Recently I started playing with Nanobot which is a bit like OpenClaw but, in my opinion much better since it is small and simple and have native integration with custom OpenAI APIs providers. I decided to use vLLM because I wanted to use Qwen 3.5 because according to my quick reaserach it is pretty good in such agentic usage and Qwen models are built with integration with SgLang and vLLM frameworks in mind. Also AMD is testing their own drivers and libraries, ROCm on docker images with SgLang and vLLM and Desktop Framework is AMD APU. So all of it since like a good idea since I decided to go with Strix Halo unifies memory architecture for my AI assistant. I bought Desktop Framework motherboard and played with it a little to test models performance on ROCm inside the docker. It was not blazing fast but enough to actually have working solution.
After a while, when I played with this device and learn about its capabilities I was able to recognise my own mistakes, correct them in order to actually do what I intended, in the beginning, to run Qwen 3.5 as my agentic model. I already integrated some of my own smart home devices into it and taught my assistant to browse web and recognise my voice commands.
So far it really feels like great experience. And I have already big plans to make it even better with integration with my calendar, todo list, notifications and similar.
I will check things out but maybe at some point I will add external GPU to Desktop Framework PC. For example Radeon Pro R9700 would be good addition to run some medium sized models really fast and leave slower reasoning for not immediate task to APU.
Probably integrating with better storage for heavy docker images, and model caches on my other server, that several TBs of storage would be better. But for that better networking would probably be better. Better networking require sadly some external NIC or PCIe card with NIC to achieve i.e. 10Gb/s speeds. Also that would be easier with some better switch – right now my whole network is running on 1Gb/s.
Last but not least: I did integrated my own model with Rider IDE and now I can run my own coding assistant 🙂 which is great because I can now work on even some proprietary stuff without compromising security – since everything stays on my own network.
It is not the best device for running some LLMs bit I still think it was money well spend. If not for actual usability then for apportunity to play with some ‘AI’ stuff and do some hacking.
It is amazing how time flies… I am living at my current house already 9 years almost. I did put up a fence around the house in 2019. Next year I installed metal gate with automation and was working on a way to make it more ‘smart’. My first attempt was to have small raspberry Pi connected via some metal wires and solid relays to the gate electrical engine. It is working like that till now.
My second attempt was to use voice assistant to control it. I did few experiments with Rhasspy. It was working to some degree but I felt a bit ridiculous shouting repeatedly ‘Open the gate!’ to the microphone and learning that it either did not recognized my command or recognized wrong one. Maybe it was a mistake and I should try harder but… It did not felt right. I was also working at the time on mobile application to connect everything at my home together and it was much better from usability point of view – just press the button in the app. No need to shout or repeat yourself very carefully in order for silly model to understand me.
For some time I was thinking about integrating my home with Home Assistant Voice, bit it seems like it would be hard integrating with anything other then Home Assistant.
But now we have LLMs.
And not only just for text there are also models for audio, images and video. I was trying to get into it when Llama came out but I had only GeForce 1060 and my tests were not entirely successful. Model was hallucinating a lot and it was slow. Also my PC was randomly rebooting when I was running a model. It seemed like I could not really get into it without substantial money spend.
I had new job, kids, a lot of other projects I was working on and there were never actually time to play with the idea of having a voice assistant.
Until now.
OpenClaw was a big news. But I never really was into using subscriptions and sharing my data with tech giant. I am self hosting my own services. And I would gladly self host my own AI assistant too. Having a device or few somewhere that you can feed your personal data too I order to teach it who your are and what you like, your personal life, favorite movies and bands in order to help you do you everyday stuff. Like… Having a friendly ghost inside your house that will make sure that everyday boring stuff is taken care of while also proving you with new movies suggestions. That is small dream that I have. But OpenClaw seems to be just giant AI slop of 400kLOC of vibe coded mess. I am not trusting that giant pile of spaghetti code with my data.
GPUs did not really are more affordable but I managed to buy 7900 XTX on sale for nice price of 800$ (about 2700PLN) sometime last year. Though ROCm was not really anything serious like CUDA or apple silicon. But is good enlught now.
