ShunyaNet Sentinel (UPDATED: 03/02/2026)

ShunyaNet Sentinel is a lightweight, cyberpunk-themed program that ingests RSS feeds (e.g., breaking news, social media), sends them to an LLM for analysis, and delivers alerts and summary reports directly to the GUI and Slack at regular intervals.

The project is built to utilize an LLM hosted locally on the same machine or network (e.g., with Tailscale) using LMStudio or Ollama (02/23 Update). It will also work with an OpenAI API key (02/23 Update).

For alerting, the program utilizes Slack Webhooks, enabling push notifications to be sent to a mobile device.

The ShunyaNet Sentinel is compatible with the latest versions of Linux, MacOS, and Windows*

The quality of reporting and analysis is influenced by the prompt, context size, RSS feeds and LLM chosen. It's recommended you turn off thinking features. Models that seem to have performed well and generally follow instructions, include:

• GPT OSS 20b (thinking set to LOW in LMStudio)
• GPT OSS 120b (prob. overkill)
• Hermes 4 70b (my go-to)
• Gemma 3 27b it abliterated (mlabonne's version)
• Qwen3 32b and/or VL 30b (i forget which...)

*Strongly suggest macOS or Linux. See Known Issues below.

See Tips, Tricks, and Known Issues at the end for important info!

High-Level Workflow

1. User enters topics of interest
2. User provides a list of RSS feeds
3. Sentinel pulls RSS feeds at user-configured intervals of time
4. RSS content is sent to the user-provided LLM server
5. The LLM reviews feeds and reports back to Sentinel on relevant topics of interest
6. If Slack webhook is configured, alerts are forwarded and notifications can be accessed on ios/android (often with live links to referenced RSS content)
7. Optional bulk analysis identifies trends over time

Core Capabilities

RSS Feed Monitoring

• Periodic polling of RSS feeds
• Deduplication and timestamp tracking
• Handles slow or malformed feeds (...more work to be done here)

LLM-Based Analysis (Optional)

• Sends RSS feed items to local/api LLM endpoint
• Prompt-driven classification, summarization, filtering
• Works with any OpenAI-compatible /v1/chat/completions endpoint, designed for LMStudio

Alerting / Signal Generation

• Slack webhook support (for webhook setup info, go here: https://docs.slack.dev/messaging/sending-messages-using-incoming-webhooks/)
• Structured output suitable for automation
• Designed for integration into larger workflows

Local & Self-Hosted

• Other than polling RSS feeds, or sending replies to slack (optional), all information remains on hardware you control

Requirements

• Python 3.10+
• Dependencies listed in requirements.txt:
  • PySide6==6.10.0
  • feedparser==6.0.12
  • requests==2.31.0
  • python-dateutil==2.9.0.post0

LLM hosted via:

• LM Studio-hosted (recommended)
• Ollama
• OpenAI api key
• (a few more to come soon)

Installation

1. Clone Repository (...Or Download the Zip) & Navigate to the Folder

git clone https://github.com/EverythingsComputer/ShunyaNet-Sentinel.git
cd ShunyaNet-Sentinel

2. (Strongly Recommended) Create Virtual Environment

macOS / Linux:

python3 -m venv venv
source venv/bin/activate

Windows (PowerShell):

python -m venv venv
venv\Scripts\Activate.ps1

3. Install Requirements

pip install -r requirements.txt

4. Run

macOS / Linux:

python3 ShunyaNet_Sentinel.py

Windows:

python ShunyaNet_Sentinel.py

Quick Start (with LMStudio) (UPDATED 02/23)

1. Click "Load Prompt File" and load default_prompt.txt
2. Click "Load Data Source File" and Load Default_test RSS list
3. Open "Additional Settings" and enter:
   • LM_PROVIDER = lmstudio
   • LLM_BASE_URL = <YOUR LMSTUDIO SERVER IP, e.g.: http://x.x.x.x:port/v1>
   • LLM_MODEL = <I recommend you leave this blank. Just follow Step 4>
   • API_KEY = <leave blank, unless you set one for your server>
4. Load model in LM Studio. Turn on "Serve on Local Network" in Server Settings. Tips:
   • In the "Load Model"/model selection window, turn on "Manually choose model load parameters"
   • In your model's load parameters window, Set Max Concurrent Predictions to 1 (for now)
   • In the inference tab, turn off or minimize thinking, if that setting is available.
   • A model of moderate size that works well: gpt-oss-20b. Thinking set to "low"
5. Click the cat! (...or just hit Fetch / Send)

Full Instructions & Config (UPDATED 03/02/2026)

1. Enter topics of interest, or load one of the default lists provided. Up to 10 topics may be added to each list.

2. Click “Load Prompt File” to load a prompt file. A default prompt is provided (default_prompt.txt).

   1. You’re encouraged to tweak and revise this prompt - it may substantially improve the quality of reporting and the ability of the LLM to identify a signal amid noise. There is A LOT of room for customization here.
   2. There is a 'default_prompt-always-reply.txt' included in the experimental folder. It's outdated, but you can use this to debug/test the LLM's analysis and make sure it is reporting something back in the correct format.
   3. UPDATED 03/02/2026 There is a 'default_Summary-Report-Only_prompt.txt' prompt also provided. This version doesnt seek to pick a signal out of noise, but rather to summarize a greater volume of relevant feeds when the information space is rich with on-topic data. Thus, it is more useful after the "event" you are looking for has occured. It is also best used less frequently (e.g., every half hour for a fast-moving and widly-reported event, or every few hours for something with less reported data) and with a large token allowance for the prompt and reply.

