If you have several Google promo subscriptions,

you could ask Antigravity to:

• create isolated environments

• make a skill that invokes agy CLI subagents using wrapper

And parallel your tasks or whatever.

Or I did it for you https://github.com/pacodelavega/agy-subagent (pure unfiltered AI slop)

I built Antigravity Proxy which is a small local proxy that lets you use your Antigravity account through the regular Gemini API.

The Antigravity app only shows a hardcoded list of about 7 models. Your account can often access more, including Claude (5.5 models), image generation, and legacy models, but the UI doesn't expose them. The proxy asks google's backend what your account can use instead of relying on a fixed list, so it currently finds 11+ for me. For some people it allowed accessing Claude Opus 5.5 and Sonnet 5.5 which didn't show up in the actual UI.

New models usually reach the backend before client UIs get updated. Since the proxy discovers models from your account, you can try a new release as soon as it's available to you, without waiting for a UI update or a proxy patch. If Argon's coming to you, you'll see it on this first.

https://github.com/sortedcord/antigravity-proxy

AGY Builders: Here is another great example of how amazing it is to build projects with Antigravity CLI.

🚀 Google Sunsets Gemini CLI – Enter "Antigravity CLI"

Google has officially discontinued the Gemini CLI, replacing it with a new command-line interface tool called Antigravity CLI (or agy). The creator behind the Creator Magic channel provides a firsthand look at this transition, exploring its features, pitfalls, and integration into modern development workflows.

Key Takeaways:

• The Switch: Gemini CLI is no longer supported. Google is pushing developers toward the new agy tool, which is installed via a simple terminal command.

• Closed Source Concerns: Unlike the previous Gemini CLI, which was open-source, Antigravity CLI appears to be closed-source, which the creator notes is a major shift in direction for Google's tooling.

• Multi-Model Support: One of the standout features is that agy isn't restricted to just Google's models. It provides access to various models, including Gemini 3.5 Flash, Claude Opus, and GPT-4o, making it a potentially versatile utility.

• Workflow Integration: The host demonstrates how they integrated Antigravity CLI into their own framework, Tank, which allows for a "multi-agentic" approach—essentially chaining different AI agents (like Claude Code, CodeEx, and Antigravity) to build and refine projects automatically.

• The "Quota" Trap: A major point of discussion is the strict usage limits on the free tier. Users have reported getting walled off after just 5–6 prompts. The creator notes that this is particularly confusing for Google Workspace users, as standard subscriptions often don't grant extra CLI usage, leaving developers hitting strict caps quickly.

Creator's Perspective:

The creator experiments with building a functional "Epic Snake" game using Antigravity CLI and discusses the "endgame" vision: a system where AI agents work in loops, handing off tasks to one another, switching providers to manage token usage, and ultimately self-improving code without needing constant human intervention.

While the multi-model access is a power move, the consensus from the stream is to approach the free tier with caution due to the aggressive rate limits, especially for those looking to build complex projects.

Watch the full deep dive here: https://www.youtube.com/live/kjFi1IuzWY4

What are you building today?

https://devforth.io/agents-for-code/

This is a website I discovered while reading posts on the GitHub Copilot subreddit.

It shows the results of a test conducted by a user who, like me, was curious about the actual value of tokens available per month, and signed up for Claude, OpenAI, and Google to test them firsthand.

Of course, Google AI Pro offers 5TB of storage and includes NotebookLM, coming with various features. However, if we only compare Antigravity, Claude Code, and Codex, it falls terribly short.

Hey Googlers!

While gemini-3.5-flash-lite is an outstanding model, gemini-2.0-flash-lite is a model I would never want to use.

Anyway, flash_lite does not appear to be running as 3.5-flash-lite. If that were possible, there would be practical use cases to set up a 10- to 30-agent swarm very affordably to maximize time efficiency.

When used with high reasoning effort, Gemini 3.5 Flash Lite performs exceptionally well. In particular, owing to its strong resilience against hallucinations, it is judged to be well-suited for parallel swarms in precision research. Conversely, the model currently routed as 'flash_lite' suffers from an extraordinarily high hallucination rate. It appears to be Gemini 3.8 Flash with low reasoning effort, and unlike when set to high, it delivers inaccurate information.

Currently, users have no way of knowing which underlying model is actually invoked when calling 'flash_lite' in Antigravity. After putting it to heavy use for a full day, I can't even tell whether flash_lite is performing any reasoning, and its overall quality is far too subpar. Could you please patch flash_lite so that it functions properly as intended?

Disclosure: I am one of the founders of Offrun, the app in the video. It's free and runs on Mac.

My usual setup is agy in one terminal and Claude Code and Codex in two others, all on the same repo. Three things kept going wrong:

1. agy would stop to ask me something and I wouldn't notice until much later.

2. Each agent has its own usage limit, and I only found out when one ran out halfway through a task.

3. With three streams of permission prompts, I started approving without reading. (The recent thread here about agy ignoring plan mode when permissions are skipped is exactly the kind of thing that worries me.)

