/approve 5a489096 allow-once

/approve a7259eaa allow-once

/approve a7259eaa allow-once

/approve a7259eaa allow-once

/approve <腳本ID> allow-always

/approve ca7db285 allow-always

/approve allow-always

/approve fdf217a3 allow-always

/approve allow-once

/approve 2a02d877 allow-always

/approve 96d0d319 allow-always

/approve <ID> allow-always

sudo apt update
sudo apt install -y open-vm-tools

ls /mnt/hgfs/

vmware-hgfsclient

cd C:\xampp\htdocs\cs_ai\
Compress-Archive -Path "*.py","*.json","ai_models\*.py","logs\*.txt" -DestinationPath "C:\Users\你的使用者名稱\Desktop\cs_ai_for_apple.zip"

/approve 4a1589e9 allow-once

/approve c8f28052 allow-once
/approve 13a0399a allow-once

/approve 3e4faa9b allow-once
/approve 39987ee9 allow-once
/approve 01a14f98 allow-once

/approve 1c85c177 allow-once
/approve 60efd9c0 allow-once

/approve acd79368 allow-once
/approve 354096e8 allow-once

/approve ad35d909 allow-once

/approve d32e9a23 allow-once

/approve 52afbca7 allow-once

/approve 9e05c85d allow-once

/approve b0154995 allow-once

/approve 69f857a9 allow-once

/approve 8aab9ae4 allow-once

/approve aa662446 allow-always

/approve e1b3155f allow-always

/approve aaf9dcc1 allow-once

/approve ed0a9dcb allow-once

/approve e76e2e71 allow-once

sudo ufw enable
sudo systemctl enable ufw
sudo ufw status verbose

/approve 36112625 allow-once

/approve c560d701 allow-once

/approve 79782ab4 allow-once

sudo ufw enable
sudo systemctl enable ufw
sudo ufw status verbose

狀態：啟用
日誌：on (low)
Default: deny (incoming), allow (outgoing), disabled (routed)

允許規則：
- 22/tcp    ALLOW IN  192.168.177.0/24  (SSH)
- 3389/tcp  ALLOW IN  192.168.177.0/24  (RDP)

狀態：啟用
Default: deny (incoming), allow (outgoing)
允許 22/tcp 從 192.168.177.0/24
允許 3389/tcp 從 192.168.177.0/24

/approve ee38c834 allow-once

| Issue               | Why it slows down                                                | Fix                                                                                                                                                                    |
| ------------------- | ---------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Huge context window | The gpt-oss:lasted model keeps the whole conversation in memory. | Trim the prompt to only the last 2‑3 turns, or use a “history window” of 200‑400 tokens.                                                                               |
| CPU‑only inference  | Ollama uses the CPU by default unless you enable GPU.            | 1. Enable GPU (if you have a compatible GPU) – add --device cuda or set CUDA_VISIBLE_DEVICES.  <br>2. Use a lighter model – e.g. ollama/qwen-1.5b or ollama/llama2-7b. |
| Model size          | gpt-oss:lasted is a large, research‑grade model.                 | Switch to a smaller, production‑ready model: <br>bash\nollama pull ollama/qwen-1.5b\n <br>or <br>bash\nollama pull ollama/llama2-7b\n                                  |
| Cache / batch size  | Large batch size increases memory usage.                         | Reduce --batch to 1 or 2.                                                                                                                                              |
| Token budget        | Large answers cause long generation time.                        | Set a lower maxTokens (e.g., 512–1024) for everyday tasks.                                                                                                             |

# 1️⃣ Pull a lighter model (if not already pulled)
ollama pull ollama/qwen-1.5b

# 2️⃣ Set it as the default (update your config or CLI call)
# Example using a single request
ollama run ollama/qwen-1.5b \
  --prompt "Your quick question here" \
  --max-tokens 512

# 3️⃣ (Optional) Enable GPU acceleration
# If you have a CUDA GPU:
ollama run ollama/qwen-1.5b \
  --prompt "Your quick question" \
  --max-tokens 512 \
  --device cuda

Tip: If you’re using the OpenClaw CLI, set the model in your config.json or via the --model flag:

openclaw run --model ollama/qwen-1.5b

| Lighter model    | Expected latency                        | Model size       |
| ---------------- | --------------------------------------- | ---------------- |
| ollama/qwen-1.5b | ~200 ms on a modern CPU (≈3 B tokens/s) | 1.5 B parameters |
| ollama/llama2-7b | ~300 ms on a modern CPU (≈2 B tokens/s) | 7 B parameters   |

