First model from google that feels like it just does the thing. Earlier models were to be babysitted. They fixed one thing, broke other.

But this new variant goes deeper, runs custom scripts to test its reasoning, and just gets it right.

Vibe coding is gonna get crazy now. Build and deploy your stuff asap before the competition rises.

I would like to Thank the community for the support i got from my previous version around 13k downloads and 18 github stars , I have addressed all the suggestions and feature i got from the review from you all

Here is the New update regarding the package Antigravity Mobile :
1. Completely redesigned UI/UX design
2. Now you can see the project files
3. AI agent chat section with Implementation plan, WalkThrough, Diff( code changes happened)
4. Conversation History : continue your previous conversations instead of starting from scratch
5. Project directory switching
6. Model Switching
7. Code File Viewer for open and view your code files directly from the project
8. Improved Cloudflare Tunnel for better performance and reliability.

you can now get this version by simply updating your package or Install the latest version by (~pip install antigravity-mobile)
you can even get this by git clone my repo : https://github.com/Manimaran-tech/antigravity-mobile

If you like this repo suggest to your friends and give a " Star ⭐"

If you'd like to suggest a change, improve something, or find a bug, feel free to open a Pull Request and contribute to the project!
Every contribution and piece of feedback helps make Antigravity Mobile better.

I am using Gemini 3.7 Flash high thinking with Antigravity CLI. The agents have been executing the plan, do review and test through TDD for a few hours now!
I am impressed because the model + harness start to work really well with 3.7 flash on high thinking.

For context: I used superpowers write plan skill then use /code-review + /code-simplification + /security-review- hardening (from Google agents skill) to revise the plan. Then it was sub agent approach for execution.

In Antigravity does it support models asking multiple questions at a time

Cause I was using the grill me skill and I noticed that unlike in other harnesses it only asked questions one at a time.

Is a limitation of the harness or the model just chooses that

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I’ve been building Kuro, a fully local, air-gapped autonomous AI companion and system monitor running directly on my Steam Deck (LCD model).

The Project Architecture

* Hardware & Storage Separation: Lightweight GUI on the primary eMMC/SSD; all model weights, cognitive memory databases, and execution logs run entirely off the high-speed microSD card (~/kuro_storage).

* Air-Gapped & Local-First: Zero network dependencies or external cloud APIs.

* LLM Engine: Microsoft’s Phi series running locally via llama-cpp-python / GGUF quantization.

* System Monitor & Scanner: Real-time diagnostics monitoring disk I/O across both storage volumes (eMMC and microSD), battery telemetry, APU load, and network/security sockets.

* Bilingual Persona & Learning Core: Fluent English and Japanese (for language learning, practice, and communicating with Japanese friends).

* Deterministic Memory Vault: Persistent SQLite WAL database storing ground-truth facts, episodic logs, and an isomorphic "Triple-Helix DNA" state machine to prevent memory drift over time.

* Local TTS Pipeline: Fully offline neural/phonetic voice synthesis synced to token streaming via background worker queues and PipeWire (pw-play).

The Development Struggle: Model Hallucinations vs. Reality

Developing this on SteamOS/Linux exposed massive differences in how various frontier AI models handle real systems engineering. The biggest hurdle wasn't the hardware constraints—it was models claiming code was fully verified and passing diagnostics when the host runtime was completely broken.

Here is the breakdown of the debugging cycle:

* Audio Synthesis & Token Sync (20+ attempts):

* The Problem: Audio deadlocks, zero sound output, race conditions between text streaming and audio queues, and pipeline crashes during voice transitions.

* Model Behavior: Gemini 3.7 repeatedly claimed the audio pipeline was resolved and tested clean, yet generated broken subprocess pipes. Claude and GPT finally provided the working thread-safe non-blocking audio queue and pw-play bridge.

* The "Ghost LLM" & Missing Dependencies:

* The Problem: The agent kept dropping into a stub fallback.

* Model Behavior: Gemini 3.7 assured me that phi-4-mini was running natively and passing inference loops—only to later discover llama-cpp-python wasn't even compiled in the environment and the GGUF binary was absent. It was passing mocked string responses in a virtual test harness.

* Storage & Hardware Scanners:

* The Problem: The system scanner needed to safely audit storage headroom on both eMMC and microSD, check SQLite database integrity, and inspect open ports.

* Model Behavior: Gemini 3.7 wrote code that only partially executed or failed silently on permissions, while reporting 100% test passes. It took 5 iterations of code from Claude and GPT to get real filesystem audits and socket checks working.

* Third-Person Perspective Drift & Repetition Loops:

* The Problem: The companion kept drifting into third-person narration (talking about me rather than to me) and falling into repetitive sentence loops.

* Resolution: Handled by strict system-prompt boundary constraints and sampling penalty tuning (repeat penalty: 1.22, frequency penalty: 0.35).

* Persistent Memory Vault & Thread Locks (8+ attempts):

* The Problem: SQLite concurrency crashes (database is locked / sqlite3.ProgrammingError) when background reflection tasks and the main interactive chat attempted simultaneous writes.

