OpenAI has announced a preview release of the official ChatGPT Desktop app for Linux. Compared to using ChatGPT in a browser tab, the GUI desktop app can run Codex in your local repos, hook into and control other apps via extensions, handle ChatGPT Work-related tasks and probe your local files without you having to upload things elsewhere. In an email, OpenAI said that ‘Linux has been one of the most-requested platforms’ for its desktop app, and with this launch it’s finally available on all major operating systems. ChatGPT desktop app for Linux joins Anthropic’s Claude desktop app for Linux, which entered […]

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My agent harnessing journey started with Nanoclaw. Which is super simple to setup and use. It works for a few simpler tasks through Telegram.

But I wanted something to work on my main computer. Out of all claw like agents, I find Hermes the most suited.

So I installed Hermes on a Raspberry Pi running on Pironman 5 Pro Max. And I installed Hermes desktop on my Asus Zenbook laptop, my primary system.

The advantage is that the Raspberry Pi remains the always-on Hermes machine. I can close Hermes Desktop or shut down the laptop without needing Hermes itself to run on the laptop (for scheduled tasks). Also, Hermes agents won't have unrestricted access to my system. A safer approach, in my opinion. At least, that's the idea I am going with.

The basic architecture is:

Laptop
└── Hermes Desktop
        │
        │ Remote Gateway
        ▼
Raspberry Pi
└── hermes serve
    ├── Agents
    ├── Jobs
    ├── Memory
    └── Tools

Let me show you how you can use the Hermes agent via the remote gateway feature.

Step 1: Start the Hermes Gateway on the Raspberry Pi

Open a terminal on the Raspberry Pi or SSH into it.

You need to add the following in the ~/.hermes/.env file of hermes:

HERMES_DASHBOARD_BASIC_AUTH_USERNAME=admin
HERMES_DASHBOARD_BASIC_AUTH_PASSWORD=YOUR_STRONG_PASSWORD
HERMES_DASHBOARD_BASIC_AUTH_SECRET=YOUR_RANDOM_SECRET

The 'random secret' can be generated with.

openssl rand -base64 32

These credentials will be used from the Hermes desktop. Now start Hermes's backend with:

hermes serve --host 0.0.0.0 --port 9119

You may see a message like this:

Headless backend (hermes serve): web UI disabled — use `hermes dashboard` for the browser UI.

This is normal. hermes serve does not provide a browser interface. It starts the backend that Hermes Desktop connects to.

Keep this process running while testing the connection.

Step 2: Verify that Hermes is listening

Open another terminal tab to access the Raspberry Pi, and run:

ss -ltnp | grep 9119

You should see something containing:

0.0.0.0:9119

This means Hermes is listening for connections on port 9119.

Step 3: Install Hermes Desktop on the pc

Hermes provides an official script for installing the Hermes Desktop:

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

During installation, Hermes may ask to choose a terminal backend:

Select terminal backend:

Local
Docker
Modal
SSH
Daytona
...
Keep current (local)

For this setup, you can leave this as:

Keep current (local)

The Terminal Backend setting is separate from the Remote Gateway setting. The gateway is what connects Hermes Desktop to Hermes running on your Raspberry Pi.

I also left out the model selection. Whatever the remote Hermes server uses will be used here, too.

Step 4: Use remote gateway on Hermes

Start Hermes Desktop from the terminal:

hermes desktop

That's the way it runs for the moment. Ironical to run a desktop GUI app from the terminal.

Anyways, inside Hermes Desktop, click on the settings and go to gateway and find the remote gateway option:

In the Remote URL field, enter the address of the device running the Hermes backend with the port 9119:

http://<IP of Pi>:9119

Then save/apply the setting and reconnect.

Hermes Desktop should now connect to the Hermes backend running on your Raspberry Pi.

Hermes Desktop becomes the interface for interacting with the Hermes instance on the Raspberry Pi.

Step 5: Make the gateway permanent

If things are working fine so far, it is time to make things permanent. Because keeping this running on the remote Hermes server is not a wise move.

hermes serve --host 0.0.0.0 --port 9119

Because if I close that terminal or reboot the Raspberry Pi, the gateway will stop.

For an always-on Raspberry Pi setup, running hermes serve as a systemd service works better.

No need to SSH into the Pi and manually launch Hermes every time. It will be automatically start thanks to the systemd service.

Get the Hermes executable path with:

which hermes

And then create the systemd service:

sudo nano /etc/systemd/system/hermes-server.service

Here's the file I used. You should replace the EnnvironmentFile and ExecStart values as per your setup.

[Unit]
Description=Hermes Agent Backend
After=network-online.target
Wants=network-online.target

[Service]
Type=simple
User=pi
EnvironmentFile=/home/pi/.hermes/.env
ExecStart=/home/pi/.local/bin/hermes serve --host 0.0.0.0 --port 9119
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

Once you have saved the service file, run it in this fashion:

sudo systemctl daemon-reload
sudo systemctl enable --now hermes-server

Check the status of the newly created systemd service:

systemctl status hermes-server

Conclusion

I am yet to fully utilize Hermes on desktop with the remote gateway method. Portability could be an issue if I move out of my home network, but even in that case, there are ways to SSH into Raspberry Pi from outside network.