3. Click “Load Data Source File” to load an RSS list. Two default lists are provided. A short “Default_test” list and a longer “Default_long” list, which focuses on world-wide news and breaking news.

   1. Tailoring your own lists to your region or topics of interest will significantly affect the output of information. An example region-focused list that I used for a recent trip is provided ('India_regional_example-v1.txt'). An example Iran-conflict one is also included.
   2. Reddit and blue-sky can be easily converted into RSS feeds. Programs, such as RSSBridge, can also generate RSS feeds from websites and social media (e.g., telegram) that don’t have one.
   3. The “Default_test” list is a short list of a variety of RSS feeds. The purpose is to keep the first RSS pull quick and short, so that you can diagnose whether all the pieces are working the way they should.

4. In "Additional Settings", set the following fields. These will save and persist if you end and restart the program. The default settings will work with most configurations - but you must still enter field #1 yourself:

6. Load your model (e.g., in LMStudio, Ollama) or acquire your API Key (e.g., for OpenAI) of choice and be sure to set its context window to comfortably exceed the value you enter in the TOKENS_INPUT fields of ShunyaNet Sentinel. The program is currently designed to be compatible with LMStudio, Ollama, and OpenAI (via API Key). Then, go to "Additional Settings" in ShunyaNet Sentinel and fill in the following fields as appropriate for your LLM solution:

LMSTUDIO

• LLM_PROVIDER = lmstudio
• LLM_BASE_URL = <your LMStudio server URL, e.g.: http://localhost:1234/v1>
• LLM_MODEL = <OPTIONAL. I recommend leaving this blank and loading your model in LMStudio directly. If you must, then the format is like: lmstudio-community/mistral-7b-instruct>
• LLM_API_KEY = <Leave this blank, unless you use an authentical key>

OLLAMA

• LLM_PROVIDER = ollama
• LLM_BASE_URL = <Your LMStudio server URL, e.g.: http://localhost:11434/v1>
• LLM_MODEL = llama3
• LLM_API_KEY = = <leave this empty/blank>

OPENAI (via API KEY)

• LLM_PROVIDER = openai
• LLM_BASE_URL = (doesn't matter: this field is ignored)
• LLM_MODEL = gpt-4o-mini
• LLM_API_KEY = sk-xxxx

7. Done! - Now click the cat! (...or hit "Fetch / Send", or just wait the number of seconds you set in FETCH_INTERVAL)

NOTE: I recommend you keep it simple for the first run. Use the default settings & make sure it works. Then, tweak context & RSS feeds. Then, adjust the prompt. I’d be curious to see folks’ improved prompts….

Tips, Tricks, and Known Issues

Issues/Warnings:

• Sentinel spontaneously quits on Windows 11 after a few hours. No idea why. No errors thrown. Doesn’t have this problem on Mac or Linux - and maybe your system will be different! If you encounter this issue and must use Windows, then I recommend you set up a virtual box with Linux/ubuntu (or suggest a better solution, if you have one!). Note, this program will run fine on a raspberry pi!
• Poor configuration choices can overload your LLM or hardware. Make sure your LLM can reliably process and reply faster than your RSS pull interval.
• LMStudio recently implemented the ability ("parallel") for an LLM to accept and process more than one prompt at once. Turn this off until you have tested it with this app, or else you can get stuck with time-out errors and a backlog of ever-continuous prompt processing.
• An error message is sometimes thrown when the app is closed on MacOS, maybe also Linux. It doesn’t seem to affect how the program operates or its stability, though. I fixed this once, but it came back. I will fix it again …at some point.
• A slow or 404 URL may slow the feed pull, so monitor your logs once in a while to cull those. The default rss lists include a few of those, but it's managable.

Suggestions:

• The first fetch / send is a stress test: it pulls the maximum feed volume for your settings and thus is likely to contain stale information. However, this is a good way to test whether everything is working, to understand your longest prompt processing time, and to get a sample of how your prompt, rss list, and topic list will perform.
• Some "thinking" models produce malformed replies or get stuck in loops. I recommend turning thinking off first. Thinking set to "low" works fine for GPT OSS.
• There are all sorts of tricks to broadcast feeds that don’t have RSS by default (e.g., look into RSSBridge). Also, some social sites can be converted into RSS feeds automatically (e.g., adding .rss to a reddit URL, or /RSS to a bluesky profile URL.)
• Some sites, reddit specifically, will rate-limit your RSS pulls. To minimize this issue, I strongly recommend you randomize the order of your RSS url list so that you do not hit the same site too fast.

Status

Alpha

License

This project is licensed under the Apache 2.0.

Attribution: ShunyaNet / EverythingsComputer

No Warranty: The software is provided "as is", without warranty of any kind, express or implied.

Dependencies

This project uses the following third-party libraries. Please review their licenses when using, distributiong, and/or altering this project:

• PySide6 — LGPL
• feedparser — BSD
• requests — Apache 2.0
• python-dateutil — BSD

Name Origin

My cat.