The Gemini AI CLI just got a massive update — and it’s one of the most important upgrades Google’s released this year.
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If you’re a developer or builder using AI tools, you’re probably wasting hours every single day.
Switching between tabs. Copy-pasting code. Restarting sessions. Losing context. Testing the same prompts over and over again.
Meanwhile, the smartest engineers are building entire apps without leaving their terminal.
How?
They’re using the Gemini AI CLI — Google’s command-line interface for its most powerful AI model.
And with the new Gemini AI CLI 0.24.0 update, everything just got faster, simpler, and smarter.
This isn’t just a small patch. It’s a complete rework of how developers use AI from the terminal.
What Is Gemini AI CLI?
The Gemini AI CLI is Google’s command-line interface that brings the Gemini AI model straight into your terminal.
It’s like having the full power of Google’s AI right inside your shell — no browser, no context switching.
You can code, debug, analyze files, and even search the web — all without leaving your command line.
The best part? It’s open-source and free to start.
You get 60 requests per minute and 1,000 requests per day using your personal Google account.
So you can test, build, and automate without paying a cent.
With version 0.24, the Gemini AI CLI has evolved from a productivity tool into a true AI development environment.
What’s New in Gemini AI CLI 0.24.0
Let’s break down what actually changed — and why it matters.
1. Google Cloud Monitoring Dashboards
This is huge.
You can now track your team’s usage and performance directly inside Google Cloud.
The Gemini AI CLI now ships with preconfigured OpenTelemetry dashboards that show everything from:
- Monthly active users
- Lines of code changed
- Token usage and API calls
- Tool frequency
- Model performance
You don’t have to write a single query.
Just enable OpenTelemetry, export your project, and the dashboards auto-populate.
You can even export metrics to Prometheus, Datadog, Jaeger, or any backend.
That means you’re not locked into Google’s ecosystem — everything is transparent and customizable.
This gives you visibility into how your team uses the Gemini AI CLI.
You can see who’s most efficient with tokens, who’s over-consuming, and where performance dips.
It’s like real-time analytics for your development process.
2. Model Persistence
Simple, but brilliant.
Before this update, every time you opened the terminal, you had to reselect your model.
Now, with Gemini AI CLI 0.24, your preferred model is saved across sessions.
Once you pick it, the CLI remembers it — globally or per project.
It’s a small detail, but it saves constant clicks and setup time.
If you’re working across multiple projects, you can even create project-specific defaults inside your settings.json.
That’s fewer interruptions, more flow.
3. Settings UI Overhaul
If you’ve ever broken something in a CLI config file, you know the pain.
The new Gemini AI CLI settings page now includes detailed descriptions for every setting.
Each toggle tells you what it does, when to use it, and what happens when you change it.
Everything’s grouped by category — UI, tools, security, and memory.
No more guesswork. No more digging through docs.
Now, you actually understand what you’re configuring.
That’s developer UX done right.
4. Autocomplete for Folders and Multi-Directory Support
You can now use autocomplete when adding directories via /dir add.
Start typing a path, and it instantly suggests matching folders.
Hit tab, and it completes automatically.
If you’re working on microservices or multi-repo projects, this is a game-changer.
You can now register multiple project directories and switch between them seamlessly.
That means the Gemini AI CLI finally matches how real development teams actually work.
No more copy-pasting folder paths. No more mental overhead.
5. Shell Output Efficiency
Another killer update.
When you run shell commands like npm install, verbose logs can eat thousands of tokens.
Now, with the Gemini AI CLI’s Shell Output Efficiency setting, you can control what gets sent to the model.
Turn it on, and the CLI automatically encourages “quiet” flags like --silent or redirects massive outputs to temporary files.
That keeps your context clean and token-efficient.
You only pay for meaningful data — not for log spam.
That’s real savings in both time and money.
6. Collapsible Image Previews
The new image collapse feature keeps your workspace tidy.
When you reference or attach images, they now appear as compact previews rather than full-size blocks.
You still see what you need, but your workflow stays clean and fast.
It’s a subtle change that makes a big difference when working with visual inputs.
7. The Choicely Extension (And Why It’s Wild)
The Choicely extension is where the Gemini AI CLI truly crosses into full app development territory.
It lets you build, deploy, and test native mobile apps — right from your terminal.
