Google Cloud’s Post

Tools like the CLI become truly useful when they empower teams to build repeatable workflows, automate safely, and standardize how tasks are run at scale. The real opportunity is turning complex AI pipelines into auditable and maintainable infrastructure that any engineer or PM can rely on. Curious to see how this shapes broader operational standards for AI delivery.

I treat it as if I’m giving instructions to any human and generally end up with expected results. I haven’t experienced hallucinations because I’m a detailed explainer! But this is the case in my ordinary life. I “over explain everything” even to A.i 🤣

I’m curious which Gemini CLI capabilities you see becoming most valuable for interns and early-career developers over the next year.

I love using .MD files as local memory stores, very helpful especially when trying to keep track of thousands of files and facts as data points to keep in context

Thanks for setting this up! I've been really enjoying the learning process for Gemini CLI. How should we think about Gemini CLI and Antigravity? Should we think of them as mutually exclusive or are there some areas where whey work together?

In everyone’s opinion what’s the best computer for Gemini as far as projects?

What's your newest trick to get the most out of Gemini CLI?

non-seq. taylor i have been using gcloud cli and then have gemini just use that for loads of help for anything gcp. rarely have to use the interface. could you clarify what benefit the gcloud extension would offer vs. just having gemini use gcloud cli commands (which has been a gamechanger btw for dev ops stuff)

Gemini-2.5-flash won a game of tic-tac-toe against an algorithm … I totally missed it and messed up the experiment. Will give Gemini the opportunity to play against other models now.