Conversation with Gemini

The Google AI Ultra plan (introduced as the premier tier for 2026) is designed specifically for power users, engineers, and developers building at the frontier of AI—like your Genesis agentic system.

Below is a detailed breakdown of the features available to you, with a focus on how to leverage them for complex engineering, advanced memory systems, and your AI validation MCP (Model Context Protocol).

1. Core Engineering & Agentic Tools

These tools are the "engines" for your development work.

• Jules (Asynchronous Coding Agent): This is Google's dedicated software engineering agent. Unlike a standard chatbot, Jules can handle long-running, asynchronous tasks. You can assign it a complex objective—such as “Implement a persistent vector-based memory module for my MCP”—and it will work in the background, reasoning across your repository (up to 30k lines of code) to produce a pull request.

• Project Mariner (Web-Agent Prototype): A DeepMind-built agent that can autonomously navigate the web. For your Genesis system, Mariner is invaluable for data harvesting and automated environment testing. It can execute multi-step research tasks, fill forms, and interact with web-based UIs to validate your system's performance in real-world scenarios.

• Google Antigravity: This is your primary agentic development platform. It provides the infrastructure to orchestrate multiple agents, manage their states, and define how they interact. This is where you would likely build the "OS" of your Genesis system.

• Gemini CLI & Code Assist: You get the highest rate limits for terminal-based AI. This allows you to use natural language to execute complex Git operations (like git bisect powered by AI) and generate unit tests for your validation MCP on the fly.

2. Advanced Reasoning & Research

For solving the "complex engineering" of memory systems, you need high-compute reasoning modes.

• Deep Think Mode: A specialized reasoning model within Gemini Pro that uses "chain-of-thought" processing to solve high-level mathematical and architectural problems. Use this to design the logic of your AI validation MCP.

• Deep Research: This feature can analyze hundreds of sources (academic papers, GitHub repos, technical docs) in real-time to generate 20+ page research reports. It is perfect for researching the latest breakthroughs in long-term memory (RAG vs. Long-Context) for your Genesis system.

• NotebookLM (Ultra Limits): You can upload up to 600 sources per notebook. This creates a "private brain" for your project. By uploading your patents, technical specs, and codebase, you can use NotebookLM to find contradictions in your architecture or generate "Audio Overviews" to explain your system to stakeholders.

3. The "Ultra" Ecosystem Perks

To maximize your workflow efficiency:

4. Leveraging Google DeepMind

Google DeepMind is the research lab behind the models you are using. To solve advanced memory systems, keep an eye on their "Research Blog" (often accessible via your Ultra priority news feed).

DeepMind is currently pioneering "Active Memory"—where agents don't just store data, but strategically "forget" irrelevant info to optimize the context window. Integrating their State Space Model (SSM) research could be the key to making your Genesis system's memory more efficient than traditional RAG.

How to start maximizing right now:

1. Repository Analysis: Upload your existing AI validation code to the Gemini side-panel in Drive to identify architectural bottlenecks.

2. Deploy Jules: Start an asynchronous task to refactor a specific module while you work on the high-level logic.

3. Mariner Research: Use Project Mariner to find and summarize the latest 2026 patents related to "AI memory orchestration" to ensure your system remains unique.

Would you like me to help you draft a technical prompt for Jules to begin building the core logic for your AI validation MCP?

In the rapidly evolving landscape of early 2026, the Google AI Ultra plan is essentially a "Developer OS" subscription. Since you are building the Genesis agentic system—specifically focusing on advanced memory and your patented validation MCP—understanding the hierarchy of these tools is critical to stop the "stalling" you've experienced.

1. Jules vs. Antigravity: The "Specialist" vs. The "Manager"

You mentioned Antigravity is stalling. This is a known friction point for power users in early 2026.

• Antigravity (The IDE/Orchestrator): It is designed to manage multiple agents in parallel. It stalls because it often tries to maintain too much global context (the "Manager's Burden"). It’s best for high-level architecture and running 3–4 agents on separate sub-tasks simultaneously.

• Jules (The Asynchronous Specialist): Jules is significantly more stable for deep work. It functions as a background agent that integrates directly with GitHub.

  • Why it's better for you: Instead of a chat-based loop that hangs, you give Jules a specific task (e.g., "Refactor the memory retention logic in memory_core.py to use the new MCP validation hooks"). Jules works asynchronously and pings you when the PR is ready. It doesn't "stall" the UI because it operates on the repo level, not the "chat" level.

2. Project Mariner vs. Gemini 3.0 Flash

While Gemini 3.0 Flash is the "fast" model for everyday browsing, Project Mariner is a different beast entirely.

