Building a DIY Router for Off-Grid Living: What It Teaches Us About Embedded Intelligence

This DIY approach flips that assumption. You're trading convenience for control. And increasingly, I'm seeing this same trade-off show up in robotics and embedded AI systems.

The Control Problem

Here's something I've been thinking about lately. We talk a lot about AI capabilities, about what models can do, about benchmarks and performance metrics. But we talk much less about AI controllability, about whether the people using these systems can actually understand and modify them.

A Raspberry Pi router is fully controllable. You can see every process running, modify any configuration, add features that the original designers never imagined. When the maker wanted their router to also manage solar power, they just... did that. No permission needed. No API limitations. No subscription tier upgrade.

Contrast this with most smart home devices, or honestly, most commercial robotics systems. They're black boxes. They do what the manufacturer decided they should do, in the way the manufacturer decided they should do it. And when your use case doesn't quite fit the intended design? Tough luck.

I initially thought this was just a consumer convenience issue. But after reading more about industrial robotics deployments, I'm starting to think it's actually a significant barrier to adoption. Companies want systems they can modify, integrate, and maintain themselves. They don't want to call the vendor every time they need to change a workflow.

What This Has to Do With Humanoids

You might be wondering what a DIY router has to do with humanoid robots. Fair question.

The connection, I think, is about architectural philosophy. The Raspberry Pi approach assumes that the user is capable and should have full access. Most commercial robotics takes the opposite view: the user is a liability who should be kept away from anything important.

There's a reason for this, obviously. Robots can hurt people. Giving everyone full system access is genuinely dangerous in ways that a misconfigured router isn't. But I wonder if we've overcorrected.

Some of the most interesting humanoid projects I've seen recently are the ones that treat operators as collaborators rather than users. They expose more of the system's decision-making. They allow for local modifications. They assume that the people working with these robots might actually know something useful about their specific environment.

This isn't the dominant approach, to be clear. Most major humanoid manufacturers are moving toward more locked-down systems, not less. But the tension is there.

The Edge Computing Angle

There's another thread here worth pulling on. The DIY router runs everything locally. No cloud dependency. No internet connection required for basic functionality (apart from the Starlink connection itself, obviously). The solar control works even if the satellite link goes down.

This is edge computing in its purest form. And it's increasingly relevant for robotics.

We've spent the last decade assuming that intelligence should live in the cloud. Send your data up, get your answers back, repeat. But latency matters when you're controlling physical systems. Reliability matters when you're in environments where connectivity is spotty. And privacy matters when you're operating in someone's home or factory.

The Raspberry Pi router handles all its core functions with a few watts of power and zero external dependencies. That's not possible for every application, obviously. But it's a useful design constraint to consider.

I should know this better, but I'm not sure what percentage of current humanoid systems require constant cloud connectivity for basic operation. My sense is that it's higher than it should be. The trend toward on-device AI models (small language models, local vision processing) suggests that others share this concern.

The Modularity Question

One thing that struck me about the ZDNet piece was how modular the setup is. The router handles routing. The solar controller handles solar. They communicate, but they're not fused into a single inseparable system. If one component fails, you can swap it out without rebuilding everything.

This is... not how most robotics systems work. Humanoids especially tend toward integration over modularity. The argument is that tight integration enables better performance. And that's true, up to a point.

But modularity enables something else: adaptability. When your environment changes, when your requirements shift, when a component becomes obsolete, modular systems can evolve. Integrated systems often can't.

I think we're going to see more tension around this as humanoid deployments scale. The first wave of customers will accept whatever architecture the manufacturers provide. But as the market matures, as companies develop specific requirements and preferences, the demand for modularity will grow.

Whether manufacturers will meet that demand is another question.

The Skills Gap (and Opportunity)

There's something slightly uncomfortable about celebrating DIY projects like this router. Not everyone can build their own networking equipment. Not everyone should have to. The whole point of commercial products is that they abstract away complexity so people can focus on what they actually care about.

But here's the thing. The maker who built this router now understands their system in a way that no commercial product user ever will. They can diagnose problems. They can add features. They can teach others. They've developed genuine capability, not just the ability to use someone else's capability.

I see a similar dynamic in robotics. The companies that are deploying humanoids most successfully aren't just buying robots and hoping for the best. They're building internal expertise. They're modifying systems. They're developing their own tools and workflows.

This creates a skills gap, obviously. Not every company can afford to develop deep robotics expertise. But it also creates an opportunity. The companies that do develop this expertise will have a significant advantage as humanoid systems become more common.

What I'm Still Uncertain About

Honestly, I'm not sure how far this DIY ethos can scale.

A single maker building a router for their off-grid cabin is one thing. A company deploying 50 humanoid robots across multiple facilities is something else entirely. At some point, you need standardization. You need support contracts. You need someone to call when things break at 2 AM.

The question is where that point is. And I think it's further out than most people assume.

We're still in the early days of humanoid deployment. The companies doing it successfully are, almost by definition, the ones willing to get their hands dirty. They're the early adopters, the tinkerers, the ones who see a Raspberry Pi and think "I could build something with that" rather than "that looks complicated."

As the technology matures, the user base will broaden. And the systems will probably become more locked down, more standardized, more "just works." That's usually how these things go.

But I hope we don't lose the DIY spirit entirely. There's something valuable about systems that trust their users. About technology that invites exploration rather than discouraging it. About robots that can be understood, not just operated.

The Bigger Picture

A Raspberry Pi router for off-grid Starlink and solar control is, in the grand scheme of things, a pretty small project. But small projects sometimes reveal big patterns.

The pattern I see here is a tension between capability and controllability. Between systems that do more and systems that users can understand. Between the convenience of black boxes and the power of transparency.

This tension runs through all of embedded AI and robotics right now. And I don't think we've figured out the right balance yet.

The maker who built this router chose controllability. They gave up some convenience (commercial routers are easier to set up) in exchange for understanding and flexibility. That trade-off made sense for their situation.

For humanoid robots, the calculus is different. The stakes are higher. The complexity is greater. But the underlying question is the same: how much control should users have? How much should they want?

I don't have a definitive answer. But I think it's a question worth asking more often. Especially as these systems become more capable, more autonomous, and more present in our daily lives.

Because at some point, we're all going to be living with robots. And I'd rather understand them than just use them.

Aisha Patel · 3 hours ago · 7 min