Fine tuning my first open LLM model
Disclaimer: This is a draft blogpost. I am dumping links, quotes and…
The LM Studio Model That Sent Me Down a Fine-Tuning Rabbit Hole
I was poking around LM Studio yesterday, sorted by most downloads, and…
Nanobot: Install and Setup
In this article, I'm going to document settting up nanobot on my…
AppleCare for Macbooks: Yay or nay?
AppleCare is insurance, no different than car insurance. The purpose of AppleCare…
How to access website deployed on Coder on your local laptop?
Install Coder Desktop. Approve any system permissions, login, and enable "Coder Connect"…
Always Switching: The Hidden Cost of AI-Assisted Momentum
AI introduced a new rhythm in which workers managed several active threads…
Strategic Ignorance in the Age of Claude Code
Funny and relatable, at least for me: Source: X.com
Understanding LLM Size, Weights, Parameters, Quantization, KV Cache & Inference Memory
How much RAM do you need to run a 30 billion parameter model? Why are there multiple versions of the same model at different file sizes? What does "8-bit quantization" actually mean, and how does it affect performance and/or precision? If you're running language models locally or planning to, understanding the relationship between parameters, weights, quantization, and memory is essential.
