When Parallel Agents Ran the Same Task Twice and Quietly Doubled the Bill — Field Notes on Measuring and Stopping Duplicates
The bill for our parallel agents came in about 1.9x higher than expected — because multiple workers were running the same task twice. These are field notes on measuring the duplication, stopping it with idempotency keys, and attributing cost per task.
How Far Can On-Device Inference Stay Free? Measuring the Line Between Foundation Models and Gemini
WWDC 2026 widened Apple Foundation Models' free tier, making on-device inference easier to reach for. But 'free, so everything on-device' hits cases where quality falls short. Here is how to decide the range you hold on-device and the range you pass to cloud Gemini by measurement, not guesswork.
After Gemini 3.5 Flash Became the Default, Route Flash and Pro Per Task
Now that Antigravity's default Flash is Gemini 3.5 Flash, leaving everything on Flash wastes accuracy and forcing everything onto Pro wastes time. Here is a two-axis decision table for splitting work between Flash and Pro, plus the routing setup to wire it into your agents.
Measuring the Break-Even Point Between Google AI Pro and Ultra — 14 Days of Quota Data from Parallel Agent Runs
Is AI Ultra ($100/month, 5x the Pro limits) actually worth it? A Python harness that aggregates daily quota consumption from agent logs, 14 days of real measurements, and a formula that converts wait time into money to settle the question.
Before Your Antigravity Agent Bill Goes Sideways: A Solo Developer's Forecast and Cutback Playbook
Discovering your monthly bill is an order of magnitude larger than expected is the first big trap of running agents. Here's the forecasting and cutback playbook I've built up as an indie developer, with concrete numbers, a working guardrail script, and the monthly review cycle I use.