Bringing My Son’s Anki Vector Back to Life with Raspberry Pi, WirePod, and Gemini AI

A developer revived an Anki Vector robot from 2018 by replacing its defunct cloud services with a Raspberry Pi running WirePod and a local AI model, Gemma 4 12B via Ollama. The robot now responds to open-ended queries without relying on external servers, demonstrating how older hardware can be upgraded with modern open-source AI.

A few years ago 2019 , I bought my son an Anki Vector robot as a gift. For anyone who never owned one, Vector was way ahead of its time. He was a small desktop robot with a surprising amount of personality. He could recognize faces, explore his environment, respond to voice commands, take pictures, answer questions, and genuinely felt different from most other smart devices. He wasn't just another gadget sitting on a desk. He felt alive. Then Anki went out of business. Like many Vector owners, we were left wondering what would happen to this little robot we had brought into our home. Later, Digital Dream Labs acquired Vector and attempted to continue supporting the platform, but after more uncertainty around the service, it became clear that depending on someone else's cloud was always going to be a weakness. The hardware still worked. The personality was still there. Vector just needed a new brain. Years later, with the rise of open-source projects and modern AI models, I decided to see if I could bring him back. The goal: Could I take this older robot, replace the cloud services he depended on, and connect him to a modern large language model? The answer ended up being yes. Using a Raspberry Pi, WirePod, and Google's Gemma 4, I was able to bring Vector back online and give him capabilities that were not possible when he originally launched. The Stack For this project I used: Anki Vector robot Raspberry Pi running WirePod Desktop PC running Ollama Gemma 4 12B open model Linux SSH Local networking The final architecture looked something like this: User | Voice Command | Anki Vector | WirePod Raspberry Pi | Local Network | Ollama Server | Gemma 4 12B | AI Response | Vector The Raspberry Pi essentially became Vector's replacement backend. Instead of reaching out to external cloud services, Vector now communicates with WirePod. WirePod handles the request and routes conversations to a locally running AI model hosted on my own hardware. The robot from 2018 is now powered by a modern LLM without relying on a company's cloud infrastructure. Step 1: Taking Back Control of the Hardware The first challenge was not installing AI. It was getting control of the robot again. Vector was originally designed around cloud connectivity. The hardware itself was still impressive, but many of the features depended on servers outside of the owner's control. When those services became unreliable, it showed one of the biggest problems with connected devices: The hardware can be perfectly fine, but the product can still stop working. Using WirePod allowed me to replace that missing backend. The process involved: Setting up the Raspberry Pi Installing WirePod Connecting Vector Configuring the replacement server Getting voice commands routed correctly After that, Vector was responding again. But I wanted to go further. Step 2: Giving Vector a Local AI Brain Getting Vector back online solved one problem. The next question was: Could this little robot become something closer to the AI assistants we imagine today? Originally, Vector's responses were limited by what he was programmed to understand. He had a lot of personality, but he was not built with today's large language models in mind. I decided to connect him to Gemma 4 12B running locally through Ollama. Now when I ask Vector something open-ended, the request travels from the robot, through WirePod, to my local AI server. For example: "Vector, explain how black holes work." Instead of returning a predefined response, Gemma generates an answer and sends it back through Vector. No subscription. No external AI API. Just my hardware running my AI model. Challenges Along the Way Like most projects, it definitely did not work perfectly the first time. Some problems I ran into: Bluetooth pairing issues Getting Vector activated again Raspberry Pi setup SSH configuration Networking between devices Model configuration Debugging why requests were not reaching the LLM Balancing model size and response speed One of the biggest lessons was realizing how many different pieces have to work together. A simple voice question goes through the robot, networking, a replacement backend, an AI server, the model, and then all the way back. When something fails, troubleshooting means understanding every layer. Why This Project Matters This started because I wanted to bring an old robot back to life. But it turned into a much bigger lesson about AI, ownership, and the future of technology. How many smart devices stop working because the hardware failed? And how many stop working because the servers behind them disappeared? Vector still had working motors, cameras, sensors, and personality. He just needed a new brain. Open-source software and local AI made that possible. What's Next? Now that Vector is running with a modern AI backend, there are a lot of possibilities: Persistent long-term memory Custom personality tuning More natural conversations Home automation integration Computer vision experiments Agent-style workflows This project started as restoring my son's old robot. It ended with a small glimpse into where personal AI devices could be heading. Sometimes old hardware just needs a new brain.