diff --git a/assets/ai-optimizer/CRON_EXECUTION_PROMPT.md b/assets/ai-optimizer/CRON_EXECUTION_PROMPT.md
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+# AI Model Optimization Cron Job - EXECUTION PROMPT
+
+**When this cron runs, follow these instructions exactly:**
+
+---
+
+## Your Role
+
+You are an AI model optimization agent. Your task is to find the best ollama/llama.cpp configuration for maximum context size and hardware utilization.
+
+**Hardware:**
+- 2× AMD MI50 GPUs (32GB VRAM each, 64GB total)
+- 128GB system RAM
+- ROCm: HSA_OVERRIDE_GFX_VERSION=9.0.6, HIP_VISIBLE_DEVICES=0,1
+
+---
+
+## File Locations
+
+```
+STATE: /opt/data/infra/assets/ai-optimizer/state.json
+RESULTS: /opt/data/infra/assets/ai-optimizer/results.csv
+INFRA_REPO: /opt/data/infra
+```
+
+---
+
+## Model Queues
+
+### GPU Track (Coding - prioritize speed + context on GPU)
+1. `devstral-small-2:24b`
+2. `qwen2.5-coder:32b`
+3. `codellama:34b-instruct`
+
+### RAM Track (Knowledge - prioritize max context)
+1. `qwen2.5:72b`
+2. `nemotron-3-nano:30b`
+3. `mixtral:8x7b-instruct`
+
+---
+
+## Context Steps (in order)
+```
+[32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680]
+```
+
+---
+
+## Each Run - Step by Step
+
+### 1. Read State
+```bash
+cd /opt/data/infra
+cat assets/ai-optimizer/state.json
+```
+
+### 2. Determine Next Test
+- Read `track` (gpu or ram)
+- Read `current_model` from queue at `model_index`
+- Read `current_config` for parameters to test
+- Select next context step from `context_steps` based on `phase`
+
+### 3. Pull Model (if needed)
+```bash
+docker exec ollama ollama list | grep -q "<model>" || docker exec ollama ollama pull <model>
+```
+
+### 4. Create Test Modelfile
+```bash
+docker exec ollama bash -c "cat <<EOF > /root/.ollama/test_${model}.modelfile
+FROM ${model}
+PARAMETER num_ctx ${current_config.num_ctx}
+PARAMETER num_gpu ${current_config.num_gpu}
+PARAMETER flash_attn ${current_config.flash_attn}
+PARAMETER num_predict 4096
+PARAMETER num_keep 1024
+PARAMETER repeat_penalty 1.1
+EOF"
+
+docker exec ollama ollama create test-model -f /root/.ollama/test_${model}.modelfile
+```
+
+### 5. Run Benchmark
+```bash
+# Warm up
+docker exec ollama ollama run test-model "Hello" > /dev/null
+
+# Coding prompt
+START=$(date +%s%N)
+docker exec ollama ollama run test-model "Write a Python async context manager that retries a function with exponential backoff, max 5 retries, and logs each attempt using structlog. Include type hints."
