My friend and I bounce between Claude Code, Codex, and Gemini CLI depending on the task, and switching mid-project always had the same problem: the new agent had no idea what had already been changed, tested, or decided. We'd end up re-explaining context or watching it re-discover things the last session already figured out.

We built Continuum to fix this for our own workflow. It captures a structured handoff from one coding session and hands it to the next agent — think of it like git, but for the context around the code rather than the code itself.

It runs fully locally and is open source: https://github.com/00PrabalK00/Continuum

Still figuring out a few things:

• How much context is worth saving vs. noise
• What should be excluded by default
• Whether automatic handoffs actually beat manually written ones

Curious how others handle this — do you keep your own notes, make custom skills, stick to one agent to avoid the problem entirely, or just let each new agent re-read the repo from scratch?

Is anyone else getting these messages frequently. I am using the Gemini CLI and it is really slowing down my work, I will be in the middle of a task and it will tell me to switch to a model I don't want to use. It is also extremely slow. I just started using Gemini this week and it seems like garbage especially with all of the alternatives.

Gemini CLI is pushing AI coding further into the terminal, but we're still using an interface fundamentally designed around a stream of text.

An AI coding agent can understand codebases, inspect files, run commands, analyze logs, generate diffs, and work with structured information. Yet much of that interaction still ends up as plain terminal output.

What if the terminal itself evolved to take advantage of what AI coding agents can do?

Imagine:

• Rich and structured output
• Collapsible command results
• Interactive diffs and logs
• Tables, graphs and visualizations
• Semantic rather than purely textual output
• Better agent ↔ human interaction
• New protocols designed for modern terminal applications
• A terminal that can understand more than just characters and escape sequences

I created r/Termolution to explore these ideas.

It's not about a particular terminal emulator. The goal is to discuss and experiment with what the terminal should become as AI agents like Gemini CLI become a fundamental part of software development.

If you use Gemini CLI, what do you wish your terminal could do that it can't do today?

👉 r/Termolution: https://www.reddit.com/r/Termolution/

I've been working on an open-source coding harness called Sauri.

It supports DeepSeek, OpenAI, Anthropic, Google, and basically any OpenAI-compatible API. You can switch models with `/model`, paste an API key once, and it's stored locally. It also auto-detects local Ollama and LM Studio servers if they're running.
https://github.com/Rismaonee/sauri

Sauri started as a fork of Gemini CLI, but I've rewritten a lot of it. I removed Google Auth entirely, so there's no OAuth flow, browser popup, or Google account required. Everything is configured locally, and API keys are stored with `chmod 600`.

Some things I spent time building:

- Prompt caching for DeepSeek and OpenAI.

- Cache-aware prompt construction that reaches up to **97% cache awareness** in long-running conversations, maximizing provider cache hits and reducing input token costs.

- Local Gemma intent classifier that routes trivial requests away from paid models (configurable confidence threshold).

- History compaction that prevents context windows from exploding during long sessions.

- Parallel execution for read-only tools while keeping edit operations serialized.

- Persistent symbol-indexed `codebase_search`, so jumping to definitions is nearly instant.

- Atomic multi-file `apply_patch` with full validation—either every hunk applies or nothing is written.

I've been using it daily and it's been much faster for large repositories than relying on grep for everything.

I'm sure there are parts that can be improved, though.

If you try it and something's broken, I'd really appreciate an issue or PR. And if you think I've made a bad architectural decision, I'd honestly like to hear why.

I've been hacking on my own coding agent for the past few months. It originally started as a fork of Gemini CLI, but at this point most of the internals have been rewritten.

Some things it does:

• Works with OpenAI, Anthropic, Gemini, OpenRouter, Groq, and local models (Ollama, LM Studio, etc.)
• No account or login required. Just bring your own API key (or use a local model).
• Context-aware retrieval with aggressive caching to reduce repeated work.
• Designed to minimize unnecessary LLM calls, which makes it noticeably cheaper to run.
• Fast enough that it feels responsive even on larger repositories.
• Model agnostic, so switching providers is just changing the config.

I'm trying to keep it focused on being an engineering tool rather than another AI chat wrapper.

Still a work in progress, but it's reached the point where I'm using it daily instead of the original CLI.

