Good Morning from my Robotics Lab! This is Shadow_8472, and today I’ve been playing with my very own Hermes AI agent. Let’s get started!
What is Hermes Agent?
At a fundamental level, the current generation of AI (Artificial Intelligence) runs off software constructs that mimic biological neurons firing off in sequence. In contrast to traditional programming where logic is hard coded, these neural nets are laid out, then “trained” by example to perform desired tasks when deployed to an “inference” phase. Traditional AI’s focus on pattern recognition (hand writing reader, market trend analysis) to generative AI’s that fill in data as they work (text/image/sound generation) based on prompts and context.
A Large Language Model (LLM) is a set of billions to trillions of neurons (or weights) trained to use language, and is at the core of all AI chat bots. (For scale, the digits 0-9 can be identified with around 200 weights not counting input neurons.) The “context window” for an LLM is how many “tokens” (linguistic elements – close, but not quite 1:1 with words) it can think about at once before forgetting the beginning of the conversation.
Various techniques and projects have extended these basics. Permanent character cards stay put in context to keep the LLM focused. Lorebooks can inject immediate context without wasting context. Conversation summaries can extend useful conversation at the cost of quality. A heartbeat can re-prompt the LLM to keep going. Taking everything together, you cross the line into “Agentic AI.” Mind Craft re-frames the 3D world of Minecraft as a text adventure [1]. Through clever prompting and this software stack, a number of LLM’s have beaten the Ender Dragon.
Hermes is an Agentic AI harness, but instead of playing Minecraft, it gives the LLM a suite of tools for interacting with your computer. Talk to it in plain English (or another language it understands), and the idea is for it to keep working until an expressed task is done. What sets Hermes apart is its self-improvement system. By letting it essentially write its own lorebook, it can take notes on what successful tasks and improve its procedure (or skills/tools) as it learns [2].
ButtonMash’s Power Supply
But my AI agent needs a “body.” If you’re just here for Hermes, skip this section. Otherwise, have some backstory.
My homelab has two servers running Rocky Linux (9 and 8, respectively). These days, the production server is Joystick, with ButtonMash running idle, full of old, inactive projects. It would make an excellent host for my AI tinkering – if only Cockpit (browser-based admin panel) would come up. I had to coax the hardware into responding, but when it did, Linux shut itself down, locking me out of diagnosing the problem. After that: blinky yellow power button.
I looked up ButtonMash’s blink pattern and found I was looking at the Power Supply Unit (PSU). A kind neighbor lent me his air compressor to dust it, but the failure remained. I followed up with a leaf blower, but no dice. A replacement PSU set me back $15, and I couldn’t confirm ahead of time because 1. Joystick, a Dell Optiplex 7020 to ButtonMash’s 7010, has a different motherboard power connector arrangement, and 2. my emergency PSU’s CPU connector has 6 pins to the Optiplexs’ 4 – the connector will physically connects with overhang, the polarity is backwards. The new PSU arrived after several days and solved the problem.
While I was waiting on the mail, I took inventory and found DerpyChips’ case vacant since pulling the hard drive for a nicer case with a dead SSD (Solid State Drive). I soon had ButtonMash/Derpy booted.
Hermes: take I
Hermes itself isn’t that heavy on system requirements, but running an LLM is. So, my plan was such: Hermes on ButtonMash (as I’ll be referring to the SSD) for isolation with the LLM on my main laptop for its modern computing power. I used a previous AI back end with Dolphin 3 as my LLM. Yeah… Dolphin wasn’t trained to use agent tools. It crashed when explicitly asked to use them. Badly.
I reached out to the community on Discord and eventually gleaned/was given several pointers. The top models for agentic tasks at the time of research (early June, 2026) are Chinese in origin. Without getting into the specifics of inter-national politics or family history living under the early CCP, my thoughts on the matter are that Google may be a villain, but at least it’s an AMERICAN villain! If there’s a chance I’ll be slowly re-indoctrinated by a cross-biased –though privately operated– LLM, I’ll rather have one born waiving the Star Spangled Banner! (Though I’ll save a mention for OmniCoder in case I relax my stance to something merely fine-tuned in the States.)
Thinking of Google, I remember they made news somewhat recently by releasing a major breakthrough that takes better advantage of computer hardware. I found Gemma 4, along with confirmation about it being trained on tools.
Side Project 1: Mounting an Encrypted Linux Drive
So, a while back, I tried PopOS 24.04 on a second M.2 drive, but was never able to mount it in my original PopOS 22.04. Thing is, LLM’s are big, and I was out of space. Coming from the perspective of saving a Linux install or two thanks to redundant boot drives, I wanted that extra insurance for my laptop.
As it turned out, installing PopOS with cryptsetup on both drives left them with matching Volume Group Names of “data”, and LVM (Logical Volume Manager) can’t have that. It just refuses to mount with a generic-sounding error. I sadly don’t have the exact command I used to re-name my volume group, but I ended with a name of data2 and manually mounted it at /data. [3] [4]
Hermes: Take II
With my newly unleashed M.2 drive3, I downloaded Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q8_K_P.gguf [5] and compiled llama.cpp [6] to run on the command line despite advice to start with a user-friendly front end like Ollama. I also got a reality check of only having 16GB vRAM on my laptop’s NVIDIA RTX 4090 GPU. Ideally, both the model and my context window will fit in vRAM to minimize data travel time, but they can overflow to system RAM or the hard drive if called upon. In short: I was warned about slow speeds, and that’s what happened. Realistic expectations locked, I began round 2 with my agent.