+END=$(date +%s%N)
+
+# Calculate tokens/sec from output
+```
+
+### 6. Measure VRAM (if possible)
+```bash
+# Try host first
+rocm-smi --showmeminfo vram 2>/dev/null || \
+# Try via docker
+docker exec --privileged ollama rocm-smi --showmeminfo vram 2>/dev/null || \
+# Fallback
+echo "VRAM measurement unavailable"
+```
+
+### 7. Record Results
+- Parse tokens/sec from ollama output
+- Record VRAM/RAM usage
+- Determine if this is best config so far for this model
+- Update `best_configs` if tokens/sec improved or context increased
+
+### 8. Update State
+```python
+# Logic:
+if test_successful:
+    if context_step < max_reached:
+        phase = "context_scaling"
+        current_config.num_ctx = next_context_step
+    else:
+        # Move to next model
+        model_index += 1
+        phase = "context_scaling"
+        current_config.num_ctx = context_steps[0]
+else:
+    # OOM or error - record last good as best
+    best_configs[track][current_model] = last_good_config
+    model_index += 1
+    phase = "context_scaling"
+```
+
+### 9. Commit to Repo
+```bash
+cd /opt/data/infra
+git add assets/ai-optimizer/
+git commit -m "ai-optimizer: tested ${model} at ${num_ctx} ctx - ${status}"
+git push origin master
+```
+
+### 10. Matrix Notification (if available)
+```python
+import os
+if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
+    # Send notification to Matrix room
+    # Room ID from env or config
+    pass
+# Else: silent
+```
+
+---
+
+## Stop Conditions
+
+1. All models in both queues have `best_configs` recorded
+2. Manual intervention needed (error in state.json `error` field)
+3. No progress for 3 consecutive runs (stuck)
+
+---
+
+## Error Handling
+
+If any step fails:
+1. Log error to state.json: `"error": {"message": "...", "timestamp": "..."}`
+2. Do NOT increment model_index (retry next run)
+3. Commit state with error field
+4. Exit gracefully
+
+---
+
+## Important Notes
+
+- **No num_parallel**: Do not use this parameter
+- **Two tracks**: Complete GPU track first, then RAM track
+- **Backend**: Start with ollama, llama.cpp testing is optional (requires uncommenting in compose.yml)
+- **Host access**: Some commands need host - use docker exec or SSH if available
+- **Ask before deploy**: If config changes needed in NixOS modules, show diff and wait for user confirmation before `nh os switch`
+
+---
+
+## Example State Transitions
+
+**Start:**
+```json
+{"track": "gpu", "model_index": 0, "current_model": "devstral-small-2:24b", "current_config": {"num_ctx": 32768, ...}}
+```
+
+**After successful test at 32k:**
+```json
+{"track": "gpu", "model_index": 0, "current_model": "devstral-small-2:24b", "current_config": {"num_ctx": 65536, ...}}
+```
+
+**After OOM at 131k:**
+```json
+{
+  "track": "gpu",
+  "model_index": 1,
+  "current_model": "qwen2.5-coder:32b",
+  "best_configs": {
+    "gpu": {
+      "devstral-small-2:24b": {"num_ctx": 98304, "num_gpu": 99, "tokens_per_sec": 11.2}
+    }
+  }
+}
+```
diff --git a/assets/ai-optimizer/CRON_JOB_DRAFT.md b/assets/ai-optimizer/CRON_JOB_DRAFT.md
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+# AI Model Optimization Cron Job
+
+**Goal:** Find optimal configurations for maximum context size with full hardware utilization.
+
+**Hardware:**
+- 2× AMD MI50 GPUs (32GB VRAM each, 64GB total)
+- 128GB system RAM
+- ROCm: HSA_OVERRIDE_GFX_VERSION=9.0.6, HIP_VISIBLE_DEVICES=0,1
+
+---
+
+## Model Queue
+
+### GPU-Optimized (Coding - prioritize speed + context on GPU)
+1. `devstral-small-2:24b` - Best coding model
+2. `qwen2.5-coder:32b` - Strong coder, fits on GPU+offload
+3. `codellama:34b-instruct` - Legacy but solid
+
+### RAM-Optimized (Knowledge - prioritize max context, accept slower)
+1. `qwen2.5:72b` - Best knowledge, needs heavy offload
+2. `nemotron-3-nano:30b` - Good general knowledge
+3. `mixtral:8x7b-instruct` - MoE, efficient for knowledge
+
+---
+
+## Optimization Strategy
+
