Arterialist
Joined 3 years ago
Karma
14
Submissions
12
Member since
Apr 5
Recent submissions
Comment · 6 hours ago
I'll measure and let you know later, would appreciate if can do measurements on your hardware too as I only have a few options to test on
Comment · 6 hours ago
there are few imo:
- trustless code execution on someone's hardware (kube32 supports gpio permissions, amongst other stuff) - your standard Kubernetes cluster can now reach the physical world natively - OTA updates at arbitrary scale - fault-tolerance in the physical world, orchestration of workloads based on physical proximity or other sensor data
those are just a few that come to mind
Comment · 6 hours ago
Hmm, have you tried k3s? It promises low ram, but it could still be too much, I'm not sure about your requirements. In the meanwhile I'll see if it can be pushed further, but I guess we both know the answer :)
Comment · 1 day ago
Hey guys, I had a few esp32 boards around for a while and always thought - how do we go beyond just one device, would it be possible to apply cloud-native thinking to distributed edge computing?
Result is Kube32. I got the cluster and a full-stack web app with real-time sync running on esp32 s3 dev board and cardputer - https://7f0c-180-74-71-21.ngrok-free.app/ (may go down) (should support at least a few concurrent users - break it)
You won't see one cool thing though, which is the hud pod that uses Cardputer as a keyboard that syncs keystrokes to all clients in real-time.
Honestly, it opens so many possibilities for distributed edge computing and multi-device systems. Let me know what use-cases do you think would benefit from kube32 the most.
Comment · 1 year ago
I've developed a model that incorporates STDP and neuromodulation under a single computational unit. A network of the neurons like these solves MNIST with ~96% accuracy without backprop in under 5 epochs of unsupervised feature extraction, or ~92% after just 1 epoch. I have integrated it into pytorch to use traditional benchmarking methods locally.
A GitHub repo with the non-pytorch implementation is available, along with tooling - https://github.com/arterialist/neuron-model
Comment · 2 years ago
Callegram team just released their free trial that allows their users to try out one of the paid features - Google Calendar synchronization - for free. All Telegram users can seamlessly view their meetings, get notified about them, and quickly jump into the call without leaving the messenger.