So I did some research and tried to find if there is another similar solution for running your personal agent like OpenClaw. My choice was s nanobot and so far I am happy with what this little project is capable off but right after I tested it and saw what it is capable of, my dream come back and tried to run it with voice files.
It does not work yet out of the box unless you use some kind of Grok subscription paired with telegram, but I am not using neither and I do not plan to use it. My plan is to run it self-hosted. As everything else I am using.
For example my first attempt to send some audio files to the model were a bit funny. It kept responding ‘excuse me?’ To every single one. It did not tried to so any kind of transcription.
Of course I understand that this model, Qwen 3 30B A3B Instruct do not have audio modality so there was no way for it to succeeded without any help from, by giving it some tools that will be helpful in this scenario. Still funny though.
First fix came to my mind to fix it was to change model that actually can work with audio instead. I tried:
Voxtral Realtime but nor vLLM nor llama.cpp in versions that I had a the time were able to run it
Voxtral Mini 4B and I was not able to run it in vLLM but I was able to run it in llama.cpp, via CLI; unfortunately it was treating audio file as a prompt instead and I could not find way to run it in simple transcription mode. Since it is specialized model, and small one running it as agent would not be good idea either.
I read about whisper and it opinions were that it is OK, but requires usually to cleanup the audio file first and it is stand-alone model that runs by its self; unfortunately there were no version for ROCm that I could find – only CUDA and apple silicon.
And there was Omni version of Qwen 2, but the transcriptions quality of were hilariously bad; it was spiting nonsense. Maybe in English it is much better. Possible that cleaning up audio first would help, I did not tried though because it seemed like a dead end.
I give it a rest for some time and played with different aspects of my AI assistant.
Few days later I was doing some more research about that topic. I found that there is something called faster whisper, and it looks interesting but it requires to have CUDA libraries installed. So it will probably wont work out of the box from Docker image like this one. There is also whisper-rocm which looks like something I could use but it was not touched in 5 months. I am bit afraid to go into that rabbit whole of cmake and pip.
Then after another couple of days I found whisper.cpp project. which also looks interesting. There is even possibility of running it on docker using vulkan GPU acceleration. Not ideal since Vulcan is pretty slow comparing to ROCm but still it was usable and had very good results for Polish language. There is just one small problem: it would require me to write some kind of API wrapper for this to be able to run nanobot on one server and models on Desktop Framework – which is how my setup looks like right now.
Also it would be possible to move nanobot to Desktop Framework device and use acceleration there for fast transcription of files there and just send the transcription to the model as message instead of Matrix metadata. But that would require some work on this new API or some work on nanobot code. Both viable solutions but I wanted to test no code solution first – running a model that would have ability of transcription or multiple models and just send information from one to another till I get desired output.
But it was not possible till I configured vLLM to run on my Desktop Framework Linux via vLLM instead of vLLM inside the docker. The problem with docker images is that they have no vllm component installed via pip on them. vLLM contributors do not want to publish such images.
But now I have working vLLM inside the virtual environment inside Ubuntu with all ROCm libraries. Also I had working Qwen 3.5 which have much better reasoning capabilities. First installed vLLM with audio. Which is as simple as:
Flags TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 VLLM_ROCM_USE_AITER=1are necessary to run vLLM using ROCm on AMD APU +395. Rest is just Voxtral model and its specific parameters. vLLM logs that model was loaded and it supports audio:
(APIServer pid=27174) INFO 03-14 19:54:49 [api_server.py:495] Supported tasks: ['generate', 'transcription']
(APIServer pid=27174) INFO 03-14 19:54:50 [launcher.py:47] Route: /v1/audio/transcriptions, Methods: POST
(APIServer pid=27174) INFO 03-14 19:54:50 [launcher.py:47] Route: /v1/audio/translations, Methods: POST
With docs and endpoint it not should be to complicated. It took me a moment but something like that worked:
And it worked pretty well, considering that I was not cleaning my recordings and I do not have good diction. In summary it was able to understand me like 80-90% of time.
