Jeremy Neely Text Jeremy

AI & Agents · Production

TokenX

Measures what each Hatz AI model really costs, then helps people choose.

Built for: AME staff and Secure AI clients choosing AI models

The model guide asking what you are doing and what matters most.
The model guide asking what you are doing and what matters most.

The problem

Hatz offers dozens of AI models but does not publish a per-model credit rate, and credits post in delayed batches. Without real numbers, people overspend or pick a cheap model that fails at the job.

What I built

I built a Python command-line tool that runs the same chat on every Hatz model and watches the credit meter to measure cost per 1,000 tokens. The results feed a plain-language HTML model guide that also ships inside Secure AI for Excel.

How it works

  1. Take a baseline reading of the credit meter.
  2. Run a fixed sample chat on one model.
  3. Wait for the credits to post and compute the rate.
  4. Retest suspect results and save progress safely.
  5. Merge costs with public benchmarks into the HTML guide.

Built with

  • Python 3.10+ (stdlib only)
  • unittest
  • Windows DPAPI
  • Hatz.ai REST API
  • Node.js
  • HTML/CSS/JavaScript

More of Jeremy's work