docs: mark superseded and point at the canonical repo

README.md

@@ -1,32 +1,41 @@
----
-license: mit
-tags: [from-scratch, opcode, ternary, trit, execution-verified, tiny]
----
-
-# Binary-Trit Coder (Clay's opcode model)
-
-A **from-scratch, 3.17M-param causal transformer** trained from *random init* on Issac's own opcode
-substrate — a bijective ternary ("trit") op grammar: `join (+)`, `take (−)`, `weave (×)` over digit leaves,
-prefix-notation, depth-2. No pretraining, no outside corpus.
-
-## Honest benchmark — its OWN domain, not Python
-The metric is **execution-verify rate**: sample a program, *run it for real*, check the model's answer.
-| Test | Score |
-|---|---|
-| in-distribution (depth-2, fresh) | **98.6%** |
-| OOD generalization (depth-3, never trained) | 47.0% |
-
-**This is NOT a Python code-gen model.** It has an 18-token opcode vocab; it will score ~0 on
-LiveCodeBench/SciCode (wrong domain). Its "learn from doing" signal is the verify-rate above.
-
-## The chrysalis (capability layers, all execution-verified)
-- variables/let-bindings (69.5% end-to-end; 100% with a calculator + show-work)
-- ping-pong φ-lattice solver — recovers a hidden intermediate 100%
-- calc-offload, dark-space inference, combustion candidate-spark
-
-## Files
-`scratch_coder.pt` (weights + vocab), `config.json`. Architecture + inference in
-[github.com/issdandavis/loom](https://github.com/issdandavis/loom) `scratch_coder.py`.
-
-*Thesis: coding is finite + bijective + execution-verifiable, so a small model from random init can MASTER
-the mapping — proven here at 98.6%.*
+---
+license: mit
+tags:
+- from-scratch
+- opcode
+- ternary
+- trit
+- execution-verified
+- tiny
+- superseded
+---
+<!-- scbe-status -->
+> **Status: superseded.** Canonical coding model: [scbe-coding-agent-vtc-qwen15-v1-gguf](https://hf.co/issdandavis/scbe-coding-agent-vtc-qwen15-v1-gguf). Kept as research history.
+
+# Binary-Trit Coder (Clay's opcode model)
+
+A **from-scratch, 3.17M-param causal transformer** trained from *random init* on Issac's own opcode
+substrate — a bijective ternary ("trit") op grammar: `join (+)`, `take (−)`, `weave (×)` over digit leaves,
+prefix-notation, depth-2. No pretraining, no outside corpus.
+
+## Honest benchmark — its OWN domain, not Python
+The metric is **execution-verify rate**: sample a program, *run it for real*, check the model's answer.
+| Test | Score |
+|---|---|
+| in-distribution (depth-2, fresh) | **98.6%** |
+| OOD generalization (depth-3, never trained) | 47.0% |
+
+**This is NOT a Python code-gen model.** It has an 18-token opcode vocab; it will score ~0 on
+LiveCodeBench/SciCode (wrong domain). Its "learn from doing" signal is the verify-rate above.
+
+## The chrysalis (capability layers, all execution-verified)
+- variables/let-bindings (69.5% end-to-end; 100% with a calculator + show-work)
+- ping-pong φ-lattice solver — recovers a hidden intermediate 100%
+- calc-offload, dark-space inference, combustion candidate-spark
+
+## Files
+`scratch_coder.pt` (weights + vocab), `config.json`. Architecture + inference in
+[github.com/issdandavis/loom](https://github.com/issdandavis/loom) `scratch_coder.py`.
+
+*Thesis: coding is finite + bijective + execution-verifiable, so a small model from random init can MASTER
+the mapping — proven here at 98.6%.*