what if google is merging the world model + diffusion + omni stuff into Gemini 4 i feels like they're moving away from the pure LLM + CoT/coding path and toward something more native to reasoning about the world not yann leCun's v-JEPA exactly, but directionally similar vs. standard LLMs

What if google did something like other AI labs.. In the near future, could they introduce "Gemini 4 Ultra" competing with astra and fable.. or will it stay flash-lite against luna and sonnet, with pro as it is against astra and fable. I mean the naming would make more sense..

❤️ What if attaching a single red heart image made an AI start writing code recursively? Give a model an unrelated coding task plus one colored shape. Claude Opus 4.7 and GPT-5.4, which never used recursion without an image, solved the task recursively 100% of the time after seeing a red heart. That matches directly telling them to use recursion. Yet branching arrows, which seem like they should evoke recursion, barely moved anything. Stranger still, the models don't notice the influence. In Gemini's thinking traces, 71.5% of the cases where the symbol actually steered it dismissed the image as irrelevant and made up technical justifications. When the image was silently injected with no instruction to look at it, 100% of GPT-5.4's traces never mentioned it. In games where models communicate visually with each other, senders pick shapes that feel intuitive to humans, like spirals and arrows, but what actually steers receivers is hearts and triangles. Generation and interpretation are dissociated. And the more the symbols were refined, the worse human accuracy got. "A Heart Means Recursion: Exploring Visual Meaning and Communication in VLMs" It also suggests that unintended visual steering could become a blind spot for oversight in GUI agents that read screenshots. #VLM# #AIAgents#

# Useful but Little-Known Features of ADK 2.0 🌍 What if your agent could dynamically pick the best LLM for each request, balancing cost and quality on the fly? ADK 2.0's model routing uses the RoutedLlm class and LlmRouter function to dynamically select which model handles each request. 📌 Title: Model Routing 🔗 URL: 🧩 Overview The RoutedLlm class enables dynamic model selection from a pool of LLMs on a per-request basis. An LlmRouter function receives the map of available models, the current request, and an error context, then returns the key of the model to use. If a model fails before producing output, the router is called again with the error information, allowing automatic fallback to a different model. Note that you cannot switch models mid-stream once `connect()` has started. 🛠 How to use it Define an LlmRouter function and pass it to RoutedLlm as the agent's model. ```typescript import { BaseLlm, Gemini, LlmRequest, LlmAgent, RoutedLlm } from '@google/adk'; const myRouter = ( models: Readonly>, request: LlmRequest, errorContext?: { failedKeys: ReadonlySet; lastError: unknown }, ) => { if (!errorContext) { return 'primary'; // Try the powerful model first } if (errorContext.failedKeys.has('primary')) { return 'fallback'; // Fall back to the lightweight model on failure } return undefined; // No more options, propagate the error }; const agent = new LlmAgent({ name: 'smart_agent', model: new RoutedLlm({ models: { primary: new Gemini({ model: 'gemini-pro-latest' }), fallback: new Gemini({ model: 'gemini-flash-latest' }), }, router: myRouter, }), instruction: 'Process the task', }); ``` Unlike agent routing (switching between agents), model routing keeps the same agent logic and only changes the underlying model. 🏗 Building it into production ・Configure automatic fallback to a backup model when the primary model fails ・Route based on request complexity to optimize costs between powerful and lightweight models ・Implement A/B testing routers to compare model performance in production ・Log model selection decisions for cost analysis and performance optimization 💡 Use cases 🔄 Automatic failover when the primary model goes down 💰 Cost-optimized routing based on request complexity 🧪 A/B testing new vs. existing models for performance comparison 🌍 Model selection based on request language or region ⚠️ Watch out Model routing is experimental and available in ADK TypeScript v1.0.0 and later. You cannot switch models mid-stream after `connect()` has started. Overly complex router logic becomes hard to debug and maintain — keep your selection criteria simple. Also, verify that your fallback model supports the same capabilities as the primary model before relying on it in production. ✨ Model routing gives you dynamic control over availability, cost, and quality — making agent operations in production far more flexible and resilient. #ADK# #AIAgent#

