Datasets:

rmems
/

Metis-SEMM-Latent-Telemetry

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 license: gpl-3.0
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 # Metis-OLMoE-Latent-Telemetry
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+The Real Origin of my Neuromorphic AI journey.  It started with frustration and a very personal need. I am 25, bouncing between majors — economics, finance, pre-med, computer science, and finally electrical engineering. Each switch came from the same feeling: I wanted to build things, understand systems at a deep level, and create something meaningful. But traditional classes and tutors were often hard for me as someone with ADHD. I struggled to grasp material quickly, and I hated how easily people got impatient when I needed things explained again. That’s when I realized I needed a better way to learn. I wanted a local AI tutor — something patient, always available, that could help me with heavy engineering concepts, Rust, Julia, and whatever else I was studying. AI like Gemini had already shown me how powerful a non-judgmental tutor could be. It never got frustrating. It just kept adapting. But my hardware couldn’t run the big models smoothly. The smaller ones weren’t smart enough for the kind of technical work I needed. So, I started thinking: what if I could build my own tailored AI tutor that actually ran well on my machine? Around that time, I made the switch from CS to EE. I wanted to understand hardware at a fundamental level. While exploring, I started mining on Dynex — not primarily for profit, but as an experiment. I wanted to see if I could turn the mining telemetry (power draw, temperature curves, clock behavior, memory pressure) into something useful. I had this idea: what if the AI could “feel” its own hardware limits? What if the telemetry became the AI’s heartbeat — a real signal of stress, adaptation, and constraint? That simple question opened the door to Spiking Neural Networks. SNNs mimic biological brains in a way that felt closer to how humans actually learn and recover — especially after my own concussion from 7th-grade football, which left me with years of invisible struggles: depression, social isolation, and the constant feeling that no one fully understood what was happening inside my head. I had even tried switching to pre-med during COVID because I wanted to help people with concussions and neurological injuries, but that path wasn’t right for me. SNNs felt like a different way to approach the same goal — building systems that understand struggle, adaptation, and resilience. So, the project shifted. What began as “I need a local AI tutor” became something deeper: creating an AI that doesn’t just run on hardware but learns from the real stress and behavior of that hardware—turning Dynex telemetry into a heartbeat. Using that signal to drive SNN quantization. And ultimately building Spikenaut — a pure, from-scratch spiking neural network. It wasn't perfect, yet it was all. There were still some gaps in my telemetry that I didn't even realize. I tried using different mining telemetry, tried creating my own HFT bot for telemetry, then I tried using mining sync node data. However, in telemetry data, there were at times zeros in value, after I had used it for training.
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 **Dataset for Spikenaut SNN Research**