Series · 7 parts
The LLM Playground
- 1
Building an Instrument, Not a Model: Why My First ML Project Was a Playground
Why I built an LLM playground instead of training a toy model, and what the architecture teaches before any experiment runs.
- 2
Watching a Model Fail on Purpose: Tokenization and the Decoding Tradeoff
How text becomes tokens, how probability distributions become text, and what happens when you push both to failure on purpose.
- 3
The Same Model, Before and After: What Post-Training Actually Changes
A controlled comparison of a base model and its instruction-tuned sibling, testing whether assistant behavior is installed or emergent.
- 4
Who Judges the Judges? Small Models Grading Small Models
Using small models as evaluators for other small models, and finding out how much you can and can't trust the judge.
- 5
It Works on My Laptop: The Distance Between Running and Shipping
The gap between an application that runs and one that ships, written from an actual deployment rather than a plan for one.
- 6
The GCP Deployment Runbook: Every Command, Every Fix
The exact commands, in order, including every failure and fix, for deploying a FastAPI + React app to Cloud Run.
- 7
What Building an LLM Playground Actually Taught Me
Every claim the series made, in one place, stated plainly and traced back to the experiment that supports it.