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.
Top comments (1)
This is a great example of how combining older hardware with modern AI capabilities can create something much more interesting than the original product vision.
What stands out is the architecture approach: keeping the edge device lightweight while moving intelligence to cloud-based AI services. That pattern is becoming increasingly common in real-world AI systems.
A few engineering considerations that become important in projects like this:
Managing latency between the device and AI services
Handling offline or degraded connectivity scenarios
Keeping API costs predictable with smart request management
Designing clear boundaries between device control and AI reasoning
Adding safety constraints before allowing AI to trigger physical actions
The interesting future direction is where these systems become more autonomous — combining local perception, small models running at the edge, and larger models for complex reasoning.
Projects like this show that AI is not only about chat interfaces. The real opportunity is embedding intelligence into existing devices and creating new interactions.
Great work bringing old hardware back to life. 🚀