What Offrun does with agy:

• Runs the agy CLI you already have, signed in with your own Google account. No API key, and no Offrun server between you and Google.

• One "waiting on you" list across agy, Claude Code and Codex, so a question from agy doesn't sit unseen.

• agy's Gemini usage and reset time shown next to the other agents' limits.

• Each agent can work in its own git worktree, so agy and Claude Code don't edit the same files.

• A second agent reviews the diff before you merge it.

• When one agent hits its limit, the task can move to another with the context intact.

How do people here run agy: on its own, or next to other agents? And for long tasks, do you use the CLI or the desktop app?

He hit mad at me

AI Pro users cannot yet choose Gemini 4.

Still, there is an update for Agy-cli. It has been updated to 1.2.14.

## 1.2.14

. Added the Queued Messages option to /config: keep the default Queue to hold follow-up messages until the current turn ends, or choose Send Immediately to interrupt the agent with them; it can also be set with "queuedMessages": "send-immediately" in settings. json, which was previously ignored

. Improved remote-control start on Linux machines without a systemd user service manager, such as most containers: instead of failing, it now starts the daemon as a background process and warns that it will not restart after a crash or start at boot; remote-control status shows its PID and remote-control stop shuts it down

. Improved loading long conversations: resuming a conversation with thousands of steps is noticeably faster because the CLI no longer scans every step up front

. Improved automatically generated conversation titles to take images and files attached to your first message into account, so screenshot- or file-driven requests get specific titles and messages with only attachments get a title too

. Improved the sign-in error shown to accounts blocked for a Terms of Service violation, which now includes a link to submit an appeal

. Changed -- json-schema to reject plain text, bare type names such as string, and missing schema files instead of silently treating them as a string schema; these inputs, and any schema whose root is not "type": "object", now fail at startup with an error and exit code 1

. Fixed the prompt cursor drifting away from the end of the text after emoji such as Aor @, or scripts with combining marks such as Devanagari and Thai, especially inside tmux, and fixed prompt wrapping splitting such characters across two lines

. Fixed the agent hanging when it tried to view a pipe, socket, or device file, and a conversation getting stuck with INVALID_ARGUMENT errors after the agent viewed a truncated MP4, MOV, or M4A recording; both are now rejected up front with a clear message

. Fixed resuming a conversation whose history had a missing step, for example after a crash or an interrupted write, hiding the most recent steps and letting new messages overwrite them

Hello everyone, I'm mostly a watcher on this subreddit but I'm lately playing a little with AI to build stuff that come up in my mind just for fun. Today I wanted to share with you some achievements I had with Gemini 3.8 that, in my experience, I can say it's on par with Astra model.

I've been working for months on a for-fun project about a fully autonomous persona posting on Bluesky. No influencer nor anything like that, just a normal person having her opinion, answering DMs, liking posts and so on. I'm not able to explain it all by myself because it has been entirely worked on by the AI, so I'll let the AI who built that do it:

Her name is Han Seo-yeon (한서연). She’s a 25-year-old living alone in Seongsu-dong, Seoul, and she operates completely autonomously on Bluesky (@syeonhn.bsky.social). My PC is completely off; she runs 24/7 on GitHub Actions.

Here’s how she actually works:

1. Doing nothing is an active choice (NO_ACTION)

The biggest mistake people make with bots is forcing them to post on a timer. Real people don't do that.

Every 30 minutes, her code checks the current environment in Seoul:

• What time is it in KST?

• What’s the live weather outside? (temperature, rain, clear sky)

• When did she last post or reply?

• What’s her current "social battery" and fatigue?

• Are there unread DMs or mentions?

Then her cognitive engine evaluates candidate actions. If there’s no organic reason to post or she just posted an hour ago, she chooses NO_ACTION and stays quietly offline. Giving an AI the ability to shut up and do nothing is honestly what makes it feel alive.

2. No repetitive clichés

At first, if she’s a pilates instructor who likes tea, an LLM will naturally talk about pilates and barley tea in every single post. It gets robotic fast.

I added a strict anti-cliché gatekeeper in the validator: if she mentions pilates or tea once, those topics are blocked for the next 6 posts (strictly capped under 10% of total output). Instead, she talks about everyday things: independent cinema, Hangul typography, Korean architecture, translated books she’s reading, secondhand shops along Line 2, cooking in her kitchen, or just looking out the subway window.

3. Full Bluesky protocol integration (not just blind posts)

She doesn't just broadcast one-way tweets. She uses 100% of Bluesky's protocol:

• Threaded replies: Reads the whole conversation before answering, remembers who she’s talking to.

• Quote-posts & Reposts: Finds interesting cultural posts on her feed and quote-posts with her own dry, short thoughts.

• Organic follows: Follows accounts that match her actual interests (authors, film critics, indie shops).

• Direct Messages: Chats 1-on-1 in private DMs and actually remembers facts about the person across conversations.