I'll be sharing more of my local AI exploration and experiences. Do subscribe to Local AI Weekly newsletter for that.

Running local AI models has become surprisingly accessible. Install Ollama, pull a model, and you can start chatting with it in minutes. That's exactly how I began my journey with local AI.

Using an LLM directly in terminal is okayish but it limits your ability. That's why there are so many frontends available for Ollama, providing you with a graphical, chatGPT-like interface. This way, you can easily revisit an earlier answer, copy code snippets, or have an extended discussion with a model.

Of the so many GUIs, Open WebUI is like the de facto Ollama front-end. I don't know about you but I find it difficult to set up.

I also looked at options like LibreChat, AnythingLLM, and LobeChat. They all felt geared toward users who wanted an all-in-one AI platform rather than a lightweight companion for a local Ollama UI.

This is the 'problem' that I think OrionChat solves...by its simplicity.

OrionChat: Web-based chat interface for Local LLMs

OrionChat is a free, open-source, web-based chat interface. It isn't built exclusively for Ollama. Instead, it can also talk to OpenAI, Google Gemini, Claude, Groq, Cerebras, and a few other providers.

But for this piece, I only care about one thing: how well it works as a front end for models running locally through Ollama.

It doesn't attempt to become an entire AI platform, no automation, agents, scheduled tasks. Instead, it provides a clean chat interface, model selection, and a responsive interface.

Once the models are downloaded, OrionChat simply detects them. I particularly liked this design because it doesn't introduce another layer of model management.

Installing OrionChat

Getting OrionChat running took me less than five minutes. There's no need to even run a Docker daemon.

Step 1: Clone the repository

Clone and move into the project folder.

git clone https://github.com/EliasPereirah/OrionChat.git
cd OrionChat

Step 2: Serve the files

Python makes this trivial if you already have it installed, which most Linux systems do:

python -m http.server

Step 3: Open OrionChat

Head to http://localhost:8000 in your browser. OrionChat's interface should load right away.

Letting Ollama talk to OrionChat

This is the part I actually got stuck on the first time. By default, Ollama only accepts requests from its own trusted origins, and a browser-based frontend like OrionChat doesn't automatically qualify. Every request gets silently blocked until you explicitly allow it.

Step 1: Check how you're running Ollama

If you installed Ollama via the official install script, it's almost certainly running as a systemd service. If you just launch it manually with ollama serve, skip to Step 3.

Step 2: Edit the Ollama service

You need to add the origin override to ollama.service systemd unit file.

sudo systemctl edit ollama.service

In the editor that opens, add this under the [Service] section:

[Service]
Environment="OLLAMA_ORIGINS=http://localhost"

Save, then reload and restart Ollama:

sudo systemctl daemon-reload
sudo systemctl restart ollama

Step 3: Or, if you run Ollama manually

Skip the systemd steps entirely and just set the variable inline when you launch it:

OLLAMA_ORIGINS=http://localhost ollama serve

Step 4: Confirm it worked

Refresh the OrionChat tab in your browser. It should now connect to Ollama and start listing your locally installed models. You can choose your default Ollama model and start prompting.

It provides an option to upload files and to use the microphone with Ollama models. This is a really nice feature you can't use in CLI mode.

My wxperience running Ollama models with OrionChat

Want to go from Gemma to Qwen? A couple of clicks. Want to try Mistral instead? Select it and keep going. For someone who regularly experiments with different models, this alone is worth the five-minute setup.

Starting a conversation feels exactly like using any modern AI chatbot. Code blocks render properly. Markdown formatting looks clean. Tables show up as tables instead of a mess of pipe characters. Long conversations stay easy to navigate because everything lives in an actual interface instead of a scrolling terminal buffer.

Copying responses feels natural too, no highlighting text in a terminal and fighting with line wrapping. For anyone who asks a lot of questions to local LLMs, that alone noticeably improves the day-to-day experience.

Also, unlike other Ollama UI alternatives, OrionChat is not an Electron wrapper hiding a browser inside your app menu. It lives in a web browser ;)

Final Thoughts

Ollama already made running local models easy, and for quick tests, the terminal is perfectly adequate. But if you're talking to local models for hours every week, a proper interface makes a real difference.

OrionChat hits a sweet spot for me. It doesn't require a complicated deployment, and doesn't try to become an all-in-one AI platform. It focuses on doing one thing well: giving Ollama a pleasant place to live outside the terminal.

After using it for a while, I found myself opening OrionChat first and reaching for the CLI only when managing or updating models themselves.

If you've been relying purely on the Ollama CLI and occasionally wish it felt a bit more like ChatGPT, OrionChat is worth the five minutes it takes to try.

If OrionChat isn't quite your fit, Open WebUI, Enchanted, Chatbox, LibreChat, and NextChat are all solid alternative Ollama frontends, each trading off simplicity against features differently. Worth trying a couple to see which matches your workflow.