations and the Lichess evaluation database (CC0).
Positions come from my own self-play games and public Lichess games. No third-party net file is used.
- Syzygy probing via Fathom (MIT).
- Strength: I have not measured an absolute rating yet, so I prefer not to quote one; I would be glad to see what CCRL gives.
- License: Freeware, closed source (see the README in each archive). Contact: valkand66@gmail.com

Thanks!

[flok]

Hello,

I would like to submit Valkand 1.1 for testing in the CCRL lists.

Download (Windows, Linux, macOS; 64-bit; net built into the executable): https://github.com/valkand66/valkand-ch ... s/tag/v1.1

- Protocol: UCI. Options: Hash, Threads (1-256), Ponder, Move Overhead, Contempt (default 15 cp, repetitions only), SyzygyPath, OwnBook (small built-in book, off by default). Pondering (UCI Ponder) is supported; no MultiPV in this version.
- Binaries:
Windows x64 and Linux x64: avx2 (recommended) and popcnt versions.
Linux ARM64 (needs dotprod: Raspberry Pi 5, Graviton2+...; not Raspberry Pi 4).
macOS Apple Silicon (M1 to M6).
The neural net is embedded in the executable: no extra files are needed.
MD5 sums of every archive are in the README of the repository (https://github.com/valkand66/valkand-chess).
- Engine: written in C by Krassimir Kandjikov (Val Kand): bitboards, alpha-beta search, NNUE evaluation written from scratch
(768 features x 16 king zones -> 256 -> 16 -> 1, 8 output buckets, int8). Developed with the help of an AI assistant (Claude).
- Net: trained by me on ~830M positions labelled with Stockfish 16 (depth 14) evaluations and the Lichess evaluation database (CC0).
Positions come from my own self-play games and public Lichess games. No third-party net file is used.
- Syzygy probing via Fathom (MIT).
- Strength: I have not measured an absolute rating yet, so I prefer not to quote one; I would be glad to see what CCRL gives.
- License: Freeware, closed source (see the README in each archive). Contact: valkand66@gmail.com

Thanks!

Closed source, trained with stockfish, developed "with the help" of an ai.