I named my agent Shimmering Dust after a character relevant to a role play I was once in. For his character card, I made him a unicorn wizard who migrated to a linked human dimension when his world was under imminent threat. Once there, he learned Information Technology and opened a computer repair shop, which he is sure to keep closed during Sabbath hours (we even had an incomplete project intended to pause agentic work Friday sundown to Saturday sundown).
Side Project 2: Graphire 4 Verification
On my backlog of potential blog topics, I have a Graphire 4 tablet from my grandfather. I knew it at least turned on a light and introduced itself to the Linux kernel from previous testing, but it has two buttons and a scroll wheel as if for a mouse.
After Shimmering Dust demonstrated tool use, he helped me write a udev rule to force load the drawing pad’s mouse button events correctly. It took another hour or so, but we figured out the kernel saw the scroll wheel as a joystick and modified the rule accordingly. Shimmering Dust was eager to go farther, but I was satisfied with distinguishing those events from the command line.
Side Project 3: Xbox Kinect/Libfreenect2
The Graphire 4 felt almost too trivial to write about, so I pulled another challenge from the backlog: an Xbox Kinect V2. The hardware juggling got intense, but the fast version is that I decided ButtonMash was due for a reinstall soon and therefore semi-expendable – suitable for exparimentation. With my blessing (and nudging), Shimmering Dust compiled the stubborn community Kinect2 driver, Libfreenect2 [7], and installed the thing.
Shimmering Dust worked on a Python script to test the Kinect, but the best we ever got was a busy signal from the device itself before I noticed an explicit incompatibility with DerpyChips’ USB 3 ports. Once ButtonMash’s new PSU was installed, we tried the USB 3 expansion card, to see the same company made that too. I then set a Raspberry Pi 4 with DietPi [8] and a monitor.
I tried having Shimmering Dust do a solo install, but he slowed way down as the we kept going without a “session handoff.” The effort concluded with a live RGB signal streaming to Protonect on the Pi.
Hermes: Take III
I don’t know for sure if Gemma 4 26B’s experts are being swapped out to my GPU properly or if I’m in strict CPU mode for improperly configured Llama.cpp. My plan going forward is to install a Ollama to see if that helps. Long term, I’ll want dedicated AI hardware for Shimmering Dust to do his heavy-duty thinking on.
But for now, I’m still learning Hermes. Network Chuck passed on the recommendation to chat with your agent like a team member, and I’ll agree; that is working out for me as well. Explain problems, call out mistakes, ask questions, trust but verify. Thanks to this project, I’m more confident with udev rules basics, and I know the Libfreenect2 driver can compile on modern Linux.
What didn’t work out as well was having Shimmering Dust try to solo it. He kept pausing for a prompt to continue after modifying his plan to continue, which got annoying as compute times stretched into the half hour range, an issue solved with a session handoff after each major goal.
Takeaway
For better or worse, we are in the AI Revolution. It’s having a rough launch, but so was the Industrial Revolution. Problems can be worked on. While I don’t believe corporate AI is the right future, I do believe in the promises of AI used responsibly.
Prayer
Father in Heaven,
I thank you for creating the world and all that is good in it. I trust that we don’t have to fear the future because you’ve promised to make everything right in the end. With that said, there’s some scary stuff out there if we get AI wrong. Please, inspire the right researchers as they study your designs of the brain to improve their own – including anyone in my audience, Lord. Please, bless them as you are able.
In Jesus’ name I pray,
Amen
Final Question
What projects do you have in your backlog that might be made easier by leveraging an AI agent?
Works Cited
[1]. Mindcraft-Bots, “Mindcraft,” GitHub.com, Jun 9, 2026. [Online].Available: https://github.com/mindcraft-bots/mindcraft Accessed: Jun. 25, 2026.
[2]. NousResearch, “Hermes Agent,” nousresearch.com, [Online]. Available: https://hermes-agent.nousresearch.com/ Accessed: Jun. 25, 2026.
[3]. Gilles ‘SO- stop being evil’, Zoredache, and Sandra, “LV Status: Not available. How to make it available?,”serverfault.com, April 29, 2015.[Online]. Available: https://serverfault.com/questions/170578/lv-status-not-available-how-to-make-it-available Accessed: June 25, 2026.
[4].W. Walker, Artiom, Gilles ‘SO- stop being evil’, and user2267, “How to rename a logical volume when there are multiple volume groups with the same name,” stackexchange.com, Aug. 19, 2022. [Online]. Available: https://unix.stackexchange.com/questions/324413/how-to-rename-a-logical-volume-when-there-are-multiple-volume-groups-with-the-sa Accessed: Jun 25, 2026.
[5]. HauauCS, “HauhauCS/Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced,” huggingface.co, May 14, 2026. [Online]. Available: https://huggingface.co/HauhauCS/Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced Accessed: June 25, 2026.
[6]. Ggml-org, “llama.cpp,” github.com, [Online]. Available: https://github.com/ggml-org/llama.cpp Accessed: June 25, 2026.
[7]. OpenKinect, “Libfreenect 2,”github.com,[Online]. Available: https://github.com/OpenKinect/libfreenect2 Accessed: June 25, 2026
[8]. D. Knight, “DietPi,”dietpi.com,[Online]. Available: https://dietpi.com/ Accessed: June 25, 2026

















