diff --git a/assets/ai-optimizer/CRON_JOB_DRAFT.md b/assets/ai-optimizer/CRON_JOB_DRAFT.md
deleted file mode 100644
index 3ff0781..0000000
--- a/assets/ai-optimizer/CRON_JOB_DRAFT.md
+++ /dev/null
@@ -1,283 +0,0 @@
-# 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/CRON_EXECUTION_PROMPT.md b/assets/ai-optimizer/README.md
similarity index 56%
rename from assets/ai-optimizer/CRON_EXECUTION_PROMPT.md
rename to assets/ai-optimizer/README.md
index 20a6600..b7b2461 100644
--- a/assets/ai-optimizer/CRON_EXECUTION_PROMPT.md
+++ b/assets/ai-optimizer/README.md
@@ -1,12 +1,8 @@
-# AI Model Optimization Cron Job - EXECUTION PROMPT
+# AI Model Optimization Cron Job
 
-**When this cron runs, follow these instructions exactly:**
+**Purpose:** Automatically find optimal ollama/llama.cpp configurations for maximum context size and hardware utilization.
 
----
-
-## 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.
+**Schedule:** Every hour
 
 **Hardware:**
 - 2× AMD MI50 GPUs (32GB VRAM each, 64GB total)
@@ -18,9 +14,9 @@ You are an AI model optimization agent. Your task is to find the best ollama/lla
 ## File Locations
 
 ```
-STATE: /opt/data/infra/assets/ai-optimizer/state.json
+STATE:   /opt/data/infra/assets/ai-optimizer/state.json
 RESULTS: /opt/data/infra/assets/ai-optimizer/results.csv
-INFRA_REPO: /opt/data/infra
+REPO:    /opt/data/infra (persistent - do not reclone)
 ```
 
 ---
@@ -46,6 +42,22 @@ INFRA_REPO: /opt/data/infra
 
 ---
 
+## Optimization Strategy
+
+### GPU Track (Coding)
+- Start: num_ctx=32768, num_gpu=99, flash_attn=true
+- Increase context until OOM or tokens/sec < 5
+- Record best config before hitting wall
+- Target: >10 tokens/sec with max context
+
+### RAM Track (Knowledge)
+- Start: num_ctx=65536, num_gpu=50, flash_attn=true
+- Allow heavy RAM offload (up to 100GB system RAM)
+- Increase context until OOM
+- Speed secondary to context size
+
+---
+
 ## Each Run - Step by Step
 
 ### 1. Read State
@@ -56,9 +68,9 @@ 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`
+- Get `current_model` from queue at `model_index`
+- Get `current_config` for parameters to test
+- Select next context step from `context_steps`
 
 ### 3. Pull Model (if needed)
 ```bash
@@ -86,11 +98,10 @@ docker exec ollama ollama create test-model -f /root/.ollama/test_${model}.model
 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
+# Knowledge prompt
+docker exec ollama ollama run test-model "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."
 ```
 
 ### 6. Measure VRAM (if possible)
@@ -99,33 +110,25 @@ END=$(date +%s%N)
 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"
+echo "VRAM 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
+- Update `best_configs` if improved
 
 ### 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
@@ -133,71 +136,79 @@ else:
 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
+git push
 ```
 
 ### 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
+    # Send notification
     pass
 # Else: silent
 ```
 
 ---
 
+## State File Structure
+
+```json
+{
+  "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"
+}
+```
+
+---
+
+## Results CSV Format
+
+```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
+```
+
+---
+
 ## 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)
+3. No progress for 3 consecutive runs
 
 ---
 
 ## Error Handling
 
 If any step fails:
-1. Log error to state.json: `"error": {"message": "...", "timestamp": "..."}`
+1. Log error: `"error": {"message": "...", "timestamp": "..."}`
 2. Do NOT increment model_index (retry next run)
 3. Commit state with error field
 4. Exit gracefully
 
 ---
 
-## Important Notes
+## Notes
 
-- **No num_parallel**: Do not use this parameter
+- **No num_parallel**: Removed to avoid limiting other settings
 - **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}
-    }
-  }
-}
-```
+- **Backend**: Start with ollama, llama.cpp optional
+- **Host access**: Use docker exec or SSH for rocm-smi
+- **Ask before deploy**: Show diff before `nh os switch`