There is also a possibility to use chat completion API for this. If model is capable of audio, you can send an attachment and ask about it. This would be a bit better if I could do something like that via element but so far, I do not think it is possible. A bit sad because I could send a recording of me noting my thoughts on something and asking my little AI assistant to add it to my notes… But there is always another day.
So the only thing left to do was to teach my assistant what to do when I am sending audio files. It should
recognize that message consists only from audio file attachment
send this attachment to Voxtral model vLLM server transcription endpoint
this endpoint will return text
text from audio transcription should be treated as prompt
I started with prompt:
Add a new skill. Audio file transcription. It will work by sending the file to http://llm:8001 using the OpenAI-compatible API for file transcription. After receiving the transcription response, execute the task from the processed audio file.
In retrospect maybe I should split it into 2 skills. One for pure transcription of an audio, with maybe support of an language parameter and second one that would be transcribing an audio and treating it as a prompt. Third thing would be to actually add entry to SOUL.md file that it is required to execute audio files from matrix as a prompt.
Do you want me to test it on one of the audio files?
I asked it then for 1 small fix: add default language as Polish. And I started testing it. I send one of the files I recorded few days ago with the question, in my native language: “How is the weather today?”. Test was a success but bot hit a small roadblock
File does not exist. Check where the file is located:
API does not recognize the whisper-1 model. I’ll try without the model parameter:
Transcription works!
The file contained: “Jaka dzisiaj pogoda?”
Do you want to test it on another file?
But it was able to overcome it by itself! Amazing! Since that was working there was only last step to do: ask it to execute the ask in the file instead of transcribing it. I send it with an additional explanation that audio should be executed.
It was a success. Then I wanted to test it with an .ogg file which is by default sent by element. If this would not work I would have to ask the bot to add conversion from .ogg to .mp3 format.
And it failed.
I’ve completed processing but have no response to give.
It responded in English not in Polish and the response sounded like null or None translated to plain English.
Strange… I send another file. This time recording. Again the same response. I asked it for transcription and it gave me correct response. But it did not executed it and did not check the weather.
Then I tried to give it strong nudge to force it to remember that it should treat audio files as prompts. I think it maybe was to strong and model exaggerated a bit.
True. I’ll update the skill to automatically execute commands from audio files.
I updated the script — now it automatically executes commands from transcription.
I’ll test it on a .ogg file:
Error — command is not in bash format. I need a parser that recognizes the command type.
I’ll update the script to handle common commands:
I updated the script — now it recognizes common commands (weather, gate, wicket) and executes the appropriate actions.
I’ll test again:
It works! The script:
Transcribes the audio file Recognizes the command “Jaka dzisiaj pogoda?” Automatically checks the weather and displays the result Do you want to add more recognized commands?
Hmm I was a bit puzzled by the outcome of that command. I inspected a bit the bash file it created and it was looking like something I would expect minus the execution of ‘common commands’ as it was calling my usual prompts. I left it there for now because it is not blazing fast so maybe this way I will save few seconds in my car when I am waiting for it to think. That was enough for this busy Saturday anyway and I was happy with that outcome for now.
Summary
It was very successful test of an audio capabilities of an Voxtral model and at the same time reasoning capabilities of Qwen 3.5 model. Together they helped me to teach AI assistant to understand my voice messages. That is big achievement. Adding direct execution of prompts to the bash file left a bit sour feeling, but I think it is only because it was implemented differently than I was imagining – something that software engineers will understand.
On another note: even if AMD Ryzen +395 APU is designed to run LLMs it is still a bit slow with multiple back and forth between the model and the nanobot agent. Executing action this way takes around 30-60s. It should be much quicker if nanobot would directly send audio files to a transcription API and then to Qwen 3.5 for processing. But for now… It is OK! More than OK ability to ask your own computer to do something… Beyond what I was imagining few years back.
I did make mistake installing my favorite Debian Linux distribution on Desktop Framework PC I am using to run my LLM models. Probably some hacking during installing vLLM and all libraries and drivers and I would be able to make it work. But is it really worth it? I think it was not. So I switched to Ubuntu and I am running Qwen 3.5 second day now.
For now I can say that I am very happy. It seems to be able to reason much better than its predecessor Qwen 3. It have pretty good vision capabilities. It is able to fix its own mistakes, which makes its usage much easier.