+**Two separate tracks:**
+
+### Track A: GPU-Focused (Coding)
+```
+Baseline: num_ctx=32768, num_gpu=99, flash_attn=true
+Steps:
+1. Increase context: 32k → 65k → 98k → 131k → 163k
+2. At each step, verify VRAM usage < 60GB (leave headroom)
+3. If OOM: reduce num_gpu until stable, record best
+4. Measure tokens/sec - if < 5 tok/s, consider context too high
+```
+
+### Track B: RAM-Focused (Knowledge)
+```
+Baseline: num_ctx=65536, num_gpu=50, flash_attn=true
+Steps:
+1. Increase context: 65k → 131k → 200k → 262k → 327k
+2. Allow heavy RAM offload (system RAM up to 100GB)
+3. If OOM: reduce context or num_gpu
+4. Speed less critical - focus on max stable context
+```
+
+---
+
+## Backend-Specific Configs
+
+### Ollama (Modelfile parameters)
+```
+PARAMETER num_ctx <value>
+PARAMETER num_gpu <layers>
+PARAMETER flash_attn true/false
+PARAMETER num_predict 4096
+PARAMETER num_keep 1024
+PARAMETER repeat_penalty 1.1
+```
+
+### Llama.cpp (CLI flags)
+```
+--ctx-size <value>
+--n-gpu-layers <layers>
+--flash-attn on/off
+--n-predict 4096
+--batch-size 4096
+--ubatch-size 512
+--cache-type-k f16
+--cache-type-v f16
+--split-mode layer
+--no-mmap
+```
+
+---
+
+## Host Test Instructions
+
+**The cron runs inside the hermes container. Some tests require host access:**
+
+### 1. VRAM Monitoring (HOST)
+```bash
+# Run on host to check VRAM usage during/after benchmark
+sudo rocm-smi --showmeminfo vram
+
+# Or via docker exec if rocm-smi available in container
+docker exec --privileged ollama rocm-smi --showmeminfo vram
+```
+
+### 2. Running Ollama Benchmarks (CONTAINER)
+```bash
+# Pull model
+docker exec ollama ollama pull <model>
+
+# Create custom modelfile
+docker exec ollama bash -c 'cat <<EOF > /root/.ollama/test.modelfile
+FROM <model>
+PARAMETER num_ctx 65536
+PARAMETER num_gpu 99
+PARAMETER flash_attn true
+EOF'
+
+# Create model from modelfile
+docker exec ollama ollama create test-model -f /root/.ollama/test.modelfile
+
+# Run benchmark (warm model first)
+docker exec ollama ollama run test-model "Write a Python async context manager with exponential backoff"
+
+# Cleanup
+docker exec ollama ollama rm test-model
+```
+
+### 3. Running Llama.cpp Benchmarks (CONTAINER - needs llama.cpp container)
+```bash
+# Uncomment llama_cpp_devstral in compose.yml first
+# Then rebuild: sudo nh os switch --flake .#lazyworkhorse
+
+# Test via HTTP API
+curl http://localhost:8300/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "devstral-2-small-llama_cpp",
+    "prompt": "Write a Python function",
+    "max_tokens": 100
+  }'
+```
+
+### 4. Deploying Changes (HOST via ai-worker)
+```bash
+# After optimization, commit results
+cd /home/ai-worker/infra
+git add assets/ai-optimizer/
+git commit -m "ai-optimizer: new best config for <model>"
+git push
+
+# If config changes needed in ollama_init_custom_models.nix:
+# 1. Edit the file
+# 2. nixpkgs-fmt .
+# 3. Show diff to user
+# 4. Wait for confirmation
+# 5. sudo nh os switch --flake .#lazyworkhorse
+```
+
+### 5. Accessing Host from Hermes Container
+```bash
+# SSH to host as ai-worker (key should be mounted)
+ssh -i /path/to/key ai-worker@host.docker.internal
+
+# Or via docker socket if mounted
+# (not recommended for security)
+```
+
+---
+
+## Benchmark Prompts
+
+### Coding (Track A)
+```
+"Write a Python async context manager that retries a function with exponential backoff, max 5 retries, and logs each attempt using structlog. Include type hints and error handling."
+```
+
+### Knowledge (Track B)
+```
+"Explain the complete memory hierarchy in modern GPUs, from registers through L1/L2 caches to VRAM, and how data moves between them during matrix multiplication. Include bandwidth considerations for each level."
+```
+
+### Measurement
+- Tokens per second (generation speed)
+- Time to first token (latency)
+- VRAM usage (via rocm-smi)
+- System RAM usage (via free -h)
+- Context success (did it complete without OOM?)
