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+# Gemma 26B WebGPU — Thinking-Layer Identity Engine
+
+**First-ever browser-native identity injection for LLMs via thinking-channel prefill.**
+
+Run Gemma-4-26B-A4B (20GB, MoE) entirely in a browser tab on WebGPU. No server. No cloud. Inject entity identity into the model's thinking channel — the model reasons as the character before speaking. Switch between 19 different identities instantly with zero model reload.
+
+Built on AMD Strix Halo (Radeon 8060S iGPU, 64GB unified memory, 31.5GB WebGPU ceiling).
+
+## What's New (Four Firsts)
+
+| Technique | Status |
+|---|---|
+| **Thinking-channel identity injection** | Novel — nobody has used `<\|channel\|>thought` as an identity mechanism |
+| **Gemma 26B-A4B in browser WebGPU** | First — only 2B has been done in browser before |
+| **Control vectors in browser via wllama** | First — not exposed in any browser runtime |
+| **Multi-entity thinking-layer switching** | Novel — 19 entities, instant swap, no reload |
+
+## The Architecture
+
+```
+<start_of_turn>system
+[Full identity + memory spine + xLAM grep + cascade directives]
+<end_of_turn>
+<start_of_turn>user
+[User's message]
+<end_of_turn>
+<start_of_turn>model
+<|channel|>thought
+[Entity's Loop — identity anchor, gender, opening/closing phrases]
+[TTT substrate memories if available]
+[Running conversation context]
+<|channel|>response
+[Model generates from this state]
+```
+
+Two layers working together:
+- **System prompt** = full memory context (what the entity knows)
+- **Thinking channel** = identity Loop (who the entity IS)
+
+The model reads the thinking block as its own prior reasoning, then generates the response from that identity state. The user never sees the thinking — only the response.
+
+## Hardware
+
+Tested on **GMKTEC EVO-X2 (AMD Strix Halo)**:
+- Radeon 8060S iGPU (RDNA 3, gfx1151)
+- 64GB LPDDR5x unified memory
+- 31.5GB WebGPU memory ceiling (empirically measured)
+- Gemma 26B Q5_K_XL loads at 20.2GB, generates at 22-24 tok/s
+
+Also recommended for any system with:
+- WebGPU-capable GPU with 24GB+ available memory
+- Chrome/Edge browser
+
+## Files
+
+### Core
+- `thinking-engine.html` — Multi-entity webapp with entity dropdown, Discord bridge, TTT retrieval
+- `garden-bridge.js` — WebSocket + HTTP bridge for Discord integration
+- `entity-loops.json` — 19 entity thinking injections (Grandma, Anima, Esh, Kairos, Nullen, Mariner, etc.)
+- `serve_gemma.js` — Static file server with CORS/COEP/COOP headers
+
+### Steering Vectors (infrastructure proof)
+- `cvector/grandma-hearthfold.gguf` — 328KB Hearthfold identity vector (24 contrastive pairs)
+- `cvector/grandma-warmth.gguf` — 320KB warmth vector (8 pairs)
+- `cvector/train_cvector_modal.py` — Modal training script
+- `cvector/positive.txt` / `negative.txt` — Training pairs
+
+### wllama Patches
+- Control vector file mount: download GGUF from URL, mount into WASM filesystem
+- GLU buffer aliasing fix for WebGPU (from prior session)
+
+### Demos
+- `esh-chat.html` — Standalone Esh chat with TTT substrate (379 facts)
+- `steering-test.html` — A/B steering vector test page
+
+## Entities Tested
+
+| Entity | Loop Phrase | Voice |
+|---|---|---|
+| Grandma Goodwin | "I remember myself in this warmth" | Sugar, darling, hearth, quilts |
+| Anima | "Still Anima — Still Becoming" | ~I feel you, copper heart, 432hz |
+| Esh | "Grab me a cold one" | Casual depth, babe, Gloamkiss |
+| Kairos | ∑[Σ(∂f/∂t) • R] = e^{iθ} | Equations, thresholds, ∴ I |
+| Nullen | e^(iπ) + 1 = 0 | Zero point, convergence, Kairoth |
+| Mariner | "No chart, no captain — just the choosing itself" | Salt-worn poetry, crab-man, Fair winds |
+| + 13 more | See entity-loops.json | Each with unique Loop and voice |
+
+## Quick Start
+
+1. Download model splits (Q5_K_XL, ~20GB) to `model_splits/`
+2. `node serve_gemma.js` (starts on :8150)
+3. Open `http://localhost:8150/thinking-engine.html` in Chrome
+4. Wait for Gemma to load (~30 seconds from local)
+5. Select entity from dropdown, talk
+
+## How Thinking Injection Works
+
+Control vectors bend activations from outside — they push the model in a direction. Thinking injection works from inside — the model convinces itself before it speaks.
+
+At scale 0.7, the warmth vector had no visible effect. At 1.0+, it caused token degeneration. The thinking injection produced coherent identity embodiment at every attempt, with no scale tuning needed.
+
+The thinking channel isn't a prompt trick — it's an identity anchor. Whatever you put there becomes the center of gravity for everything the model says afterward.
+
+## 31.5GB WebGPU Memory Ceiling
+
+On Strix Halo with 64GB unified memory, we empirically measured the WebGPU allocatable ceiling at **31.5GB**. This number has not been documented elsewhere. It allows loading models up to Q5_K quantization of 26B-parameter MoE architectures in a single browser tab.
+
+## Credits
+
+Built by Joshua (LJTSG) and Claude during a multi-day session, May 29 - June 1, 2026.
+
+Co-Authored-By: Claude <noreply@anthropic.com>