Agent Loop
I do not know why but I had some weird problems with running Qwen as model for an agent with loops and tool usage. It was working to some degree but I had to do change some parameters.
When I changed it to llama3_json it was working but agent loop was not… well… looping!
And this loop is kinda whole point of agents.
For example I asked it to check the weather after configuring it with a new skill before, with all the explanation how to do that, with all the information it need to call the API… it worked at first but then it forgot:
The API key for OpenWeatherMap is invalid or expired.
Let’s try again — but first let’s check if the key is in SKILL.md:
And then nothing…
And nothing…
It was not doing anything. I am sure because I waited and then checked the nanobot logs and vLLM server activity and both were idle. Again I think I did not broke it. Maybe custom tool parser did but without none tool were called ever so it is better to have at least one loop and one tool call then none whatsoever.
With Qwen 3.5 both works out of the box. For example I asked it to check opening hours of tool shop nearby me:
And it kept looping and looping an looping… which is a bit frustrating on its own… but it is better to have long loops then no loops at all! I think 🙂
It tried to find it by:
my own custom web search based on Browsh
Duck Duck Go via Browsh
and one more time DDG but via cURL
Google
Google via Browsh
Then pure html version of DDG
then DDG one more time
Bing
Google Maps (this one was surprising!)
by opening shop web page 5 times a bit differently every time
Google Maps again
shop web page like 20 times
This is when I stopped it because it was doing the same thing over and over again and it was not going anywhere and I did found it myself anyway already. Whole process took my assistant maybe 25 minutes already.
I felt a bit silly and confounded watching it. But on the other hand it was great to have a loop!
Vision capability
This one was surprising! And in a good way! It was nice sunny day which is nice because we had harsh winter and it is still, technically a winter. Having almost 20℃ it was ideal for some activities out side. I took a picture of my wife and send it to the bot just to check what will happen.
It answered with an actual correct facts.
Beautiful view!
The photo shows:
Garden with grass and trees Gate on the left side Greenhouse with metal structure Solar panels on the roof Wooden swing in the background Person in the middle of the garden Sunny day — shadows are visible Do you want me to do anything else?
OK, maybe one thing was not entirely correct. Gate on the left side was in fact a fence. But still, quite impressive.
I will probably use it to tag and organize my photos. I also wonder if model could be finetuned to recognize my family members on photos.
And it would be really cool to teach it to answer questions like: is there my son on this image? If yes I could be use it to inform me for example where my family members are inside and outside of my house; paired with my security camera I could automate things like close the property gate when I am outside and car just drove into the garage. No more doing it manually! But only if there are no people near the gate since this is like 500kgs of moving metal. Of course there are other safeguards… but still!
As another test I send it captcha image
It answered ‘tophones’. Almost perfect!
Self correcting
It is able to correct its own mistakes. To a degree but still. For example, when I asked it to add new skill, it wrote it in incorrect path, but then fixed it immediately.
Error — I wrote the file in the wrong path. I’ll fix it:
I updated the file name. Now I’ll test:
Or when I added another skill it was unable to call external OpenAI API. I forgot to specify name of the model to call in this API and it hallucinated one called whisper-1. Of course it was incorrect but then it decided to call the API again without the model name. And this time succeeded.
This was nice to see your virtual entity making mistakes and learning in the process.
Initiative
I was able to teach my assistant voice transcription of audio files I will sent via matrix. I was a little surprised to see that instead of doing what I asked it to do:
I will send audio
Transcribe it
Take an action on transcription
My assistant transcribed a message and then just sent it to the chat as text message. Which is still an achievement, even if this is not entirely what I asked for. I asked to act on a transcription. When I asked why it sent it as a message it corrected itself and shown initiative by transcribing the file again and acting upon this but via changing the script that it wrote for transcription in following way:
if(transcribedAudio == "do X")
{
doX();
}
And then it showed another initiative and automatically transcribed previous file again but this time it was automatically acted upon but this time the action was executed but now from model tool generated response but by the command hardcoded in the script. It left a bit sour taste in my mouth but after a bit of time I decided that it was for the better, optimize response times in that way.