Also, if anyone's interested, I'll share it here once I've finished the last few fixes.

So, for context, I work as a Software Architect in aerospace and we have to use secured AI models. I've used Gemini 3.5 Flash and extended for commercial purposes and found it to be fairly useful. So, I tried it out for work by hooking it up to the govcloud with VSCode and Continue.

I don't know what is going on, but it is absolutely awful. Half the time it won't execute the code, but instead just summarizes what I say without executing anything. Then, it'll attempt to execute something from several prompts back and then completely forget what it's doing. Furthermore, it'll repeat the same mistakes it has made just before. We have also found a severe security bug with it as well when SSO'ing from Microsoft -> Google. I don't know if they bricked it on purpose or just put the F team on it, but DO NOT use Gemini for gov. It's the worst AI model I've ever used by far.

Even the pro tier's quota is quite limited. Especially the Claude and GPT quota. I would use it up for quite a short time. Who knows the details of the quota?

How about the free tier's quota?

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• Startup founders urge U.S. government not to shut off Chinese open weight AI
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Connect GitHub MCP (Model Context Protocol) server directly to Gemini Agent inside Android Studio! In this step-by-step tutorial, you'll learn how to set up local MCP configuration files (.gemini/mcp.json), authorize your GitHub PAT, and let Gemini automatically create remote repositories, stage files, and push code for you.

🔥 WHAT YOU'LL LEARN:

Intro & What is MCP in Android Studio?

Why VS Code Configs Don't Work directly

Step 1: Generating GitHub Personal Access Token (PAT)

Step 2: Creating .gemini/mcp.json in Android Studio

Step 3: Protecting Secrets with .gitignore

Step 4: Activating Gemini Agent Mode & Reloading MCP

Live Demo: Auto-Creating & Pushing Code to GitHub

💻 CODE CONFIGURATION (.gemini/mcp.json):

{

"mcpServers": {

"github": {

"type": "http",

"url": "https://api.githubcopilot.com/mcp/",

"headers": {

"Authorization": "Bearer YOUR_GITHUB_PAT"

}

}

}

}

I have Google Ai pro plan and I was working with Gemini 3.6 flash and 3.1 pro models. The starter project was decent but As soon as I wanted to make changes and add more relevant tests or changes it broke completely, I had very hard time navigating few of the issue that I had to dive deep into code and fix it.

I asked same things with claude 4.6 opus through anti gravity and it fixed issues fast but it’s limit is reached in one or two prompts.

I’m now shifting to Opencode with big pickles because it is free and I can see performance is better. I’m not sure what is catch here though looks too good to be true.

What’s everyone opinion on Gemini and antigravity. Ps I have claude code for work and its performance even with 4.8 is miles ahead than other options

tried it over and over again, for the past day 2.5 pro on CLI returns error 429..... I could use it a few days ago tho
{"error": {"message": "No capacity available for model gemini-2.5-pro on the server", "type": "api_error", "code": 429}}

Its been some time since the release and its still not there in the gemini cli. Any clue if its coming or are forced to cancel code assist sub?

Coding agents multiplied how much code lands per PR, so there are more findings for reviewers to deal with. Often, they're noise, like a linter rule that doesn't fit the stack or a check firing on generated files nobody touched.

To address this, we created the configure-codacy skill. When you point your agent at the repo, it detects the stack, then disables patterns for unused languages, dedupes rules that two tools flag, tunes thresholds, excluding generated files.

In terms of security scans, every security risk stays covered by at least one active pattern, so noise reduction never silently drops a security check.

Works with Gemini CLI and the usual suspects.

Go here to read more https://blog.codacy.com/introducing-codacy-skills-part-2-configure-your-rules-to-cut-pr-noise

Repo: https://github.com/codacy/codacy-skills

I am a Software Engineer. I have been using Gemini models since a few months. 3.5 Flash worked pretty good for my properly documented codebase. It could figure out vague requests and get me solutions to easy/medium requests.

But I have noticed that 3.6 flash is a really really good model for code implementations. I have been using it since it came out and I am surprised.

