r/google_antigravity

I'm really looking forward to Gemini 4, PLEASE be good!

slower than top 30 models now mod will delete this post throwing excuse as this is low effort post

everyone is facing same issue and its not their network issue btw

also my other requests are getting done by 2 hours 1.5 hours 30 min each

https://preview.redd.it/7rq6j0t219uh1.png?width=576&format=png&auto=webp&s=53557fe7bd4d25aa84fd1ceed849daf7d9acacae

So what is the problem?

I just ran Google Antigravity from my phone !!!

No labtop

No Desktop

Only a phone

And Yes got it working using Termux.

#Ai #antigravity #SWE #SoftwareEngineer #Google

Use the new /plan command in Antigravity to:

• Keep actions safe (biases toward research & read-only tools)

• Map out complex tasks before touching code

• Generate a clear, structured artifact plan every time

Give it a spin and tell us what you build!

The best ai models (cost effective) are being removed and we will be left with argon (i think it would be highly priced)!!

Note: Posting this as an open-source reference pattern and diagnostic tool that you can build on, adapt to your own environment, or use as inspiration.

Hey everyone, here is an updated version of the skill I have been refining to audit Antigravity agent sessions. Ever since Gemini 3.8 Flash came out, I noticed token usage was draining significantly faster than before.

Watching the agent execute, I found that token drain primarily comes from three structural bottlenecks:

1. Command retry loops: A single failed command that takes 2 or 3 retries permanently injects thousands of tokens of stderr and broken payloads into every subsequent turn of that conversation.
2. Unbounded file reading: Agents repeatedly calling view_file on 500+ line files without line bounds, dumping entire source files into context instead of inspecting specific slices.
3. Context accumulation in long sessions: As conversations exceed 30 to 50 tool calls, model output and internal reasoning tokens scale rapidly because the entire tool history is re-sent on every step.

---

What the Skill Does

Instead of feeding megabytes of raw transcripts into the agent and wasting tokens, the skill runs zero-dependency local Python scripts under the hood in seconds.

Importantly, this tool does not blindly modify your configuration or overwrite files. It operates as an interactive diagnostic assistant: it first discovers what is happening, reports the exact bottlenecks to you, proposes categorized remediations, and waits for your confirmation before applying any changes.

Here is the updated skill definition header:

---
name: improve-agent-efficiency
description: Probes host development tools, mines causal command failure chains across past agent sessions, measures token burn and efficiency gains, and executes verified remediation across host binaries, global rules, and workspace AGENTS.md files.
---

You can trigger it directly from chat:

• For status & efficiency gains: "Analyze our efficiency gains and token burn since yesterday."
• For a full diagnostic audit: "Audit my past sessions and optimize agent rules."

---

How the Scripts & Workflow Work

The workflow is structured across distinct phases:

1. Interactive Scoping and Discovery (< 1 second)

For developers with dozens of repositories, you do not want to scan hundreds of sessions blindly. The script scripts/audit_trajectories.py includes a rapid inventory mode (--inventory-only) that discovers your active workspace, top repositories, and session date boundaries. If invoked bare in chat, the agent renders an interactive selection modal (ask_question) so you can select the exact repository scope and time window before deep parsing begins.

2. Out-of-Band Host Probing & Trajectory Mining

The skill bundles two standalone Python 3 scripts in scripts/:

• probe_environment.py: Inspects your local machine. It tests terminal output stream encoding for non-UTF8 crashes, detects installed CLI binaries (git, gh, rg, dotnet, node, cargo, python), and cross-references your active GEMINI.md to flag discrepancies.
• audit_trajectories.py: Parses local session transcripts out-of-band at zero LLM token cost. It measures true failure rates, calculates native token burn telemetry across prompts, model responses, thinking tokens, and tool output streams, and reconstructs multi-turn incident chains: [Initial Failed Command] -> [Intermediate Retries / Scratch Scripts] -> [Eventual Working Command]

3. Zero-Match Search Disambiguation

In command-line tools like rg, grep, findstr, and diff, exit code 1 denotes that zero matches or differences were found, rather than an execution crash. The auditor explicitly disambiguates clean zero-match returns from actual syntax or runtime crashes, preventing false alarms.

4. Read-Only Mode vs. Guided Remediation

Depending on what you ask for, the skill adapts its behavior:

• Mode A (Read-Only Gains & Token Burn Audit): Run with --compare (e.g. audit_trajectories.py <dir> --since <YYYY-MM-DD> --compare). It splits your session history into a baseline and target window, outputs a comparative table of failure rates, retry loops, and token burn per turn/session, and exits without making changes.
• Mode B (Guided Remediation Pipeline): If you ask to fix or optimize rules, the agent presents the diagnosed friction points to you, outlines proposed changes across three layers, and requests confirmation before applying them:
  • Host Layer: Package manager installs (winget, brew, apt) or persistent environment variables (PYTHONIOENCODING=utf-8).
  • Global Rules (~/.gemini/config/GEMINI.md): File-First execution SOP for multi-line scripts, bounded file reading conventions, and compact git commands.
  • Workspace Rules (<repo_root>/AGENTS.md): Language-specific runtime pinning, package manager lockfiles, and dev server hygiene.
• Verification Gate: If fixes are confirmed and applied, the agent executes live verification commands to confirm exit code 0 before concluding.

---

Multi-Platform & Zero Dependencies

The scripts rely exclusively on the Python 3 standard library (os, json, re, platform, datetime). They run on Linux, macOS, and Windows without requiring pip install, and support projects across TypeScript/Node, Python, Go, Rust, and .NET.

---

Links

• Repository: https://github.com/amirf147/my-antigravity-skills
• Skill Directory: https://github.com/amirf147/my-antigravity-skills/tree/main/skills/improve-agent-efficiency

For many of us, 3.7 has been the only model that consistently made real work viable on Antigravity, yet they want to remove it.

Based on my experience and the many posts across these forums, a lot of users seem to be reporting the same pattern:

Gemini 3.8 can spend 20-30 minutes reasoning through a simple task while consuming 10-20% of a 5-hour usage allowance.

Gemini 3.7 often completes similar tasks in just a few minutes while using only 3-5% of the same allowance.

This is a huge difference, especially for Pro subscribers.
I'm seeing similar reports throughout Reddit.

Are the people making these decisions aware of the amount of community feedback on this issue?

If you rely on 3.7, please consider politely sharing your experience with the Gemini team and people such as u/LoganKilpatrick1 and u/OfficialLoganK on X.

The more real user feedback they see, the better the chance they understand the impact of removing it.

StatusGator has also reported multiple Antigravity slowdowns over the last month. Many users feel that 3.7 remained far more usable during these periods.
Google Antigravity Outage History | StatusGator

If 3.7 works better for you than 3.8, please share your experience below so the feedback is collected in one place.

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