/approve fe8575ca allow-once

# 1️⃣ 暫停所有 GPU 進程
nvidia-smi --gpu-reset  # 會把 GPU 進程重置，類似停機

# 2️⃣ （可選）卸載驅動
sudo rmmod nvidia
# … 等待數秒 …

# 3️⃣ 重新載入驅動
sudo modprobe nvidia

⚠️ nvidia-smi --gpu-reset 可能導致未保存的工作遺失，請在重置前確保所有任務已完成。

sudo apt update
sudo apt install nvidia-driver-470  # 或最新可用版本

nvidia-smi

ollama run ollama/qwen-1.5b --device cuda  # cuda 對應 GPU

openclaw run --model ollama/qwen-1.5b --device cuda

screen -dmS ollama-gpu ollama run ollama/qwen-1.5b --device cuda

[Unit]
Description=Ollama GPU Service
After=network.target

[Service]
ExecStart=/usr/bin/ollama run ollama/qwen-1.5b --device cuda
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

sudo systemctl enable --now ollama-gpu.service

ollama run ollama/qwen-1.5b --device cuda

openclaw run --model ollama/qwen-1.5b --device cuda

注意：

• 確保 GPU 驅動與 CUDA 版本相容。
• 若你使用的是 Ubuntu 22.04 或 24.04，推薦使用官方的 nvidia-driver-525 或更新版本。
• 在重啟後，請先檢查 nvidia-smi 是否顯示正常，確定 GPU 可用。

{
  "asset": {
    "generator": "OpenClaw Simple Apple Generator",
    "version": "2.0"
  },
  "scene": 0,
  "scenes": [
    {
      "nodes": [0]
    }
  ],
  "nodes": [
    {
      "mesh": 0,
      "translation": [0, 0, 0]
    }
  ],
  "meshes": [
    {
      "primitives": [
        {
          "attributes": {
            "POSITION": 1
          },
          "indices": 2,
          "material": 0
        }
      ]
    }
  ],
  "materials": [
    {
      "pbrMetallicRoughness": {
        "baseColorFactor": [1.0, 0.2, 0.2, 1.0],  // bright red
        "metallicFactor": 0.0,
        "roughnessFactor": 0.6
      },
      "name": "AppleColor"
    }
  ],
  "accessors": [
    {
      "bufferView": 0,
      "componentType": 5126,
      "count": 642,
      "type": "VEC3",
      "max": [0.5, 0.5, 0.5],
      "min": [-0.5, -0.5, -0.5]
    },
    {
      "bufferView": 1,
      "componentType": 5123,
      "count": 1288,
      "type": "SCALAR",
      "indices": true
    }
  ],
  "bufferViews": [
    {
      "buffer": 0,
      "byteOffset": 0,
      "byteLength": 642 * 12,
      "target": 34962
    },
    {
      "buffer": 0,
      "byteOffset": 642 * 12,
      "byteLength": 1288 * 2,
      "target": 34963
    }
  ],
  "buffers": [
    {
      "byteLength": 642 * 12 + 1288 * 2
    }
  ]
}

<script src="https://cdn.jsdelivr.net/npm/three@0.158.0/build/three.min.js"></script>
<script>
  const scene = new THREE.Scene();
  const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 10);
  camera.position.z = 2;

  const renderer = new THREE.WebGLRenderer({ antialias: true });
  renderer.setSize(window.innerWidth, window.innerHeight);
  document.body.appendChild(renderer.domElement);

  const loader = new THREE.GLTFLoader();
  loader.load('sweet_apple.gltf', gltf => {
    scene.add(gltf.scene);
    animate();
  });

  function animate() {
    requestAnimationFrame(animate);
    renderer.render(scene, camera);
  }
</script>

# Every day 8:00 AM Taipei time (Asia/Taipei)
0 8 * * * openclaw gateway run --model ollama/qwen-1.5b --text "Generate daily industry brief" >> /home/cssp/.openclaw/workspace/daily_news.log 2>&1

| Step                   | What it does                                           | How I’ll help                                                                                                                             |
| ---------------------- | ------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- |
| 1. Set up the scene    | Arrange lights, camera angles, and assets.             | I can generate a 3‑D scene file (GLTF/OBJ) with pre‑configured lighting and camera rigs.                                                  |
| 2. Capture frames      | Render a sequence of frames (e.g., 30 fps).            | Using a head‑less renderer (like Blender’s command line, Three.js‑node, or a lightweight GPU renderer), I can produce the frame sequence. |
| 3. Assemble into video | Combine frames into an MP4/H.264 file.                 | I’ll run a video‑encoding tool (ffmpeg) to stitch frames and add sound if needed.                                                         |
| 4. Add polish          | Color‑grade, add effects, and add titles or subtitles. | I can apply simple color‑grading or overlay text using ffmpeg filters.                                                                    |
| 5. Deliver             | Upload to your server or share a download link.        | I’ll handle the upload to your /uploads/ path or generate a signed URL if you use cloud storage.                                          |