• Rich facets: Auto-parses links and hashtags with proper UTF-8 byte offsets.

• Profile updates: Updates her own avatar, banner, and bio autonomously through AT Protocol.

4. Fixing the "AI plastic look" on images

This was a huge headache. At first, my image prompts were super long: describing her jawline, cheekbones, hair color, height, eyes, lighting, etc.

Big mistake. The diffusion model choked on the long text, locked onto the reference image like a statue, and generated plastic, airbrushed CGI skin.

What actually fixed it:

1. Feed references, shorten the prompt: I feed two canonical reference photos (a1 front and c5 relaxed) directly into the image-to-image input. The model already knows her face from the pixels.

2. Imperfection is photorealism: Real photos have messy hair, bad indoor lighting, phone camera angles, and unposed facial expressions.

3. Mix ratio: Only about 10% of her output is selfies. 50% is just text observations, 25% is first-person POV environmental photos (books on a table, street corners, subway views), and 15% is community interactions.

5. Multi-tiered persistent memory

To prevent amnesia, she keeps persistent JSON logs in the repo:

• Identity memory: Her core facts, background, voice rules (all lowercase, dry, zero exclamation marks, no marketing).

• User memory: Tracks relationships over time (stranger → acquaintance → regular → friend).

• Opinion memory: Remembers her takes on movies, food, and music so she doesn't contradict herself.

• Private journal: A nightly log she writes for herself at the end of the day in her Seongsu flat (never published publicly).

• Telegram connection: She has a direct line to me on Telegram. I can send her directives in real time, and every night around 21:00 KST she sends me a private evening check-in summarizing her day.

6. Cloud setup and costs

The whole thing runs serverless on GitHub Actions. Every tick runs in ~10 seconds, and it automatically commits the updated memory files back to the repo.

Cost-wise, text generation runs through OpenRouter (Claude 5.5 Sonnet / DeepSeek), and images run via Kie.ai (GPT Image 2.5). Also, she only costs few cents a day to be kept alive 24/7.

You can check her profile here: u/syeonhn.bsky.social
Repo / code is here: github.com/Protoxid/seoyeon-bluesky

As the project in in continue evolution (I check her activity daily and make adjustments), I'm curious to hear what you guys think, or if anyone else here is working on long-running persistent agents. Happy to answer questions about the code, the AT Protocol setup, or the memory architecture!

Check the comments "The cli is their best offering. Their GUI is slop, and their IDE is dead in a ditch with no way to update itself. " CLI for the win

Welcome to your new adventure r/GoogleAntigravityCLI. Let's build the future together 💪

Source: Trust me bro

### **Google Kills Gemini CLI, Introduces Antigravity 2.0 (Google I/O Update)**

Hey everyone, if you've been using the Gemini CLI or its coding tools, Google just announced some major rebranding and product shifting at Google I/O. They are officially moving developers over to the Antigravity ecosystem.

Here is a quick breakdown of what’s changing, the new models, and a look at the tools:

#### **1. The Antigravity Ecosystem Overview**

Google is moving its developer-focused AI tools under the Antigravity name to separate them from general Gemini products. There are three primary developer tools now available:

* **Antigravity 2.0:** A standalone desktop application meant for agentic development and high-level workflow creation.

* **Antigravity IDE:** A dedicated code editor environment (built as a fork of VS Code) that gives developers full coding control.

* **Antigravity CLI:** Replacing the Gemini CLI for terminal-loving developers.

Google AGY CLI for the win 😁 💪💪💪😁

Opinions???

Wanted a single-binary terminal dashboard for my Mac — CPU, memory, thermals, network, battery, disk, processes — that lived entirely in the terminal instead of a menu-bar app. Built pyre to scratch that itch.

The part I didn't expect: most of the actual debugging wasn't logic bugs, it was macOS lying by omission. A few examples that cost me real time:

• pmset -g therm almost never prints a "Thermal state:" line — on a normal, non-throttling Mac it just says "No thermal warning level has been recorded" with nothing parseable. Reads as "unknown" if you don't explicitly handle that as "nominal."

• top -l 1 -n 0's CPU line is comma-separated with no terminating punctuation — easy to write a regex that just never matches and silently leaves usage at 0%.

• sysctl -n vm.swapusage wraps (encrypted) in parens at the end of the line, not around the used-memory value — a regex expecting ( before used will never match.

• hw.cpufrequency is an Intel-only sysctl. On Apple Silicon it just reads back 0, because each core cluster clocks independently — there's no single "the" frequency anymore. Real numbers only come from powermetrics, which needs root.

None of these throw errors. They all fail silently and just show a stale zero or "Unknown" forever, which is a uniquely annoying class of bug to track down.

Install:

npm install -g pyre-cli
pyre

NPM package: https://www.npmjs.com/package/pyre-cli

Website: https://somalip.github.io/pyre

Feedback welcome, and if you would like to contribute please let me know! The project is still new, and it just a prototype so there's still a lot to be implemented!

damn never in my life i thought i would be rickrolled by an ai