# Practical and Useful Patterns with ADK 📄 What if you could define agents in YAML instead of code? ADK's Agent Config enables declarative agent definitions with environment-specific switching -- no redeployment needed for prompt or model changes! 📌 Title: Agent Config — Declarative, Code-Free Agent Definitions in YAML 🔗 URL: 🧩 Overview Agent Config lets you build ADK workflows without writing code, using YAML files to define `name`, `model`, `description`, `instruction`, `tools`, and `sub_agents`. Create projects with `adk create --type=config`, then run with `adk web`, `adk run`, or `adk api_server`. For programmatic loading, use `config_agent_utils.from_config()` in Python. This separation of agent definition from code enables prompt changes, model swaps, and environment-specific configurations without redeployment. 🛠 Usage A basic Agent Config YAML: ```yaml # root_agent.yaml name: assistant_agent model: gemini-flash-latest description: A helper agent that answers user questions. instruction: | You are an agent that answers various user questions. Provide accurate and helpful responses. tools: - google_search sub_agents: - config_path: specialist_agent.yaml ``` Create and run a project: ```bash # Create project adk create --type=config my_agent # Run options adk web # Web interface adk run # Terminal execution adk api_server # API server mode ``` Load programmatically in Python: Use `config_agent_utils.from_config()` from `google.adk.agents` to programmatically load an agent from a YAML file path (e.g., `"my_agent/root_agent.yaml"`). 🏗 Practical Patterns **Environment-Specific Configuration**: Maintain separate YAML files for dev/staging/prod and select them via environment variables. ```yaml # config/dev/root_agent.yaml name: assistant_agent model: gemini-flash-latest instruction: | [DEV] Include debug information in your responses. # config/prod/root_agent.yaml name: assistant_agent model: gemini-2.5-pro instruction: | Answer user questions accurately and concisely. ``` Read the environment name with `os.getenv("ENVIRONMENT", "dev")` and dynamically load the corresponding YAML file via `config_agent_utils.from_config(f"config/{env}/root_agent.yaml")`. **Prompt Versioning**: Track YAML files in Git for full prompt change history. Update instructions without code changes and roll back easily when needed. **A/B Testing**: Prepare multiple YAML files with different instructions or models, and switch between them at runtime to compare performance. Call `get_ab_variant(user_id)` to determine the A/B variant (`"a"` or `"b"`), then load the corresponding YAML file with `config_agent_utils.from_config(f"config/variant_{variant}.yaml")` for runtime A/B testing. 💡 Use Cases 🔄 Prompt and model changes without code modifications or redeployment 🌍 Per-environment configuration management (dev/staging/prod) 📊 A/B testing different instructions and models 📝 Git-tracked prompt versioning with easy rollback 🧩 Enabling non-engineers to update agent configurations safely ⚠️ Considerations - Currently only Gemini models are supported. Other model providers are not yet available. - Custom code tools are limited to Python and Java. - `LangGraphAgent` and `A2aAgent` are not yet supported in Agent Config. - API keys and project settings are managed via `.env` files -- be careful not to commit secrets. ✨ Agent Config separates agent definitions from code, enabling non-engineers to safely modify prompts and models while making environment switching and A/B testing straightforward. Use it to maximize operational flexibility! #ADK# #AIAgent#

# Useful but Little-Known Features of ADK 2.0 🌍 What if you could define AI agents with just a YAML file — no Python code required? ADK 2.0's Agent Config lets you declaratively define agents in YAML or JSON configuration files. Build, run, and modify agents without writing code. 📌 Title: Agent Config (Declarative Agent Definition) 🔗 URL: 🧩 Overview Agent Config lets you declaratively specify an agent's name, model, description, and instructions in YAML or JSON. You can define tools (built-in like google_search, or custom), and sub-agents (referencing other config files via config_path). It supports LlmAgent; the LangGraphAgent and A2aAgent types are not yet supported. Configured agents can be executed via `adk web`, `adk run`, `adk api_server`, or programmatically through `from_config()`. 🛠 How to use it Define your agent in a YAML file and run it with the ADK CLI. ```yaml # root_agent.yaml name: research_assistant model: gemini-2.0-flash description: An agent that assists with research instruction: | Use the search tool to investigate the latest information for the user's question and provide a clear answer. tools: - name: google_search sub_agents: - config_path: ./summarizer.yaml ``` ```bash # Run via CLI adk web # Interactive browser UI adk run # Terminal execution ``` For programmatic usage, generate an agent object with `from_config()`. 🏗 Building it into production ・Manage agent definitions as YAML files in Git for full change history tracking ・Maintain separate config files per environment (dev/staging/production) ・Use config_path in sub_agents to build reusable agent modules ・Deploy config-based agents as APIs using `adk api_server` 💡 Use cases 📝 Non-engineers can modify agent behavior by editing YAML alone 🔄 Version-controlled agent definitions with automated CI/CD deployment 🧩 Reuse common sub-agents across multiple projects 🚀 Consistent configuration management from prototype to production ⚠️ Watch out This feature is currently experimental. It only supports Gemini models — LangGraphAgent and A2aAgent are not yet supported. For complex custom logic or callbacks, the traditional Python code-based approach remains more appropriate. The specification may change in future versions, so check the latest documentation before production adoption. ✨ Agent Config dramatically lowers the barrier to defining and operating agents. It's especially effective for team collaboration and streamlined configuration management. #ADK# #AIAgent#

Was thinking about this over the weekend. What if LLMs were Italian food? Let me know what you think of the list (but remember you're arguing with an Italian). ChatGPT → Margherita Pizza The crowd-pleaser. Gemini → Lasagna Built with data layers (Search, YouTube, Android, etc.) Claude → Osso Buco Slow-cooked, meticulous, and expensive. Grok → Spicy 'Nduja Loud, punchy, sometimes TOO punchy. DeepSeek → Cacio e Pepe Simple ingredients, hard to make, shockingly good. Llama → Family-Style Antipasto Platter Open and shareable. Kimi → Tiramisu New on the menu and winning fans over fast.