Not web wrappers. Actual iOS and Android apps.
Install it with:
gemini extensions install <choicely GitHub URL>
Then tell the CLI what app you want — for example, “shopping app with Firebase” or “maps-based app with in-app purchases.”
The extension clones the SDK demo, configures app keys, and handles the full build and deployment process.
You can even test on connected devices using Android ADB or Xcode.
This means developers can prototype, ship, and test without ever touching Android Studio.
And it’s not hypothetical — companies like Eurovision Song Contest and Arsenal Fan TV are using Choicely-powered apps with millions of users.
That’s enterprise-grade capability baked into a command-line tool.
8. Extension Ecosystem
Extensions are now the backbone of the Gemini AI CLI.
You can install tools for:
- Google Workspace automation (Docs, Sheets, Slides, Gmail)
- Flutter app development
- Cloud database operations (BigQuery, Firebase, CloudSQL)
- Security scanning and code reviews
- Stripe payments and subscriptions
- Jira ticket management
Every extension adds its own commands and configs — like /stripe manage or /jira sync.
And because it’s open-source, you can build your own internal extensions.
Bundle your company workflows into one package and share it across your team.
That’s how you scale efficiency and collaboration.
How Teams Use Gemini AI CLI
If you’re working solo, the Gemini AI CLI streamlines your workflow.
If you’re on a team, it multiplies your results.
Here’s how:
- Set up monitoring dashboards to track usage and spot inefficiencies.
- Enable model persistence so everyone’s on the same version.
- Use autocomplete to jump between repos instantly.
- Install extensions for your stack — mobile, backend, or analytics.
- Run security scans before every push to production.
Everything happens inside one environment. No context switching. No wasted time.
That’s why dev teams love it — it reduces friction and lets you focus on building.
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Advanced Gemini AI CLI Tips
1. Enable Shell Efficiency Mode
This will drastically reduce your token use. Run quieter commands to save processing cost.
2. Use Multi-Directory Mode for Microservices
If your project has multiple repos, register them all. You’ll move faster and keep context in sync.
3. Create Custom Extensions
Bundle repetitive workflows — like testing or deployment — into your own CLI extension. Your future self will thank you.
4. Connect to Google Cloud Dashboards
Monitor your token usage and performance in one place. Know exactly where time and resources go.
5. Update Weekly
Google ships new preview builds every Tuesday. Stay current — small updates often bring big improvements.
Why Developers Are Switching to Gemini AI CLI
Here’s the bottom line.
The Gemini AI CLI brings Google’s best AI features directly to the command line — where real work happens.
It’s faster than browser-based tools. It’s open-source. It’s customizable.
And it’s the first CLI built around real developer workflows — not demos.
You can code, debug, analyze, and automate without ever touching a browser.
If you’re serious about speed, focus, and performance, this is the new standard.
FAQs About Gemini AI CLI
1. What is Gemini AI CLI?
It’s Google’s official command-line interface for the Gemini AI model — letting you use AI directly from your terminal.
2. Is it free?
Yes. Free to start with generous usage limits. You can scale up via Google Cloud credits.
3. Can I build mobile apps with it?
Yes. Install the Choicely or Flutter extensions to create full native apps from your terminal.
4. What languages does it support?
Anything your shell runs — JavaScript, Python, Go, Rust, and more.
5. Is it safe for teams?
Yes. All configurations and telemetry data are secure and exportable.
6. Can I use it offline?
You can run local tasks and connect once online for AI inference.
7. Does it work with VS Code or IDEs?
Yes. You can call it from any terminal inside your editor.
8. How do I install it?
Run:
npm install -g @google/gemini-cli
or update with:
npm update -g @google/gemini-cli
Final Thoughts
The Gemini AI CLI update isn’t just another AI release — it’s a complete workflow shift.
It’s the difference between jumping across five apps and doing everything in one.
It’s faster, cleaner, and built for real developers.
With model persistence, dashboards, and extensions, you can now code, test, deploy, and monitor — without leaving your terminal.
Start small.
Install the CLI.
Set your defaults.
Try it on one workflow — code reviews, debugging, or docs.
Then watch how much time you save.
Because the future of development isn’t about learning new tools — it’s about building smarter ones that remember how you work.
And that’s exactly what the Gemini AI CLI was made for.