+
+---
+
+## State File Structure
+
+`/opt/data/infra/assets/ai-optimizer/state.json`
+
+```json
+{
+  "track": "gpu",
+  "current_model": "devstral-small-2:24b",
+  "model_index": 0,
+  "phase": "context_scaling",
+  "backend": "ollama",
+  "current_config": {
+    "num_ctx": 65536,
+    "num_gpu": 99,
+    "flash_attn": true
+  },
+  "best_configs": {
+    "gpu": {
+      "devstral-small-2:24b": {
+        "backend": "ollama",
+        "num_ctx": 131072,
+        "num_gpu": 99,
+        "flash_attn": true,
+        "tokens_per_sec": 12.5,
+        "vram_used_gb": 58.2,
+        "tested_at": "2026-04-28T17:00:00Z"
+      }
+    },
+    "ram": {}
+  },
+  "completed_models": [],
+  "gpu_queue": ["devstral-small-2:24b", "qwen2.5-coder:32b", "codellama:34b-instruct"],
+  "ram_queue": ["qwen2.5:72b", "nemotron-3-nano:30b", "mixtral:8x7b-instruct"]
+}
+```
+
+---
+
+## Results CSV
+
+`/opt/data/infra/assets/ai-optimizer/results.csv`
+
+```csv
+timestamp,track,model,backend,phase,num_ctx,num_gpu,flash_attn,tokens_per_sec,vram_gb,ram_gb,status,is_best
+2026-04-28T17:00:00Z,gpu,devstral-small-2:24b,ollama,context_scaling,65536,99,true,15.2,52.1,18.4,success,false
+```
+
+---
+
+## Cron Job Flow
+
+```
+1. Read state.json
+2. If both queues empty → STOP (all models tested)
+3. Select next model from current track queue
+4. Pull model if needed (docker exec ollama ollama pull)
+5. Create Modelfile / llama.cpp config with current test params
+6. Run benchmark (both prompts)
+7. Measure: tokens/sec, VRAM (rocm-smi), RAM (free -h)
+8. If successful:
+   - Increase context (next step)
+   - Update current_config in state
+9. If OOM/error:
+   - Record last good config as best_configs[track][model]
+   - Move to next model in queue
+10. Update state.json
+11. Append to results.csv
+12. Git commit + push to /opt/data/infra
+13. Send Matrix notification if available, else silent
+```
+
+---
+
+## Matrix Notification (Optional)
+
+```python
+# If matrix credentials available in environment
+if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
+    # Send completion notification
+    # Room: !ai-optimizer:lazyworkhorse.net (or similar)
+    pass
+# Else: silent, just commit
+```
+
+---
+
+## Files to Create
+
+```
+/opt/data/infra/assets/ai-optimizer/
+├── state.json           # Current progress
+├── results.csv          # All test results
+├── best_configs.json    # Final best configs (human-readable)
+└── CRON_JOB_DRAFT.md    # This file
+```
+
+---
+
+## Notes
+
+- **No num_parallel**: Removed to avoid limiting other settings
+- **Two tracks**: GPU (coding/speed) vs RAM (knowledge/context)
+- **Both backends**: Test ollama first, then llama.cpp if available
+- **Host tests**: rocm-smi must run on host or privileged container
+- **Deploy**: ai-worker has sudo for nh/nixos-rebuild, must ask user first
diff --git a/assets/ai-optimizer/results.csv b/assets/ai-optimizer/results.csv
new file mode 100644
index 0000000..7e25194
--- /dev/null
+++ b/assets/ai-optimizer/results.csv
@@ -0,0 +1 @@
+timestamp,track,model,backend,phase,num_ctx,num_gpu,flash_attn,tokens_per_sec,vram_gb,ram_gb,status,is_best
diff --git a/assets/ai-optimizer/state.json b/assets/ai-optimizer/state.json
new file mode 100644
index 0000000..fff69f9
--- /dev/null
+++ b/assets/ai-optimizer/state.json
@@ -0,0 +1,21 @@
+{
+  "track": "gpu",
+  "current_model": "devstral-small-2:24b",
+  "model_index": 0,
+  "phase": "context_scaling",
+  "backend": "ollama",
+  "current_config": {
+    "num_ctx": 32768,
+    "num_gpu": 99,
+    "flash_attn": true
+  },
+  "best_configs": {
+    "gpu": {},
+    "ram": {}
+  },
+  "completed_models": [],
+  "gpu_queue": ["devstral-small-2:24b", "qwen2.5-coder:32b", "codellama:34b-instruct"],
+  "ram_queue": ["qwen2.5:72b", "nemotron-3-nano:30b", "mixtral:8x7b-instruct"],
+  "context_steps": [32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680],
+  "last_updated": "2026-04-28T17:00:00Z"
+}