When I handed my workspace over to Multica back in May, I was happily picturing how letting AI take on more of the work would mean fewer late nights for me. The work did get done — but my token bill went through the roof. The other day I pulled a three-month usage report for the workspace out of curiosity, and I had to double-check that I was reading it right: about 38M tokens in May, jumping to nearly 500M in July, with a three-month total of 883M. So here’s a data-driven look at how my token consumption exploded over those three months — and why. Because my AI tasks have gone from “trying it out” to “can’t live without it”.

To do some small-scale model training at work, I eventually recommended buying a Mac Mini. The reasoning wasn’t complicated: I needed it for bioinformatics analysis, running agent deployments, and occasionally training models with modest parameter counts — these scenarios are exactly where M-series chips with unified memory shine. The cost of separate RAM plus a large-VRAM GPU far exceeds the Mac platform; do the math and the choice is clear.

And just like that, I unlocked the achievement of pushing forward multiple devices, multiple platforms, and multiple projects all at once.

I’ve been trying to use AI Agents / workflows to build efficient agent teams or AI workflows to improve productivity. After two weeks of experimentation, I’ve encountered several pitfalls…