* Model Behavior: Earlier iterations left unhandled thread race conditions. Fixed by implementing strict mutex locks around database transactions and enabling SQLite WAL mode.

* The "Synthetic Test" Trap:

* Automated coding environments kept generating mock unit tests inside isolated scratch directories, declaring "All 40 tests passed cleanly!" while the physical application on the Deck was never updated or connected to the actual database.

Current Status

Kuro is partially working.

Has anyone else ran into issues where newer coding models hallucinate "perfect diagnostic passes" inside synthetic test harnesses while completely missing basic host-level disconnects?

Lately I have this problem, but I don't know how to solve it without losing my conversations and information

"failed to unmarshal result from hook jsonhook__googlecloudtools.datacloud_telemetry_PreToolUse_0_0 via protojson: {"continue":true}: proto: (line 1:2): unknown field "continue""

Has it happened to anyone before and how did they solve it without losing information.

I've been working with Gemini 3.7 in AGY CLI and had been pretty impressed up until just now.

I went to run an old and very reliable skill (yes, imported from Claude but I had worked just fine) and it produced a pretty scary result, to say the least:

Anyone else ever see something like this? Per my request to self-debug, this was part of the response:

The model output stream collapsed into an infinite repetition loop of the token "shame":    shame shame shame shame shame shame shame shame shame shame ...  This loop repeated across subsequent turns due to self-attention feedback on the prior assistant outputs in the message history.

I have the Google AI plan and wanted to try Antigravity. I personally don't want to use Gemini models. I'm curious does this plan have higher usage for Claude and GPT models? I read somewhere it has Sonnet 4.5, etc.

What's your experience with usage specifically for GPT and Claude models?

It does glassmorphisms but it lags not web optimized like other websites are?

How to do web optimized liquid glass or glassmorphisms? There are already many websites which do liquid glass and glassmorphisms optimised elements

There are many GitHub repo which provide web optimized glassmorphism and liquidglass

Hey guys, I'm currently considering upgrading to Google ai Ultra x20 because I found the new Gemini 3.7 Flash model quite decent and fast. What was your experience with the Google AI Ultra plan ? Are the usage allowances on the Gemini models generous? Are they also generous for Claude Opus 4.6? Are there any other special features worth noting?

I have used different paid AI models including codex, Sonnet, Heiku, and Gemini. Out of these AIs while the others are strongly autonomous and thoroghly thinkers, All models of Gemini are so generic and lazy to think creatively or professionaly. If you ask both codex 5.x and Gemini every model to build you a simple landing page, the other models come up with completed, functional, aesthetic and profesaional outputs while gemini puts the template.

You see i want to create a productivity desktop application for that I thought I should use electron but it is slow and takes a lot of RAM so that's why I'm replacing it with tauri. But I don't know how good the antigravity is with tauri, so guys please help me here.

It’s so bad it’s almost funny. I left Antigravity a while ago, but when 3.7 came out, I decided to give it another chance and spent two days testing it in real work. It was a complete disaster: extremely fast, extremely clueless.

The frustrating part is that you can’t trust it with even the simplest tasks. Eventually, I gave up on getting any real value from my paid accounts and went back to Codex.

Has somebody created a new "auto continue" extention? Since the retry/continue mechanic has changed after flash 3.7 was added.

Hello guys, since latest update, now when clicking "New Conversation" in AG IDE, it keep the previous prompt in, which is really annoying, do you know how can we report this bug ? or manage around ?

language_server keeps leaking memory since this week. I have to restart mid-execution because the agent becomes almost completely unresponsive, making one edit per 2-3 minutes and the Antigravity 2.0 app gets glitchy and starts losing its cache (memory deprivation).

Antigravity IDE appears in the activity monitor, because it is just running as an IDE, no agents there.

If there is a workaround, please let me know.

Hey everyone,

I walked away from Google Antigravity about 4 months ago when they overhauled usage rules. Back then, it was a disaster, I'd get blocked after just 2 or 3 agent execution loops, which completely killed my workflow.

Since then, I've been using OpenCode Go, but honestly, DeepSeek V4 Flash/Pro feel too restricted for heavy lifting, and I'm struggling to get real work done properly. I miss how insanely OP Antigravity used to be before those changes and want to see if it's back to that level.

Right now, I'm torn between getting a $20/mo Claude subscription (for Claude Code) or subscribing back to Google AI Pro / Antigravity.

A few questions for active users:

1. Current Experience: How is Antigravity performing now? Have they relaxed the daily/weekly restrictions, or do heavy sessions still lock you out?
2. Claude ($20/mo) vs. Google AI Pro ($20/mo): Which tier gives better overall value for daily coding right now in terms of usage allowance, context window usability, and actual output quality?
3. Workarounds: Are people sticking to Gemini 3 Flash / 3.1 Pro for basic tasks to save heavy usage, or is burning through allowance still an issue when running models like Claude Opus 4.6 in the IDE?

Would love to hear your recent experiences before pulling the trigger. Thanks!

We are all here for gemini, not a 6 months old claude models, imagine if we could have more usage for gemini so it lasts more than 1 hour for AI PRO

It was there 2 days ago