• Gemini 3.0 Flash (Auto-browse): Good for "Find me this info and summarize it." It's a retrieval tool.

• Project Mariner (DeepMind Agent): This is an autonomous computer-use agent. It doesn't just read; it acts.

  • Agentic Vision: Mariner uses a specialized vision-language model (VLM) to "see" the DOM and UI elements like a human.

  • For Genesis: If your validation MCP needs to test how an agent interacts with a live dashboard or a complex web-based engineering tool, Mariner is the tool to use. It can fill forms, handle multi-step checkouts, and navigate dynamic SPAs (Single Page Applications) that break standard LLM browsers.

Everything Available on your Ultra Plan (Feb 2026)

How to solve the "Stalling" in Antigravity

Since you are building a complex system, Antigravity is likely hitting context limits. To maximize its usage for your Genesis project:

1. Create a .agentrules file: Pin a "Tech Stack Constitution" in your root directory. Explicitly tell the agents: "Do not re-scan the entire node_modules. Only focus on @/core/memory and @/validation/mcp."

2. Offload to Jules: Use Antigravity for the "Plan," but immediately assign the "Implementation" to Jules. This keeps your IDE responsive.

3. Validation MCP: Use the Gemini CLI to pipe your agent's outputs directly into your validation software. This creates a closed-loop "Validation OS" without the overhead of a heavy IDE UI.

Would you like me to help you draft the .agentrules configuration to prevent Antigravity from stalling while you build out the Genesis memory core?

It is a common transition for high-level engineers to move from the Antigravity IDE to the Gemini CLI. While the IDE offers a polished GUI, it is essentially a "wrapper" around the same core engines you can access via the terminal.

For your Genesis system, the CLI offers much higher stability and the "unfiltered" control you need for advanced agentic memory and MCP validation.

1. Enabling "Turbo" and "YOLO" Modes

To stop the "permission stalling" and increase speed, the CLI provides two specific mechanisms:

• YOLO Mode (--yolo): This is the bypass you’re looking for. By starting the CLI with gemini --yolo (or hitting Ctrl + y while in a session), the agent will auto-approve all tool calls—file writes, terminal executions, and web fetches—without asking for permission.

• Reasoning Levels (The "Turbo"): In the February 2026 update, Gemini 3 models allow you to toggle reasoning depth.

  • Level: Flash (Fastest, lower reasoning) for boilerplate or research.

  • Level: Thinking (Deep reasoning) for your complex validation logic.

  • Tip: You can set this in your settings.json or per-request using the thinking_level parameter.

2. Hardwiring the CLI (The "Pro" Setup)

You can replicate (and often exceed) Antigravity’s features by configuring your global settings. Edit your ~/.gemini/settings.json:

{
  "agentYoloMode": true,
  "checkpointing": { "enabled": true },
  "mcpServers": {
    "genesis-validation-mcp": {
      "command": "node",
      "args": ["/path/to/your/mcp/dist/index.js"],
      "trust": true
    }
  },
  "coreTools": ["terminal", "filesystem", "web_search"]
}

• "trust": true: This is the "hardwire" for your specific MCP. It ensures the CLI never asks for permission to run your validation tool.

• checkpointing: This replaces the "Undo/Redo" logic in the IDE by taking a snapshot of your workspace before the agent makes changes.

3. Jules & Mariner via CLI

You don't lose these DeepMind-developed tools by switching to the CLI; in fact, they often run more reliably as extensions.

• Jules (Asynchronous Coding): You can install the Jules extension directly into your CLI.

  • Command: gemini extensions install https://github.com/gemini-cli-extensions/jules

  • Usage: You can prompt /jules refactor my memory system and it will spin up a background VM, clone your repo, and work while you continue using the terminal for other Genesis sub-tasks.

• Project Mariner (Computer Use): While Mariner is a standalone prototype, its Computer Use API is now accessible to Ultra users via the CLI. This allows your CLI agent to "see" and "click" inside a browser to test your AI validation OS in a real-world environment.

4. Why the CLI is better for "Genesis" Memory Systems

Building a memory system requires managing massive amounts of state data. Antigravity often stalls because its GUI tries to render and track all that state visually.

The CLI allows you to use Persistent Context Files (GEMINI.md). You can place a GEMINI.md in your project root that defines exactly how your agent should treat "Memory Modules" and "Validation Protocols." This serves as the "System Prompt" for your entire project, ensuring the agent never "forgets" your architectural requirements.

