Für die, die es schon immer wissen wollten: Ich hatte es nicht im Netz gefunden, daher habe ich die Temperaturen beim Resch&Frisch Ofen selber gemessen. Hier sind die Programme und die dazugehörigen Gradzahlen:
Programm Temperatur
Programm 1 125° / 130°
Programm 2 190°
Programm 3 180°
Programm 4 170°
Programm 6 160°
Programm 7 160°
Programm 12 240°🧌 New feature for MonsterMQ and MonsterMQ-Edge!
Database connections for archives can now be configured at runtime directly in the dashboard – no restart needed.
👉 This is especially important for MonsterMQ-Edge running on a Unified Comfort Panel – you can now configure archiving online, without touching the panel (MMQ config file).
The result: your data from WinCC Unified Panels can be easily archived centrally to PostgreSQL or MongoDB, configured and managed remotely.
Or forward the data to a central full MonsterMQ instances to collect the data from all your panels.
And here’s the bigger picture: MonsterMQ and MonsterMQ-Edge share the same dashboard, same GraphQL interface. One central place to manage both – from the full broker down to the tiny monster running on your panel. 😅
Let me know if you want it as an Edge App, which can be deployed on the Panel. But remember: you need an Edge License to run Edge on the Panel.
MonsterMQ-Edge still has limited functionality compared to the full broker – but it’s growing.
#MonsterMQ #WinCCUnified #EdgeComputing #MQTT #OpenSource
🧌 A tiny MonsterMQ is running on industrial panels!
It turns your existing panels to a MQTT enabled device!
MonsterMQ-Edge is a lightweight MQTT broker – the Docker image is just 36MB. I have poured it into a SIEMENS Industrial Edge App…
Here’s what it does on the panel:
- 👉 Connects to the WinCC Unified Runtime
- 👉 Configurable tag publishing with wildcard support
- 👉 Configurable from a central MonsterMQ dashboard
- 👉 Store and forward to other MQTT brokers
- 👉 Archive data to Postgres or MongoDB
- 👉 … more will come …
Btw.: you can do the same with the full version of MonsterMQ with WinCC Open Architecture or with WinCC Unified running on the PC.
🔗 monstermq.com 🔥 SIEMENS
Disclaimer: experimental state. But it is cool to see a tiny Monster running on a SIEMENS Panel.
#MonsterMQ #WinCCUnified #EdgeComputing #MQTT #OpenSource
🧌 Weekend project: a lightweight MonsterMQ broker for the edge!
Built in Go, based on another open-source project (Mochi MQTT), implemented to expose the same GraphQL interface as the full MonsterMQ broker and having the same storage backend (SQLite, Postgres, MongoDB).
What does that mean in practice? You can run a lightweight MonsterMQ instance at the edge and monitor and configure it from the same MonsterMQ dashboard and having the data in the same storage format. No separate tooling needed.
Current state:
- 👉 Single binary 25M
- 👉 In memory last value storage
- 👉 Archiver for SQLite, PostgreSQL and MongoDB.
- 👉 MQTT Bridge available to pub/sub from/to other brokers.
- 👉 Backend storage options: SQLite, Postgres or MongoDB
- 👉 Same GraphQL interface – compatible with the existing dashboard
Very early stage, and just an experiment for now. What do you think about it?
#MonsterMQ #MqttClaw #MQTT #Edge #EdgeComputing #Go
🦞Little Monsters are crawling in MonsterMQ!
MonsterMQ can run AI Agents – triggered by MQTT topics or on a schedule, with direct access to broker data in the context, and support for MCP Servers (including the internal one).
The agent can publish data to other topics – and that can trigger the next little agent. 🔄
Is this a good idea? Honestly, I don’t know yet – this is purely for learning right now.
🧌 What has MonsterMQ become?
I started this as an open-source MQTT broker, to learn MQTT. But somewhere along the way it grew into something bigger…
Connectivity:
🔄 MQTT Bridging
📨 Kafka Bridging
🔌 OPC UA Client & Server
⚙️ PLC4X (PLC connectivity)
🏭 WinCC OA & WinCC Unified Clients
🚀 NATS Client & NATS Protocol Server
Processing & Transformation:
🕸️ Neo4J integration
⚡ SparkplugB Decoder
🔄 Workflows for data transformation
🕸️ Clustered and distributed setup
Archiving & Storage:
💾 Last value storage: in-memory and databases, infinite retained messages
🗄️ Internal database archiving: MongoDB, Postgres, Timescale, SQLite
❄️ Logging to QuestDB, MySQL, Postgres, Timescale, Snowflake
APIs & Interfaces:
🔍 GraphQL
📡 MQTT API
🤖 MCP Server
📊 Prometheus
🏭 i3x / CESMII
🔜 REST API — coming soon!
An integrated topic browser, visualizer, and AI analyzer. 💡
👉 MonsterMQ.com
MonsterMQ got a NATS Bridge!
The broker now has a NATS bridge integrated – you can receive messages from NATS and also publish to NATS.
For example, with simple wildcard subscriptions you can bring your topics straight into NATS – no extra glue code needed!
A big thanks to Kevin Joosten for this contribution!
‼️ And I added that MMQ can act as a NATS Server (NATS core 1.0). So, now NATS clients can connect to MonsterMQ and pub/sub to topics. NATS protocol is super simple.
This opens up some new integration scenarios where MQTT and NATS need to coexist in the same architecture.
Star it if you like it – and drop me a message if you’re using MonsterMQ in your setup!
MQTT Broker with i3x? – MonsterMQ got it!
A i3x server integrated directly into MonsterMQ!
What’s happening under the hood:
👉 MQTT topics are stored in memory
👉 Historical values are persisted in MongoDB
Those features have been available in MonsterMQ for a long time…
The i3x interface just exposes all of that data via the i3x protocol!
💡 This means you get a full MQTT broker with structured, queryable data access via i3x – all in one place.
WhatsApp messages from WinCC Unified
Someone asked me if it’s possible to send WhatsApp messages from WinCC Unified 💬
Yes, and it’s simple! A small Python helper connects to the GraphQL server of WinCC Unified, subscribes to a tag to get the message text, and forwards it to WhatsApp via Twilio. That’s it. And for sure, the same can be done with WinCC Open Architecture.
Available here: https://github.com/vogler75/winccua-twilio
LLM based voice Interaction in WinCC OA!
At our USA Tour, someone asked about voice interaction with WinCC OA – and luckily, I had exactly that showcase in my demo.
I recorded a quick demo (not perfectly, sorry), but it still shows how easy it is to bring LLMs into WinCC OA: capturing voice directly in the browser, sending it to an LLM via simple HTTP REST calls, and letting the system respond or act.
WinCC OA is open and flexible – that’s why use cases like this can be built so quickly.
And excuse the not-so-nice UI – I built it myself 😄
🎥 Check out the short demo!