I spent a weekend fine-tuning a model for my knowledge management app, designed to handle notes, PDFs, and presentations with Oracle Database 23ai’s vector search (see my management AI post). It aced testing on my RTX 5090 server, but on my M2 MacBook Pro? Barely usable. A query like “Summarize last week’s customer meetings and identify risks” took over a minute, leaving me staring at a spinning wheel while my coffee got cold. ...
After successfully fine-tuning ChefBot on cooking recipes, I wondered: Could I fine-tune an AI on my own management experience to create a personalized executive assistant? Imagine asking your AI: “Summarize last week’s 1:1 with Sarah and suggest coaching points”, and getting a response in your voice, drawing from years of team dynamics and decision patterns. All running locally, with complete privacy and no API costs. This is the story of how I built exactly that. Spoiler: I failed three times before succeeding, and the lesson wasn’t about hyperparameters. ...
Imagine an AI that coordinates your entire cooking process—faster, smarter, and without ChatGPT’s API costs. With my RTX 5090 workstation humming, I’m answering: Can a specialized language model outcook ChatGPT in the kitchen? Over the past few days, I’ve been fine-tuning Qwen’s 32B and 14B parameter models to create ChefBot, an experimental Specialized Language Model (SLM). Think ChatGPT for recipes, but tuned for cooking data and running without per-token API costs. ...