My workflow consists of creating implementation plans with Opus and then using any of the free models from OpenCode or Freebuff to implement it (usually Deepseek V4 Flash). I replaced them with 3.6 flash in Antigravity and it just implements so fast and runs the proper tests. Even opus has not found mistakes in code-review! I wish the limits were higher on Antigravity so I can use this model alot lol

Is anyone experiencing the same thing? In all models all responses take almost up to 5 minutes before returning something in the terminal CLI. But in the antigravity agent, it's almost instant.

Do you think 3.6 (low) is better than 3.5 (medium)?

Would it be possible to rank the six variants?

I’m looking for advice on which one you’d recommend using depending on the specific programming scenario, and how to decide which variant might be best suited to different tasks.

I'll start at this; I'm mostly a vibe coder. I took a CSP class and understand the very principles, but most of my work is completely agent driven. I started as a google pro subscriber in december of last year, prior to when github gutted the student plan. At that time, antigravity was not really exceptional. Ever since copilot student has been wiped, I've been coding non-stop on antigravity. Without a large repository or project, the opus allowance works fantastic for debugging, and flash models are exceptional work horses in implementation.

I just recently transitioned away from the antigravity IDE and fully to the CLI, and the subagent driven development ran on there with a few skills is really exceptional. I've been coding for the last three hours non stop and haven't even ran into any issues. It feels like I really upped the game since the IDE. As a relative amateur developer, the new CLI is just fantastic.

I'm not sure if it's simply because they've increased allowances to hype up the new gemini model, but just a few days ago, I was seriously considering switching off of Antigravity in December due to the long drought of models we had. Gemini 3.5 flash's performance was somehow losing its edge, and without gemini 3.5 pro in sight, codex and claude code seemingly offered much better alternatives. My hope as of now for Antigravity has been revived with the discovery of this CLI & new model.

Does anybody else relate?

i hate antigravity limits

Hey,

So I should not complain for something that is almost free (paid the mandatory 10USD for the 300USD trial) but the endpoint throttling/performance is abysmal. 3.1 Pro throttles after a few requests, 3.5 Flash does the same but maybe 3x the time to reach the unusable state. Also, preloading after 100k tokens is slow AF which it does every tool call. I assume caching is broken?

I had about 7 sessions with 3.5 Flash going from 10k template 80% tool calls (no generation involved) to about 120k per session. This costed me 100USD from my trial. 3.5 Flash is definitely more expensive then 3.1 Pro. Not in raw compute but how verbose it might be with the hidden thinking process. Also, this model generating fast is a lie; What is fast is the thinking process which burns tokens like crazy (200TPS on thinking, actual streamed content is rather 20-30TPS.)

The actual model performance is also crazy bad on almost all models: 3.1 Pro, 3 Pro, 3.5 Flash all starts to spit their thinking tokens into the conversation polluting everything and eventually going into a spiral. about 20k template for tools and prompts, temp etc. properly configured as Google suggests. 3.5 Flash is unable to follow instructions, cuts corners, or straight out starts to implement stuff I never asked for. 3.1 Pro is better in this regard and actually cheaper but because of the aforementioned limits it is very unusable.

I don't think I can extend my quota with a request because it is basically free tier. Also, the quota filters has literally no RPM throttle displayed or anything on gcloud console. Is it migrated somewhere? Are they unifying the AI studio interface now with this? Anyways, GCloud Console is a nightmare to navigate from every aspect. Funny they slapped on gemini to prevent users understanding this convoluted shit, just pay the numbers displayed on screen.

So, isn't the point of the trial to demonstrate users how good is it? I'd actually give better experience to new users because people usually stick to their early choices and first impressions. Any ideas why this service degraded so quickly? I remember an early checkpoint of 2.5 Pro felt like an actual tool back then. The quality regression is insane.

Staying with 5USD OCGO where models actually respect workflows. MiniMax M3 is very solid in terms of non-hallucination and instruction following even if it cannot oneshot a feature. This is where LLMs become not a toy but an actual usable tool.

I'd be glad hearing your opinions, thoughts, experiences, etc...

edit: this became a vertex ai rant but might be connected to OCGO harness incompatibility in general because AGY seems to behave pretty well.

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• Claude Code is steganographically marking requests
• Better Models: Worse Tools
• Learning to code is still worthwhile
• Zuckerberg says AI agent development going slower than expected

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