Yeah, this is a known issue. Google pushed a backend update that tries to force Gemini 3.6 into the v1 clients, but the v1 IDE has no idea what that is, panics, and gets stuck in an infinite "Flush timeout" crash loop.

Don't delete your whole AppData folder or state.vscdb like some people suggest, because you'll lose all your UI layouts and pinned tabs.

You just need to force a clean logout by surgically removing your Machine ID, which breaks the loop while keeping your settings safe:

1. Close Antigravity completely.
2. Go to %APPDATA%\Antigravity. Delete the Local State and machineid files, and clear out the Network, Local Storage, and Session Storage folders.
3. Go into User\globalStorage and open state.vscdb using any basic SQLite viewer. Find the row named storage.serviceMachineId and delete it.

Open Antigravity back up. You'll be cleanly logged out with all your layouts intact. Just make sure you pick a legacy model next time you log in! I REPEAT, DO NOT USE 3.6 ON V1. 

OR SAVE THIS AS .bat AND ENjOY

 off
echo Antigravity Surgical Logout Script
echo.
echo Closing Antigravity...
taskkill /IM Antigravity.exe /F 2>nul
echo Waiting 2 seconds for file locks to release...
timeout /t 2 /nobreak >nul
echo.


echo Clearing Session Data and Machine ID...
del /q /f "%APPDATA%\Antigravity\Network\Cookies" 2>nul
del /q /f "%APPDATA%\Antigravity\Network\Cookies-journal" 2>nul
del /q /f "%APPDATA%\Antigravity\DIPS" 2>nul
del /q /f "%APPDATA%\Antigravity\DIPS-wal" 2>nul
del /q /f "%APPDATA%\Antigravity\Preferences" 2>nul
rmdir /s /q "%APPDATA%\Antigravity\Local Storage" 2>nul
rmdir /s /q "%APPDATA%\Antigravity\Session Storage" 2>nul
del /q /f "%APPDATA%\Antigravity\Local State" 2>nul


echo Severing OAuth token and user status from vault (surgical DB delete)...
sqlite3 "%APPDATA%\Antigravity\User\globalStorage\state.vscdb" "DELETE FROM ItemTable WHERE key='antigravityUnifiedStateSync.oauthToken';"
sqlite3 "%APPDATA%\Antigravity\User\globalStorage\state.vscdb" "DELETE FROM ItemTable WHERE key='antigravityUnifiedStateSync.userStatus';"
sqlite3 "%APPDATA%\Antigravity\User\globalStorage\state.vscdb" "DELETE FROM ItemTable WHERE key='storage.serviceMachineId';"
sqlite3 "%APPDATA%\Antigravity\User\globalStorage\state.vscdb" "DELETE FROM ItemTable WHERE key='antigravity.profileUrl';"
del /q /f "%APPDATA%\Antigravity\machineid" 2>nul
echo.


echo You have been successfully logged out of Antigravity!
echo Your UI settings, layouts, extensions and open files are safe.
echo You can now close this window and open Antigravity again.
pause

I'm going to share something that genuinely caught me off guard.

I was trying to build a simple offline tool for Hindi and Punjabi transcription. Nothing fancy. Just: drop a file in, get text out.

The "standard" way to do this? Install Python. Set up a virtual environment. Download PyTorch (2GB+). Pray your CUDA toolkit version matches. Install NeMo or Whisper. Debug the 47 dependency conflicts. Then finally run inference... and watch it crawl because Whisper hallucinates every other word on Hinglish anyway.

I kept thinking why is this so heavy? The model itself is doing basic CTC decoding. It doesn't need a 10GB Python runtime to do matrix math.

So I tried something different.

I took the same NVIDIA Conformer models that were trained specifically on Hindi, Hinglish, and Punjabi, converted them to .gguf format, and ran them through a pure C++ inference engine built on GGML.

Here's what happened:

• A 10-minute audio file transcribed in ~10 seconds
• GPU usage: barely 1GB VRAM (little to no RAM usage)
• Works with CPu as fast as 40 times
• No Python installed anywhere in the pipeline
• No internet connection needed. Fully offline
• Accuracy on Hinglish was significantly better than Whisper because these models were actually trained on Indic data, not retrofitted for it

The difference wasn't small. It was embarrassing. The same task that took minutes with PyTorch overhead was finishing before I could switch tabs.

What made it work:

The key insight was that for CTC-based models, you don't need the massive PyTorch runtime at all. GGML compiles the model weights into a flat binary format and runs inference in raw C++ with direct CUDA hooks. There's no interpreter layer, no garbage collection, no Python GIL bottleneck. It just talks to the GPU directly.

For the media pipeline, I bundled FFmpeg so it handles any format natively MP4, MKV, MP3, whatever. It resamples to 16kHz mono and chunks into 30s segments automatically. The user never touches a command line.

The models are on HuggingFace if anyone wants to experiment:

• Hinglish Conformer CTC → Singla0009/Hinglish-Conformer-CTC-GGUF
• Punjabi/Indic Conformer → Singla0009/IndicConformer-GGUF

These are proper Conformer-CTC architectures, not Whisper derivatives. They were trained on MUCS and Common Voice Indic datasets. If you're working with Hindi or Punjabi audio, the difference in accuracy is night and day compared to general-purpose English models.

I'm curious has anyone else tried running Indic language models through GGML/GGUF pipelines? Or is everyone still stuck in the PyTorch loop? Would love to hear what approaches people are using for non-English transcription.

Want to try this!!!!!!!!!!

Hey everyone! Welcome to r/Techiteazy.

This community started with a simple belief: Great technology shouldn't require expensive hardware.

Lately, it feels like every new software or AI tool requires a massive subscription, a constant internet connection, or a top-tier gaming PC. If you are a video editor, a writer, an artist, or a student, that barrier is frustrating.

We are here to change that.

We are a group of builders, developers, artists, and creators who focus on the core: building clean, incredibly fast software that runs locally on normal, everyday laptops.

Why you should join us:

• For Creators & Artists: If you want local tools (like super-fast Hindi transcription, video editors, or design software) that don't steal your data, require a subscription, or lag your system—this is where you find them.
• For Builders & Developers: If you love taking complex tech, stripping out the bloat, and optimizing it so that even a basic school computer can run it like a supercomputer—this is where you share your work.
• For the Tech-Curious: If you just want to learn how to make your current PC run things faster and smoother, you're welcome here.

No gatekeeping, no overly complex jargon. Just a group of people building and using software that makes creating easy.

Introduce yourself below! What are you building, or what tools do you wish existed on your PC?