After spending weeks obsessing over scaling laws and raw TFLOPS, I decided it was time to move up the stack. It’s one thing to have a powerful model; it’s another to have an Agent that knows how to use it. I took the architecture described in my recent overview of AutoMind AI Agent — an adaptive agent for automated … Read more
After building my dual-RTX 4080 rig (which I covered in my previous post), I felt like a kid with a supercar stuck in a school zone. It was time to take it to the track. I decided to reproduce the foundational 2020 OpenAI paper: “Scaling Laws for Language Models.” Why this paper? Because it’s the “Old Testament” … Read more
After publishing my overview of the LLM Scaling Laws, I was left with a nagging question: Does this actually hold up when you aren’t training on a massive cluster? Theoretical comprehension is one thing, but as I’ve discussed in my previous posts, Implementation-First Research requires getting your hands dirty. So, I decided to take my local Ubuntu workstation — … Read more
Let’s cut through the hype: most AI research assumes you have a massive budget, but in my homelab, reality is measured in GPU temps and Python execution speed. I’m stripping away the fluff to see which ‘frontiers’ actually matter when you’re running on bare metal. Let’s see what’s worth our compute cycles. If you’ve spent any time reading my thoughts over at AI … Read more