Typed some more

Author: mooskagh

content/contribute/gsoc/2026.md

@@ -78,6 +78,59 @@ reasonable strength.
 * **Skills needed:** C++, Network programming
 * **Difficulty:** Hard / 175 hours
 
+Allow Lc0 to handle computation over the network. That allows to run local Lc0
+engine while using remote hardware (e.g., GPUs in the cloud) for neural network
+evaluation, or to share evaluation between multiple Lc0 games.
+
+## Port existing backends to the new backend API
+
+* **Skills needed:** C++
+* **Difficulty:** Easy / 90 hours
+
+Starting with Lc0 v0.32, we have a new backend API that is more flexible.
+However, most of the existing backends (CUDA, OpenCL, XLA) still use the old API
+through a compatibility wrapper. We need to port them to the new API to be able
+to use new features and optimizations.
+
+## Extend UCI protocol with JSON-based input and output information
+
+* **Skills needed:** C++
+* **Difficulty:** Easy / 175 hours
+
+The UCI protocol is the standard way for chess engines to communicate with user
+interfaces. However, it has limitations in terms of the amount and structure of
+information that can be exchanged. We want to extend the UCI protocol to support
+JSON-based messages for detailed state information, which can be useful for
+analysis and debugging.
+
+We won't use existing json libraries; instead we'll implement a JSON
+serializer/deserializer in our custom protobuf code.
+
+## Update the XLA backend to use StableHLO, through writing MLIR bytecode
+
+* **Skills needed:** C++, MLIR, XLA
+* **Difficulty:** Hard / 175 hours
+
+The XLA backend for Lc0 currently uses an older HLO graph format. We want to
+update it to use StableHLO, a MLIR-based intermediate representation for machine
+learning models. We don't want to add a full MLIR dependency to Lc0, so the task
+includes writing a custom MLIR bytecode writer for our neural network format,
+and update the XLA backend to write StableHLO.
+
+Having MLIR bytecode writer will also open possibilities for future backends and
+ways to write kernels, like Triton or cuTile.
+
+## Support for foreign neural network formats (e.g., Ceres)
+
+* **Skills needed:** C++, ONNX
+* **Difficulty:** Medium / 175 hours
+
+Lc0 currently uses its own neural network format, and supports generic ONNX as
+well. There are other engines which use different formats, like Ceres, Stockfish
+NNUE, or Monty. Supporting these formats would allow more experiments, and with
+faster nets (e.g., Stockfish NNUE) we may have a faster backend for search
+optimization.
+
 ...
 
 ## `TODO: DELETE THIS` Unexpanded list
@@ -103,4 +156,5 @@ reasonable strength.
 * Move certain parts of Lc0 to a separate repository with more relaxed license
 * Implement chess variant? (duck chess?)
 * Implement some tool (set of colab scripts) to explore Lc0 activations etc
+* dev.lczero.org
 * "Your idea here."