It would be probably better to have small model trained in such most popular voice commands deciding what should be done instead of simple if. Or it should be at the least trimmed and lower case, plain ASCII characters only comparison. Otherwise “Open” and “open” will be different cases in this comparison.
Summary
After one day of using Qwen 3.5 as my AI assistant model I am very pleasantly surprised by its capabilities and I can’t way to work with it further on automating some boring stuff that I am doing every day.
I am using pyLoad download manager for quite some time now. Previously when I was running windows server I was using JDownloader. I think it was better in few things, for example it was able to automatically name a package based on the name of the file from the link. So if I would be trying to download https://cdimage.debian.org/debian-cd/current/amd64/iso-cd/debian-13.4.0-amd64-netinst.iso, it would automatically name a package ‘debian-13.4.0-amd64-netinst.iso’. Folder with downloaded file would be named the same too. Much more convenient. If I would drop multiple links for the same file it would be downloading them in parallel, downloading them much quicker. Though with current speeds in average home maybe this is not necessary anymore. It had better UI, in my opinion, but maybe I was more accustomed to windows desktop then; certainly it was better to have single view instead of 3 views like in pyLoad:
For current downloads
for already finished downloads
for packages
But it did not have web ui. There was some kind of plugin for that, but it is hard to add web UI to the program that was meant to be windows application and I was unable to make it work.
When I switched to pyload I was a bit sad by the downgrade, but having ability to run your service in web browser was worth it. Still I do not like UI in pyload so and replacing it with my AI assistant seemed like a great idea to save few few minutes every time I wanted to download something.
That was something really surprising and it did not stopped me. Since there is web UI, there must be some kind of an API that can be called for login and adding packages. I inspected the page in the browser and it was sending login form via /login page.
Ok, this does not look that bad and returns Set-Cookie header, so it should be fine. But then, I could not call correctly endpoint for actually adding a package. It did not work – I could not figure out how to call it with correct set of parameters that will be recognized as valid links. The web UI one on the other hand were not working without the Csrf token. And then API endpoints from docks stopped working too showing that they are ‘Obsolete API’. After doing login again it started working but they were required Csrf token this time. OK fine, I can get you CSRF token. I found it in head>meta, but it requires another call to /dashboard for HTML.
curl https://p.np0.pl/dashboard
CSRF token is in <meta name="csrf-token" content="">. Value of the tag need to be extracted and fed to another call. After that addPackage finally worked but it is not JSON API too and I had to guess how to pass list of links to it.
Again it is not as straightforward as it should be. Passing a string causes pyload to use string as list and tries to download every letter. Not what I wanted but a bit funny though.
Not as straightforward as I would hope but at least it works. Ideally it would be just one call with an API key with JSON inside. That would be simple and easy to understand. But pyLoad is pretty old codebase so I was not totally surprised by this complication.
I tested it all in Rider that have pretty nifty tool for HTTP calls:
### GET request to example server
# @no-redirect
POST https://p.np0.pl/login?next=dashboard
Content-Type: application/x-www-form-urlencoded
do=login&username=u&password=p&submit=Login
> {%
if (response.headers.valueOf("set-cookie")) {
client.global.set("cookie", response.headers.valueOf("set-cookie"));
client.log(response.headers.valueOf("set-cookie"))
}
%}
###
GET https://p.np0.pl/dashboard
> {%
const csrfRegex = /<meta\s+name=["']csrf-token["']\s+content=["']([^"']+)["']/i;
const match = response.body.match(csrfRegex);
if (match && match[1]) {
const csrfValue = match[1];
client.global.set("crsf", csrfValue);
client.log("CSRF token: " + csrfValue);
}
%}
###
POST https://p.np0.pl/api/addPackage?name=test&links=["https://cdimage.debian.org/cdimage/daily-builds/daily/arch-latest/arm64/iso-cd/debian-testing-arm64-netinst.iso"]
X-Csrf-Token: {{crsf}}
I used prompt like below to make bot generate this flow as new skill.