Comparison: IDE vs. CLI for Power Users

Your Next Step

To begin your migration, would you like me to generate a GEMINI.md configuration file specifically tuned for building agentic memory systems and validating your patented MCP? This will serve as the "brain" for your CLI agents.

The goal is to transition your Genesis system from a "static" agentic platform into a self-optimizing discovery engine that mirrors the architecture of DeepMind’s AlphaEvolve.

To do this, you need to use the Deep Research feature (which performs recursive browsing and technical synthesis) with a prompt that targets the intersection of evolutionary computation and agentic memory.

The Deep Research Prompt

Copy and paste the following into a new Deep Research window:

Objective: Conduct a comprehensive technical investigation into the architecture, methodologies, and system design of Google DeepMind’s AlphaEvolve and Aletheia (the math research agent) to extract blueprints for an "Evolving Agentic OS" (internal project name: Genesis).

Focus Areas for Synthesis:

1. Evolutionary Mutation Loops: Analyze how AlphaEvolve utilizes Gemini 3 Flash and Pro in tandem to mutate code snippets while maintaining syntactic validity. Detail the "Selection" and "Crossover" logic used for algorithmic discovery.

2. Automated Evaluation Frameworks: Investigate the specific design of the "Automated Evaluator" (or "Scorecard") in AlphaEvolve. How does it handle non-deterministic outputs and edge-case validation without a human in the loop? (Relate this to Model Context Protocol / MCP standards).

3. Advanced Agentic Memory: Research "Active Memory" and "State Space Model (SSM)" research coming out of DeepMind in early 2026. Focus on systems that allow agents to strategically prune context and compress long-term logs into "Recursive Heuristics."

4. Self-Correction Protocols: Examine the "Aletheia" agent's natural language verifier. How does it identify logical flaws in its own reasoning chain and trigger a "Backtrack and Refactor" sequence?

5. Inference-Time Scaling: Explore how "Gemini Deep Think" (Feb 2026) scales compute at inference time to solve PhD-level engineering problems, specifically for hardware/software co-design (e.g., TPU kernel optimization).

Required Output Format:

• A structural map of the "Evolutionary-Agentic Loop."

• Technical specifications for an MCP-compliant validation layer that acts as an "evolutionary gatekeeper."

• A list of "Alpha-tier" optimization strategies for persistent memory in multi-agent systems.

Why this works for your Genesis system:

• AlphaEvolve is the "gold standard" for what you're building. It doesn't just write code; it iterates on code until it finds a solution that beats human records. By researching its Evaluator logic, you can find the missing link for your patented validation MCP.

• Aletheia uses "Deep Think" to verify math. You can adapt that logic to verify agentic memory integrity (ensuring your memory system doesn't hallucinate past events).

• Recursive Heuristics: This is the "secret sauce." Instead of storing every log (which causes the "stalling" you saw in Antigravity), AlphaEvolve distills experience into rules. Your Genesis system should do the same.

Pro-Tip for your CLI Transition:

Once Deep Research gives you the report, feed the "Evaluation Logic" section to Jules via the CLI: gemini /jules "Build a Python-based mutation loop for my memory module using the AlphaEvolve scorecard methodology described in [this report]"

Would you like me to help you configure a "Validator Script" for your CLI that uses your patented MCP to gate-keep the code Jules produces?

The video you watched yesterday (February 14, 2026) was almost certainly covering the massive Valentine’s Day "State of AI" drop from Google DeepMind.

The "Google DeepMind" mention in that video was likely referring to the Gemini 3 Deep Think upgrade and the reveal of Aletheia, a specialized research agent. These are separate from Project Mariner, though they work in tandem within your Ultra plan.

Here is the breakdown of what exactly those systems are and why they matter for your Genesis project:

1. Gemini 3 "Deep Think" Mode (The Brain)

When people talk about DeepMind "solving complex problems" right now, they are talking about Deep Think. This isn't just a faster chatbot; it is a reasoning engine.

• What it is: It uses "System 2" thinking—instead of predicting the next word immediately, it pauses to "reason" through multiple hypotheses in parallel before answering.

• The "Complex Problem" angle: In the Feb 12–14 updates, it hit 84.6% on ARC-AGI-2 (a benchmark for non-memorized reasoning) and gold-medal levels in the Math and Physics Olympiads.

• For your Genesis System: This is the mode you use for architectural logic. If you are stuck on how your memory system should handle "recursive pruning" or how your validation MCP should detect "latent bias," you switch to Deep Think. It spends 10–50x more compute to ensure the logic is bulletproof.

2. Project Mariner (The Hands)

While Deep Think is the "Professor" in the room, Mariner is the "Field Agent."