# Useful but Little-Known Features of ADK 2.0 🌍 What if you could package reusable capabilities as modular "skills" and plug them into any agent? ADK 2.0's Skills feature lets you integrate modular, self-contained units following the spec. Skills are structured in three levels — metadata, instructions, and resources — and can be loaded inline or from the filesystem. 📌 Title: Skills for Agents 🔗 URL: 🧩 Overview Skills are modular, self-contained capability units based on the specification. They consist of three levels: L1 (metadata) defines name and description in SKILL.md frontmatter, L2 (instructions) provides concrete behavioral instructions in the body, and L3 (resources) includes references, assets, and scripts. Skills can be defined inline or loaded from the filesystem. The `SkillToolset` class wraps skills for agent consumption. 🛠 How to use it Define skills as SKILL.md files and load them into agents. ```python from adk import Agent from adk.skills import SkillToolset # Load skill from filesystem skill_toolset = SkillToolset( skill_path="./skills/data_analysis" ) agent = Agent( name="analyst", model="gemini-3.5-flash", tools=[skill_toolset], instruction="Use the data analysis skill to answer questions." ) ``` The SKILL.md frontmatter contains metadata like name, description, and version. The body provides specific instructions for the agent. L3 resources can reference external files and scripts. 🏗 Building it into production ・Manage skills as independent directories for easy versioning and testing ・Build a shared skill library for cross-team reuse of common capabilities like data retrieval and report generation ・Use L3 resources to bundle data and scripts that a skill needs ・Version skills in frontmatter to manage compatibility across deployments 💡 Use cases 📊 Share data analysis skills across multiple agents 📝 Package report generation templates and logic as reusable skills 🔌 Standardize external API integration know-how as skills for team-wide use 🧩 Compose complex workflows from combinations of modular skills ⚠️ Watch out The Skills feature is experimental. It's available in Python v1.25.0+, TypeScript v0.6.1+, and Go v1.2.0+, but the API and spec may change. Pin your versions for production use. The specification itself is still evolving, so check for updates periodically. ✨ Skills let you organize agent capabilities into reusable modules, dramatically improving development and maintenance efficiency. Start small and build your skill library incrementally. #ADK# #AIAgent#

"I don't think it's enough for us to get to 3,000 alone." That's what Jessica told me about our new invite. We built it so the people already coming to Social Media Marketing World can invite their Player 2. It's fun. People will use it. But Jon said the next part is the incentive. Something you can't buy. You only get it if you refer somebody. And that's where it got hard. Jessica: "The hardest part is tracking who referred who. What if somebody already bought a ticket that their friend referred?" I told the team whatever we do has to be simple. Jon had an answer: "We have our referral affiliate program. We can track it through the affiliate referral." But Chad Petersen's idea was to reward people with things, not money. Jon agreed: "We have a system in place that has the infrastructure to track everything, but we're using it for something else." My instinct says the tools are out there. So Jon's running deep research with Claude and Gemini to find a way to track it. If you've ever run a referral program, you know the reward is the easy part. How would you track who invited who? Start your comment with "I'd..." 👇 Press Start: The Road to 3,000. #SocialMediaExaminer# #SMMW27# #RoadTo3000# #EventMarketing# #ReferralMarketing#

AI found over 2,000 video clips from my conference to use in my ads. I rejected nearly all of them. Here's what happened. I'd just trained AI to make video ads from testimonials, and they came out beautiful. But I had this dream: what if it could go through the thousands of clips shot at the conference, find the right ones, and drop them in at the right moment? That's what good video editors do, right? So I asked ChatGPT, Gemini and Claude to find me a solution. I landed on one tool and downloaded it. That process was a nightmare. It was limited to 500 videos a month. I have thousands. Then Claude said, "I know the tech stack this is built on. Why don't I just build this for you?" I'm all, let's do it. This is where things got really, really, really messy. AI had to watch literally everything. Hours. I was canceling meetings. I got fried in the brain. So I said, there's got to be a better way. Scrub all these clips and tell me which ones you think are best. It came back with the shots it liked as little animated GIFs. It built a searchable index: type "writing" and up come people taking notes. Click any clip and it shows you the key moments inside it. Then I trained it on how I wanted the cuts done, and it rebuilt one of our testimonial ads with b-roll. While I'm trying to get to 3,000, this whole system was born less than 48 hours ago. Would you let AI pick your clips? Reply "Yes" if you'd try it, or tell me where it would fall apart for you. Press Start: The Road to 3,000. #SocialMediaExaminer# #SMMW27# #AIVideoEditing# #AIinMarketing#