$1.6 million dollars in Bitcoin was drained from my account on July 29th in the Cold Card wallet hack.
My Bitcoin was in cold storage. My keys were on a ColdCard device kept in a safety deposit box that had never been connected to the internet.
This part's nerdy, but here's what happened:
Hackers discovered a vulnerability in the part of the hardware wallet code used to create seed phrases.
This allowed them to use AI to brute force guessing seed phrases.
I was at our cottage and heard about the hack today.
"No way this affects me." I thought.
I logged into Wasabi––software that lets me view my bitcoin wallets online.
Right away I saw lines of red transaction–withdrawals–and I knew.
From 9:36pm - 9:43pm on July 29th, every wallet I had had been emptied.
18.25245043 btc gone. That's just over $1.6 million dollars CAD.
Perhaps the hardest part about this is that I did everything right.
I never shared my seed phrase with anybody. My devices never touched the internet. Everything was kept in multiple safes and safety deposit boxes.
None of it mattered. All because the hardware that created the seed phrase originally had one line in their code from 2021 that had a vulnerability.
I'm filing a police report and a report with the Ontario Securities Commission. But I don't expect to recoup anything.
A part of me is trying to make sense of what just happened. Or try to figure out a lesson in it. I'm struggling. $1.6 million is a staggering amount of money to have stolen.
I guess all that I can think about right now is that I'm so damn happy that I'm an entrepreneur and that my earning potential is under my control. Mark my damn words. I'll recover.
Two lesser-noted points in the Jan '25 DeepSeek paper caught our attn at Superfocus dot ai.
1) They produced AI models small enough to run directly on consumer electronics devices but with surprisingly strong reasoning ability. 2) They did it with relatively fewer data samples.
We wanted to know if these smarter “on-device” size models are actually good enough to be useful? And how cheap of a chip could we run these models on?
Why does this matter? There are lots of situations where you can’t, or wouldn’t want to, use AI in the cloud via the internet.
Personal & family privacy (ex: your home). Child safety. Security. Data/IP privacy (factories). No or unreliable signal (commercial/defense drones). Low latency (robot/vehicle safety). No or outdated physical/cloud infra (industrial, warehouses).
Plus, you don’t have to pay compute costs for on-device AI!
We’re sussing out many of these use cases, but because of our shared interest in education, my co-Founder @hsu_steve & I immediately thought about kids & learning.
Below is a video of my daughter demo’ing our prototype AI reading buddy. We’ve rigged our little AI puck up to a pair of smart glasses from our homies at @MentraGlass . (h/t @caydengineer )
This may not be a big deal for huge models in the cloud running on GPUs. What’s significant is that we’re doing it with small models and completely on-device. No internet connection.
The AI sees what my daughter sees. It listens to her and follows along as she reads. And helps her when she needs it by speaking to her. All from small AI models that we finetuned, engineered and got running fast and accurately on a chip that costs less than $25.
It’s not quite a reading tutor yet, but perhaps you can see how it could become one? Or how on-device AI like this could solve other problems?
Would love to know what you think, good, bad, or other!
@wreckful242 @AkhiTweet @h_haltam Lol, shame on me for what? All I did was call out some lazy, basic and obviously ineffective Twitter dawah. Be better next time