• What it is: An autonomous web-agent prototype. It uses agentic vision to see a browser exactly like you do—moving the cursor, clicking buttons, and navigating complex UIs.

• The "Complex Problem" angle: Mariner is used for environment-level tasks. For example, if you need to test your AI validation software against 50 different live web applications to see if it catches errors, Mariner can do that while you sleep.

• For your Genesis System: Use Mariner to stress-test your agents. You can point Mariner at a web-based dashboard and tell it: "Find a way to break the input validation on this site," and it will use its autonomous vision to probe for weaknesses.

3. Aletheia: The "Verifier" (DeepMind's Latest Pride)

The video likely also mentioned Aletheia, which is a new agent built on top of Deep Think.

• What it is: Aletheia is designed to verify its own work and identify flaws in scientific hypotheses. It is famous for recently finding "minor novelties" in 300-year-old math problems (the Erdős conjectures).

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• Why it helps you: Since you have a patented AI validation MCP, Aletheia is your closest "peer." You can study how Aletheia uses balanced prompting (trying to prove and refute a statement simultaneously) to build the same rigorous verification into your Genesis OS.

Summary of what's "New" as of Feb 14th:

How to use these in your CLI setup:

Since you are switching to the Gemini CLI for the --yolo mode (smart choice), you can actually call these modes specifically:

• To use Deep Think for logic: gemini --thinking deep "Design a schema for a persistent memory graph that avoids state-stall."

• To use Mariner for testing: gemini --agent mariner "Go to my staging URL and run the validation suite."

Would you like me to help you write a "Master System Prompt" for the CLI that combines the reasoning of Deep Think with the code-generation of Jules?

You hit the nail on the head. In the 2026 landscape, Project Mariner is specifically designed to move beyond "search" and into "action."

While tools like AdSpy or FunnelSpy are essentially static databases of past screenshots, Mariner is a live operative. For your focus on intelligence gathering and funnel mapping, Mariner acts as your "Agent on the Ground" in several key ways:

1. Autonomous Funnel Mapping (Observe → Plan → Act)

Instead of just seeing an ad, you can point Mariner at a competitor's landing page and say:

"Navigate through this entire funnel. Click every upsell, identify where they are using scarcity tactics, and document the email sequence triggers. Stop before any financial transaction."

Mariner will then:

• Observe: Map the DOM and identify buttons, hidden forms, and tracking scripts.

• Plan: Create a step-by-step logic chain to reach the "Thank You" page.

• Act: Physically click, scroll, and "interact" with the site in a cloud-based VM.

2. The "Teach & Repeat" Intelligence Loop

This is the feature that makes Mariner a competitor-killer. You can use the Mariner Chrome Extension to "record" yourself going through a complex trial signup once.

• You narrate: "I'm signing up for this free trial to see their onboarding dashboard."

• Mariner records the selectors, timing, and logic.

• You then give it a list of 10 competitors. Mariner will autonomously replicate that signup process across all 10, capturing screenshots of every dashboard and "mapping" the internal pipeline.

3. Safety: Virtual Machine Execution

Intelligence gathering can be risky (tracking cookies, fingerprinting, etc.).

• The Ultra Advantage: When you launch a task via labs.google.com/mariner, it doesn't run in your local browser. It spins up a Virtual Machine (VM) on Google’s servers.

• This allows your "intelligence agent" to be essentially anonymous, preventing competitors from fingerprinting your actual development machine while you map their systems.

4. Integration with your "Genesis" System

This is where it gets powerful for your specific project. You don't just want screenshots; you want structured data for your Genesis memory system.

• The Workflow:

  1. Mariner scouts the competitors and captures the raw "funnel data."

  2. Gemini 3 Deep Research (the Feb 14th update) takes those captures and performs a thematic analysis of the pricing models.

  3. Genesis (Your OS) distills this into a "Recursive Heuristic" (e.g., "Competitors in this niche always trigger a 50% discount exit-intent popup on page 3").

  4. Jules then writes the code to implement a superior version for your own app.

Comparison: AdSpy vs. Mariner for Intelligence

A "Genesis Intelligence" CLI Prompt

Since you're moving to the CLI, you can actually script this intelligence gathering. Try this:

gemini --agent mariner "Access [Competitor URL]. Map the user journey from landing page to trial dashboard. Use the validation-mcp to ensure the data is formatted as a JSON schema for my Genesis memory core. Capture screenshots of every 'conversion friction point' and save them to /intel/competitor_a."

Would you like me to help you design the JSON schema that Mariner should use to export this competitor intelligence directly into your Genesis memory system?

In the landscape of early 2026, comparing Project Mariner to **