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With the holiday break sitting in the middle of this release cycle, it's a smaller release than usual this month. But that hasn't stopped us continuing to make the FlowFuse Expert even more useful.

Most factories don't get built all at once. They evolve. Someone buys a robot in 2018. A conveyor system gets added when production ramps up. Last year, a vision system showed up because quality was slipping. Every piece from a different vendor, every piece with its own manual, its own software, its own way of doing things.

And somehow, it all has to work together.

If you've been using Node-RED in production, you already know the pain points. Flows get complex. Teams grow. Someone makes a change that breaks production at 2 AM. You need to roll back, but which version was working? Who approved this deployment? How do you keep your dev environment in sync with production?

When a critical motor bearing assembly fails after just 6 months—half its rated lifespan—maintenance teams face a fundamental question: "How long should this component actually last?"

A PLC (Programmable Logic Controller) is an industrial computer that continuously monitors sensors, executes control logic, and operates motors, valves, and equipment in real-time, serving as the reliable backbone of modern manufacturing and industrial automation.

The Five Whys is a root cause analysis technique where you ask "why" repeatedly—typically five times—until you identify the underlying systemic cause instead of just symptoms.

This release comes with some big updates for the FlowFuse Expert.

Building IoT systems means making hard choices about how messages flow through your infrastructure. Apache Kafka and MQTT couldn't be more different in their approaches. MQTT came from the world of sensors and resource-constrained devices. Kafka was designed to push massive data streams between backend systems. Getting this choice right early determines whether your architecture scales smoothly or hits a wall.

Legacy industrial devices communicate in bytes. Your temperature sensor doesn't send you {"temp": 23.5} - it sends you [1, 3, 4, 1, 44, 0, 200, 190, 125]. Those numbers are meaningless unless you know how to decode them.

When working with Siemens S7-1200 and S7-1500 PLCs, you’ll notice that TIA Portal creates optimized data blocks by default. Unlike the classic S7-300/400 controllers, optimized blocks do not use fixed memory offsets. Instead, the PLC compiler reorganizes the data internally for better performance and memory efficiency. This architectural change often creates confusion when trying to read PLC data from external systems.

You're generating terabytes of sensor data every day. Most of it is waste, I mean..

Not the measurements—those are fine. It's the packaging. Text formats wrap every reading in field names, quotes, and brackets. You're moving more formatting characters than actual data across your network.

Bad data quietly corrupts production analytics, triggers false equipment alarms, and causes automation systems to make faulty control decisions. Unlike system crashes, these issues go unnoticed until they've already propagated through your operations—affecting multiple processes and causing unexpected shutdowns or production anomalies.

Network outages happen. A fiber cut, a switch failure, or infrastructure maintenance can take your connectivity offline without warning. When it does, your PLCs continue operating normally—they don't wait for the network to recover.

This release unlocks several new abilities for our users, speeding your development time, easing management of Node-RED instances, providing a smoother path to adding large numbers of devices, and more. Let's dig in.

Most factory HMIs are still stuck in one place. Dedicated panels mounted next to equipment, or SCADA workstations in the control room. Need to check something? You're walking over there.

In industrial operations it's all about getting more out of the CAPEX already spent. Achieving higher efficiency means everyone needs to get data from a lot of different machines, have an understanding how these machines form lines and fit together, and holistically understand these as a group of assets that collectively can achieve more.

In production environments, labels are everywhere! Products have Serial Numbers and Lot Codes, Packages have Batch IDs and Dates. These are often critical to the processes for packaging, tracking, logging, inventory and so on.