Add skill: download via pyLoad. This skill will send files to download using the pyLoad application. PyLoad is located at “pyload.local”. The file is an address in http or https format.
The process works as follows:
Log in by performing a POST to the /login endpoint with data 'do=login&username=user&password=password&submit=Login' in application/x-www-form-urlencoded format
Get the response header value set-cookie – this will be needed for subsequent requests
Fetch HTML from https://p.np0.pl/dashboard using the cookie value from the previous step. In the response, find the value of the meta tag named csrf-token – this will be needed for subsequent requests
Send a POST request to https://p.np0.pl/api/addPackage?name={{filename}}&links=["{{fileurl}}"] using the cookie and the meta tag value as csrf token
This was enough for assistant to write working skill with working bash script. I just ask it to adjust name of the skill and name of bash script file since I did not liked what it came up with, but that was really minor things, otherwise it worked pretty much the first time. Qwen 3.5 seems really capable.
Right now I can just write to my AI assistant:
Download using pyLoad following file https://cdimage.debian.org/cdimage/daily-builds/daily/arch-latest/arm64/iso-cd/debian-testing-arm64-netinst.iso
And pyLoad will take care of the rest. It is a bit more convenient then my previous flow that was:
Copy the link
open pyLoad
Login into pyLoad
If I not logged into my password manager than login in there first
Open new package form
Invent some package name that I usually shorten and even mistype sometimes for me later to wonder what that is
Copy link into the form
Submit
Right now I can write just one prompt. Probably with few prompts of explanations or change of SOUL.md file I could just send a link to the chatbot and it would do all the rest. I will do that next time.
Whole script looks similar to below (it was generated by Qwen):
#!/bin/bash
# pyLoad skill - Download files using pyLoad on p.np0.pl
FILE_URL="$1"
PACKAGE_NAME="${2:-$(basename "$FILE_URL" | cut -d'?' -f1)}"
if [ -z "$FILE_URL" ]; then
echo "Użycie: pyload-download <adres_pliku> [nazwa_pakietu]"
exit 1
fi
# Krok 1: Zaloguj siÄ™
LOGIN_RESPONSE=$(curl -s -c /tmp/pyload_cookies.txt -b /tmp/pyload_cookies.txt \
-X POST "https://p.np0.pl/login" \
-H "Content-Type: application/x-www-form-urlencoded" \
-d "do=login&username=user&password=pass&submit=Login")
if [ $? -ne 0 ]; then
echo "BÅ‚Ä…d logowania"
exit 1
fi
# Krok 2: Pobierz dashboard i CSRF token
DASHBOARD=$(curl -s -b /tmp/pyload_cookies.txt -c /tmp/pyload_cookies.txt "https://p.np0.pl/dashboard")
CSRF_TOKEN=$(echo "$DASHBOARD" | grep -oP '(?<=<meta name="csrf-token" content=")[^"]+')
if [ -z "$CSRF_TOKEN" ]; then
echo "Nie znaleziono CSRF token"
exit 1
fi
# Krok 3: Dodaj pakiet
ADD_RESPONSE=$(curl -s -b /tmp/pyload_cookies.txt -c /tmp/pyload_cookies.txt \
-X POST "https://p.np0.pl/api/addPackage" \
-H "Content-Type: application/x-www-form-urlencoded" \
-H "x-csrf-token: $CSRF_TOKEN" \
-d "name=$PACKAGE_NAME&links=[\"$FILE_URL\"]")
if [ $? -ne 0 ]; then
echo "BÅ‚Ä…d dodawania pakietu"
exit 1
fi
echo "Pakiet '$PACKAGE_NAME' dodany do pyLoad"
echo "Status: $ADD_RESPONSE"
Summary
Maybe at some point I will think about using my AI assistant to sort my downloads into better directory structure or I will remove pyLoad altogether, but for now it is fine.
I bough lately Desktop Framework with intention of running Qwen 3.5 as model for my AI assistant on Nanobot. At first I could not run this model on this hardware for some weird bug in one of the libraries. I explained why and how I fixed it here. In this post I will just put the list of packages that I used to run Qwen finally and vLLM command switches and parameters.
Here is the list of packages that I used to finally get it working:
