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LP: Extend OpenClaw for a Local-First AI Assistant - #3504

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pareenaverma merged 17 commits into
ArmDeveloperEcosystem:mainfrom
odincodeshen:feature/openclaw_arm_continuum
Jul 28, 2026
Merged

pareenaverma merged 17 commits into
ArmDeveloperEcosystem:mainfrom
odincodeshen:feature/openclaw_arm_continuum

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Before submitting a pull request for a new Learning Path, please review Create a Learning Path

  • [ x ] I have reviewed Create a Learning Path

Please do not include any confidential information in your contribution. This includes confidential microarchitecture details and unannounced product information.

  • [ x ] I have checked my contribution for confidential information

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Added draft status and cascade settings to the OpenClaw index.
@pareenaverma
pareenaverma merged commit 6c24835 into ArmDeveloperEcosystem:main Jul 28, 2026
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```

If you do not already have a Telegram bot, follow the official [Telegram Bot tutorial](https://core.telegram.org/bots/tutorial) to create one before continuing.

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I've validated the steps below to configure telegram with the DGX Spark on my personal telegram account.


Create a Telegram account if you do not already have one.

Create a new Telegram bot
* Open Telegram on a client device, telegram is available on a variety of platforms.
* Start a chat with BotFather.
* Send:
/newbot
* Follow the prompts to create your bot.
* Once the bot is created, copy the HTTP API token provided by BotFather. You will need it in the next steps.

From your Telegram client, open a chat with the bot you just created and send it a test message, such as Hello. This creates an update that you can use to retrieve the bot’s CHAT_ID. Next, open a terminal on your local machine and run the following command, replacing TOKEN with the HTTP API token provided by BotFather:

curl https://api.telegram.org/bot<TOKEN>/getUpdates"

you should see an output like the following, copy the id value.

{
    "ok": true,
    "result": [
{
            "update_id": 78772718,
            "message": {
                "message_id": 2,
                "from": {
                    "id": 8974517049,
                    "is_bot": false,
                    "first_name": "Kie",
                    "last_name": "Hej",
                    "language_code": "en"
                },
                "chat": {
                    "id": 8974517049,
                    "first_name": "Kie",
                    "last_name": "Hej",
                    "type": "private"
                },
                "date": 1785762058,
                "text": "Hello?"
            }
        }
]
}

Use the id value and set as your-telegram-chat-id in the .env file

```

Wait until the log includes `Application startup complete`. Model download and compilation messages before this point are expected. Press `Ctrl+C` to stop following the log; this does not stop the container.

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The first time you run docker compose up, vLLM downloads the ~30 GiB Qwen model, so startup can take a significant amount of time before the runtime becomes available. The reported “1–3 minute” startup time is therefore misleading on the initial run. Should let the reader know that is can take a while for the vLLM service to be available

@jasonrandrews jasonrandrews added publish and removed ready_to_publish Ready for final review and publish labels Aug 12, 2026
@jasonrandrews jasonrandrews moved this from In Progress to Done in Arm Learning Paths Roadmap Aug 12, 2026
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7 participants