2026-02-27T15-23-31_auto_memory/memories.db-wal, memory/2026-02-27-phase-1-

memory/2026-02-27-phase-1-predictor-implementation-review.md

@@ -0,0 +1,29 @@
+# 2026-02-27 Session Notes
+
+## Phase 1 Predictor Implementation Review
+
+Nicholai completed and presented Phase 1 of the predictive memory scorer—a Rust crate scaffold implementing a complete training and inference pipeline for real-time memory preference scoring. He requested code review of the newly added packages/predictor/ crate.
+
+## Implementation Summary
+
+The Phase 1 deliverable includes:
+
+- **Autograd engine** (autograd.rs): Operation-level automatic differentiation with Rng, Param, Tape abstractions and custom operations (Sigmoid, MeanPool, FeatureConcat, ListwiseLoss) plus comprehensive forward/backward implementations and unit tests.
+
+- **Scorer core** (model.rs): Cross-attention-style ScorerConfig and CrossAttentionScorer implementing candidate scoring with softmax ranking for preference prediction.
+
+- **Protocol layer** (protocol.rs): JSON-RPC protocol types defining the service contract.
+
+- **Tokenization** (tokenizer.rs): HashTrick tokenizer for feature hashing.
+
+- **Training infrastructure** (training.rs): Scaffolding including Adam optimizer.
+
+- **Data pipeline** (data.rs): Placeholder reader for training data.
+
+- **Persistence** (checkpoint.rs): Save/load/apply for model checkpoints.
+
+- **Service entry point** (main.rs): JSON-line stdin/stdout service with status, score, and train handlers.
+
+- **Build config** (Cargo.toml): Crate manifest and dependencies.
+
+The session involved reading the implementation files to understand architecture and provide technical feedback. Build artifacts were properly ignored in .gitignore.