Most edge computing projects fail because they get too bloated and hard to manage. I built Edge Veda to fix that.
The Core Idea: > Instead of giving an app a fixed amount of memory or CPU (which usually leads to crashes), Edge Veda uses 'Composite Budget Contracts'.
How it works:
* Smart Allocation: The app and the device 'agree' on a budget based on the device's Current Capabilities at that exact moment.
* Less Mess: It uses a simplified Project Structure so you don't get lost in the code.
* Easy Entry: I wanted a clearer Learning path for developers entering the edge computing space.
Our Roadmap is focused on making these 'contracts' scale across massive networks.
Is 'Budgeting' resources better than 'Limiting' them? I'd love your feedback.
The dynamic budget negotiation idea is interesting. One thing I've found working with on-device AI models is that the "budget" isn't just about what the device can handle in theory — it shifts meaningfully between runs on the same hardware.
Ran MobileNetV2 100 times on a Snapdragon 8 Gen 3 and got 83% latency spread. Cold start was 2.689ms, steady state median was 0.369ms. The first couple of runs are basically a different device thermally.
Curious how Composite Budget Contracts handle that kind of within-session variance. Do you re-negotiate after warmup or is the contract set once at initialization?
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[ 4.6 ms ] story [ 21.9 ms ] threadThe Core Idea: > Instead of giving an app a fixed amount of memory or CPU (which usually leads to crashes), Edge Veda uses 'Composite Budget Contracts'.
How it works: * Smart Allocation: The app and the device 'agree' on a budget based on the device's Current Capabilities at that exact moment. * Less Mess: It uses a simplified Project Structure so you don't get lost in the code. * Easy Entry: I wanted a clearer Learning path for developers entering the edge computing space.
Our Roadmap is focused on making these 'contracts' scale across massive networks.
Is 'Budgeting' resources better than 'Limiting' them? I'd love your feedback.